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Article

Low Concentrations of Antibiotics Alter Microbial Communities and Induce High Abundances of Antibiotic-Resistant Genes in Ornamental Water

1
Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control (AEMPC), Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CIC-AEET), Nanjing University of Information Science & Technology, Nanjing 210044, China
2
NUIST Reading Academy, Nanjing University of Information Science & Technology, Nanjing 210044, China
3
Changwang School of Honors, Nanjing University of Information Science & Technology, Nanjing 210044, China
*
Authors to whom correspondence should be addressed.
Water 2023, 15(17), 3047; https://doi.org/10.3390/w15173047
Submission received: 5 August 2023 / Revised: 22 August 2023 / Accepted: 24 August 2023 / Published: 25 August 2023
(This article belongs to the Special Issue Emerging Contaminants in Water Environments)

Abstract

:
Antibiotic resistance poses a significant threat to the public health domain. A favorable platform for generating and disseminating antibiotic-resistant genes (ARGs) and antibiotic-resistant bacteria (ARB) is provided by landscaped fish ponds created by urbanization. This research delved into the effects exerted by different concentrations of specific antibiotics, namely tetracycline and ciprofloxacin, on the microbial community composition present in water samples obtained from a landscape pond. Additionally, we analyzed the abundance of ARGs and the class 1 integron-integrase gene (intI1), and identified potential hosts of ARGs. The results indicated that the consistent administration of antibiotics significantly influenced the microbial community structure, resulting in variations within both bacterial communities and functionalities. Furthermore, the absolute quantities of ARGs, including tetA, tetC, qnrA, and qnrS, as well as the integrase gene intI1, exhibited augmentation in response to varying types and concentrations of antibiotics. Notably, the regular input of low concentrations of antibiotics produced higher levels of abundance of ARGs than the regular input of higher concentrations of antibiotics. The use of different types of antibiotics led to diverse host bacteria structures.

Graphical Abstract

1. Introduction

Antibiotics are defined as a class of bioactive substances, including naturally occurring or synthetic compounds, that possess antimicrobial properties and are designed to target microorganisms [1]. Since the use of penicillin in the 1940s, a wide range of antibiotics have been widely used in many industries, including human medicine and animal breeding, making major contributions to the treatment of human disease and food supply [2]. Nevertheless, antibiotics administered to human or animal organisms encounter challenges in achieving complete absorption or degradation [3]. They are more commonly found as active chemicals in human excreta, such as feces and urine, entering the environment through wastewater and feces [4]. Many antibiotic-resistant bacteria (ARB) and antibiotic-resistant genes (ARGs) are produced under the stimulation of antibiotics, which results in the emergence of antimicrobial resistance (AMR) [5]. In 2015, the World Health Organization stated that the huge public health crisis caused by AMR needed to be addressed through a worldwide effort [6]. According to their forecast, a great threat of death caused by AMR will be faced by 10 million worldwide by 2050 if no measure is adopted to stop its spread [7]. Notably, the tetA, tetC, qnrA, and qnrS genes are frequently detected in a variety of environmental media. tetA and tetC are tetracycline-resistant genes and provide a code for efflux pumps that reduce the bactericidal effect of tetracycline (Tet). qnrA and qnrS are ciprofloxacin (Cip)-resistant genes. The proteins encoded by qnrA and qnrS interfere with target enzymes, reducing sensitivity to quinolone antibiotics, and thereby provide a survival advantage for bacteria under antibiotic pressure [8].
Pruden et al. found that ARGs combine the biological characteristics of being “self-replicating and dispersing” with the physical and chemical characteristics of being “non-perishable and environmentally persistent” [9]. The main mechanisms of transmission can be divided into horizontal gene transfer (HGT) and vertical gene transfer (VGT) [10,11]. Thus, mutations in bacteriophage genes will inevitably be induced in the environment with low concentrations of antibiotics [12]. Meanwhile, mobile genetic elements such as the class 1 integron-integrase gene (intI1) are commonly linked to genes conferring resistance to antibiotics. Mobile genetic elements with ARGs can frequently laterally transfer into a wide range of bacteria [13]. Eventually, this will lead to the accumulation of ARGs in an environment and the emergence of multidrug-resistant bacteria.
It was widely acknowledged that China was the foremost global producer and exporter of aquaculture products [14]. In intensive or semi-intensive farming, antibiotics are frequently used to protect fish from infectious diseases [15]. In this context, aquaculture has become a major consumer of antibiotics [16] and freshwater aquaculture areas have also been turned into reservoirs of antibiotics and ARGs [17]. The significant environmental and human threats posed by antibiotics and ARGs attracted increasing concern in China, and the use of certain antibiotics is gradually being banned [18]. However, urban landscape fish ponds, as small bodies of water, have been paid relatively little attention in terms of microbial community resistance compared to traditionally farmed waters. Urban landscape fishponds have been realized as an independent part of the urban ecosystem [19], with the advantages of resilience via peak flow reduction, the ability to improve cities’ ecological diversity, and the ability to increase natural urban water storage, and are thus considered to be significant to the construction of “Sponge Cities” [20]. However, with the development of urbanization, higher requirements are starting to be established for the ecological and environmental construction of urban landscape ponds [21]. Thus, in order to ensure the vitality of aquatic animals in the pond and provide a more perfect visual landscape for the public, a certain level of antibiotics has been injected into ponds to achieve a more ideal breeding effect, following the aquaculture model [22]. However, previous studies reported that antibiotic drugs were administered to fish at concentrations ranging from 30 to 200 mg/kg to during aquaculture [23]. Imitating similar aquaculture patterns, the use of high concentrations of antibiotics ensured that the growth of animals in ponds was good but caused a great deal of antibiotic contamination [24]. Additionally, it remains unknown whether the structure of the microbial community and the abundance of ARGs and intI1 are affected by the external inputs of low concentrations of antibiotics in water.
The primary objective of this investigation is to examine the impact of exogenous antibiotics on the microbial community, ARB, as well as the abundance of ARGs and intI1 within the urban landscape pond water system, focusing on the following aspects: (1) whether low and high concentrations of exogenous antibiotics can differentially alter the composition of microbial communities in water samples and explore the functional differences in microbial communities in water samples; (2) whether exogenous low concentrations of antibiotics have the advantage of increasing the abundance of ARGs and intI1 in water samples; (3) an investigation of the interrelationships among the microbial community, intI1, and ARGs, while also identifying potential host bacteria responsible for carrying ARGs. Also, this study introduces significant innovations that contribute to our understanding of antibiotic resistance and microbial ecosystems. Our conclusions were as follows: (1) we found correlations amongst the microbial community, intI1, and ARGs; (2) diminished concentrations of antibiotics amplify the overall prevalence of ARGs and intI1 to a greater degree; (3) temporal factors significantly contribute to alterations in microbial compositions and functionalities. Two experimental models consisting of tetracycline, which is widely used due to its high production volume, low price, and excellent efficacy [25], and ciprofloxacin, which can obtain highly germicidal to gram-negative bacteria, the main bacterial pathogens in aquaculture [26], were established. Bacterial communities in water samples were characterized by 16S rRNA gene amplicon sequencing. Quantitative real-time PCR (qPCR) was employed to unveil the absolute abundance of ARGs and intI1. These findings will help to increase our understanding of the impact that exogenous antibiotics have on reshaping the structure of ARB within pond water and augmenting the prevalence of ARGs and the integrase gene intI1.

2. Materials and Methods

2.1. Sampling Sites and Sample Collection

Sampling was carried out in an urban pond in Nanjing, China. The urban pond covers an area of approximately 0.02 km2. This pond plays a significant role in surface runoff and environmental improvement. A small urban river runs through the pond, surrounded by large areas of trees and lawns. This urban river is narrow and meandering, with a length of approximately 200 m, and its main functions are drainage and landscaping. Koi fish, goldfish, Chinese carp, etc., are raised in this urban pond. The fish in the lake are not fed with feed daily. Because the density of fish in this pond is low and the water quality is good, antibacterial drugs are not applied at ordinary times.
Surface water was collected from this urban pond (118°720025′ E, 32°210425′ N, GPS). Water samples were collected by retrieving 12 L of water from a depth ranging from approximately 15 cm to 20 cm below the water surface, near the central region of the lake. Subsequently, the collected water was transferred into a high-density polyethylene (HDPE) container for further preservation. The HDPE bucket was sterilized before sample collection. Once the collection was complete, the HDPE bucket was transported to the laboratory within 1 h and kept away from the light for further treatment and analysis. The formal experiment was carried out within 24 h. The fundamental physicochemical characteristics of the water samples are documented in the Supplementary Information section.

2.2. ARGs Mutation Experiments

2.2.1. Determining Inhibitory Concentration

Bacterial ten percent inhibitory concentration (IC10) and half inhibitory concentration (IC50) for antibiotics were determined according to previous methods [27]. Specifically, the Escherichia coli K12 (E. coli K12) were cultured and diluted to about 105 CFU/mL. To each well of the 96-well plates, a precise volume of 5 μL of the bacterial culture, 15 μL of antibiotics at varying concentrations, and 130 μL of fresh Luria–Bertani (LB) media was dispensed. Sterilized phosphate buffered saline (PBS) was used as a blank control because the stock solution of antibiotics was prepared in sterile PBS. The plates were incubated at 37 °C for 24 h before liquid samples were measured at the optical density at 595 nm (OD595) using a visible-light spectrophotometer (Multiskan FC, Guangzhou, China). The growth rate of E. coli K12 without drug exposure was set to 100%. IC10 and IC50 were determined as the concentration of antibiotics, which inhibited 10% and 50% of the growth. Each test sample was tested at least in triplicate. The inhibitory rate was calculated based on the OD595 value and the published method [28]. Detailed inhibition rates of antibiotics are presented in the Supplementary Materials. Then, the full dose–response curve for E. coli K12 was established. Finally, IC10 and IC50 were calculated by curve-fitting and regression analysis. The IC10 and IC50 of tetracycline were 0.404 mg/L, 0.73 mg/L. The IC10 and IC50 of ciprofloxacin were 0.004 mg/L and 0.007 mg/L.

2.2.2. Construction of Experimental Systems

To evaluate the effects of different antibiotics separately, at different concentration levels, on the structure of ARGs and ARB in the water, two systems were established. In order to better simulate the natural conditions of fish pond water, the glass beakers used to construct systems in this study were covered with black film. The water samples were left open in a room-temperature and well-ventilated environment and protected from light.
System-1 was established to assess the potential impact of varying concentrations of tetracycline on the absolute abundance of ARGs and the composition of ARB in pond water samples. System-1 was composed of a 1000 mL experimental arrangement incorporating 1000 mL of pond water enriched with 0.404 mg of tetracycline powder (with a purity of 98%; Aladdin, New York, NY, USA). Furthermore, an additional 1000 mL system was formed, containing 1000 mL of pond water along with 0.73 mg of tetracycline powder. The test period was 30 days. To support the growth of microorganisms, the water sample was stirred daily for 5 min at 300 r/min by a stirrer (JB-80, Shanghai Xiniulaibo Instrument Co., Ltd., Shanghai, China) to maintain adequate aeration. Throughout the experiment, the periodic addition of carbon sources in the form of anhydrous glucose powder (>99% purity; Macklin, Rochelle, IL, USA) was carried out every 3 days, with a dosage of 10 mg, and these additions were made directly to the water sample [29]. Considering an estimated tetracycline half-life of around five days in water, the administration of antibiotic powder in this experimental system was conducted at 5-day intervals [30].
System-2 was set up to investigate whether the pond water samples exposed to different concentrations of ciprofloxacin could cause a change in the absolute abundance of ARGs and the structure of ARB. Just like System-1, 1000 mL of a water sample was merged with 0.004 mg of ciprofloxacin powder (with a purity of 98%; Aladdin, New York, NY, USA). Additionally, 1000 mL of pond water was also mixed with 0.007 mg of ciprofloxacin powder. The observation of this study period was 30 days. The procedures for drug and glucose dosing, along with the frequency of daily water agitation, were conducted following the same protocols as previously outlined in System-1 [30].
In conjunction with the aforementioned systems, a control group was established to which no antibiotics were added. This control group was intended to demonstrate the inherent, spontaneous variation in ARGs and ARB over the course of time. To ensure the growth dynamics of microorganisms in the water column, daily aeration operations and glucose dosing were the same as for System-1 and System-2.

2.3. Sequencing of Bacterial 16S rRNA Genes

A total of 25 mL of liquid samples were gathered from each system at three distinct time points: 5 d, 15 d, and 30 d. The water samples collected on day 5, 15, and 30 were subjected to centrifugation at 10,000 rpm for 5 min to isolate the precipitate. To further extract the DNA of microorganisms in precipitate samples at the end of the experiment, the collected precipitate was stored immediately at −80 °C. Specifically, the total genomic DNA of microorganisms was extracted from each precipitate sample using the TIANamp Bacteria DNA Kit (TIANGEN, Beijing, China) following the manufacturer’s protocols. The quantity and quality of total genomic DNA were assessed using an ultra-microspectrophotometer (Quawell Q3000, Shanghai, China). Its concentration range was 3.1–44.1 ng/µL, which met the requirements of subsequent tests.
Subsequently, the quantification of the V3-V4 hypervariable regions of the 16S rRNA genes was performed using primers 314F (5′-CCTAYGGGRBGCASCAG-3′) and 806R (5′-GGACTACNNGGGTATCTAAT-3′) to analyze the microbial community in the collected samples. The reaction conditions for this assay were kept consistent with those used in the preceding experiment [31]. The PCR-purified samples underwent high-throughput sequencing utilizing the Illumina HiSeq 2500 platform (Illumina, San Diego, CA, USA). Clean reads were quality-controlled using an analysis software of Trimmomatic v 0.33 on the analytics platform of Biomarker Technologies (Beijing, China). The primers of raw reads were then identified and removed by Cutadapt 1.9.1. The paired-end clean reads were subjected to assembly into longer reads using Usearch v10 and, subsequently, the potential chimeric sequences were identified and eliminated utilizing the UCHIME v4.2 software. All sequences were divided into operational taxonomic units (OTUs) according to USEARCH 7.1 at 97% sequence similarity and subjected to bioinformatic statistical analysis. The representative sequences of OTUs were aligned against a 16S rRNA gene database employing QIIME (v 1.9.1) and the RDP classifier. Subsequently, the taxonomic classification and relative abundance of each OTU were determined. A quality assessment of genetic sequencing data is presented in Supplementary Table S2.

2.4. Quantification of ARGs and Integrase Genes

Liquid samples, each comprising 25 mL, were additionally retrieved from each respective system on days 5, 15, and 30. Subsequently, the total genomic DNA was isolated and assessed following the methods outlined in Section 2.3. Tetracycline-resistant genes (tetA, tetC), intI1 and ciprofloxacin-resistant gene (qnrA, qnrS) were then quantified with a StepOnePlus™ Real-Time PCR system (Thermo, Waltham, MA, USA). The primer sequences and annealing temperature are shown in Table S1. The reaction conditions of qPCR are described in the Supplementary Materials. In a sample, genes were deemed successfully amplified only if all three technical duplicates exhibited a positive outcome. The standard curve (Regression coefficients, R2 > 0.990) and the negative control were produced each run. The fluorescence threshold was set to 40. The generated standard curve was used for quantitative calibration. The starting copy number of the target genes was calculated by comparing the threshold cycle (CT) with the standard curve.

2.5. Statistical Analysis

Statistical analyses were conducted utilizing R version 4.1.2 and Origin 2021. Stacked bar charts illustrating the composition and proportion of microbial communities were created utilizing the R programming language, employing the barplot and vegan packages. The difference between groups was analyzed using the permanova test and the abund_jaccard distance algorithm. Nonmetric multidimensional scaling (NMDS) analysis, employing the Unweighted_unifrac distance matrix, was conducted to examine dissimilarities between samples. The box-plot was then plotted by BMK Microbial Diversity Analysis Platform. Histograms of the absolute abundance of ARGs and intI1 in water samples were performed using Origin 2021. By evaluating quantitative correlations between genes and genus-level microorganisms, potential hosts for the genes were determined. The clustering heat map was subsequently generated using the R programming language and the ggcor and ggplot2 packages. The t-test was adopted to estimate statistical differences and a difference was considered significant at p < 0.05.

3. Results and Discussion

3.1. Effects of Tetracycline on Microbial Community Composition in Pond Water

Figure 1 depicts the constituents and proportional representation of bacterial communities in the water samples retrieved from System-1. Proteobacteria exhibited a substantial predominance at the phylum level in all aquatic systems of System-1, followed by Bacteroidetes, Actinobacteria, Verrucomicrobia and Firmicutes (Figure 1b). This is in accordance with earlier observations in water contaminated with antibiotics or animal feces [32,33]. It could be concluded that the difference in antibiotic concentrations varied the structure of the microbial community at the phylum level [34,35]. For example, at 5 d, there were no large differences in the relative abundance of Bacteroidetes among the control group (9.21%), the Tet (IC10)-treated group (3.35%), and the Tet (IC50)-treated group (3.89%). In contrast, the relative abundance of Bacteroidetes in the control group, the Tet (IC10)-treated group and the Tet (IC50)-treated group was 9.84%, 21.81% and 24.24% at 30 d, respectively.
Additionally, the composition and relative abundances at the genus level on water samples of System-1 are displayed in Figure 1a. The primary predominant genera comprised 60% of the total bacterial 16S rRNA sequences, including Sphaerotilus, Acinetobacter, Aeromonas, Gemmobacter, Pseudmonas, Flavihumibacter, Runella, uncultured_bacterium_f_Rhodobacteraceae, uncultured_bacterium_f_Burkholderiaceae and Pseudorhodobacter. This is similar to the results of a previous study, which showed that Acinetobacter, Aeromonas and Pseudmonas were the dominant resistant bacterium in antibiotic-contaminated water bodies [36]. Furthermore, with respect to inter-bacterial correlations at the genus taxonomic rank, it was discerned that a positive correlation existed between Acinetobacter and Aeromonas (Supplementary Figure S1).
Regarding the relative abundance of the aforementioned bacterial genera, it was observed that, upon the addition of tetracycline at two concentrations (i.e., IC10 and IC50), alterations occurred in the relative abundance of these bacterial genera. For example, Flavihumibacter belongs to the Bacteroidetes. At 30 d, the relative abundance of Flavihumibacter in the Tet (IC10)-treated group and the Tet (IC50)-treated group was increased 1.4-fold and 1.1-fold, compared with the control group. In earlier studies, Flavihumibacter was found to be associated with the decomposition of organic matter in sediments [37]. It is therefore likely that such connections exist between growth in the abundance of Flavihumibacter and the increase in organic matter in the water column. This increase in organic matter could be attributed to the death of microorganisms caused by antibiotics. Changes in microbial structure would undoubtedly lead to the destabilization of biological functions in nature, resulting in a loss of stability in the environment such as nitrogen transformation, sulfate reduction, nutrient cycling or organic matter degradation in the environment [38].
In addition, according to the results of a hierarchical cluster analysis of the nine samples, at the genus level, microorganisms were divided into three different categories according to their composition and relative abundance at 5 d, 15 d, and 30 d (Figure 1a). It was observed that the impact of time on the microorganisms in the water column outweighed the effect of antibiotic concentration.
Following this, we employed NMDS as a visualization technique to evaluate the variability in microbial population structure at distinct sample locations within System 1. (Figure 1c). Nine sample points were not accurately divided into three groups based on the time factor, which may be due to the Unweighted_unifrac distance matrix algorithm. To examine whether time exerted a more pronounced influence on the changes in the microbial community structure in comparison to the concentration of administered tetracycline, we conducted an analysis of similarities (ANOSIM). This analysis was carried out using the permanova test and the abund_jaccard distance algorithm to assess the differences between the groups [39] (Figure 1d). We found that the within-group differences of these three groups were smaller than the between-group differences. As shown in Figure 1d, the median of three groups (5 d, 15 d and 30 d) was smaller than the median of the intergroup analysis group (All between groups). R2 = 0.90 showed that this subgroup explained a high degree of variance; p = 0.001 showed that the results of this analysis had a high degree of confidence [40]. This was consistent with the previous analyses suggesting that time played a key role in the alterations in microbial population structure.
To analyse the effects of exogenous antibiotics on the functional profiles of bacterial communities, a KEGG analysis was carried out to investigate the functional annotations of differentially expressed genes and differences in metabolic pathways [41]. We then conducted an analysis of the relationship between bacterial communities and their population function. The complex heatmap is shown in Supplementary Figure S2. We found that the time factor was more significant in altering population function compared to the concentration of different drugs. For example, the 5 d samples (Tet (IC10) 5 d and Tet (IC50) 5 d) or the 30 d samples (Tet (IC10) 30 d and Tet (IC50) 30 d) were more similar in terms of the functional predictions of the bacterial community, such as “cell growth and death” and “drug resistance: antimicrobial”. Additionally, we further found that low concentrations of tetracycline also promote altered population function. For instance, “Replication and repair” was upregulated in the Tet (IC10)-treated group at 30 d compared to the control group. This result confirmed that long-term exposure to antibiotics had an impact on population function. The reason for this change may be similar to that of the gut, which can be attributed to a change in population structure under the pressure of antibiotics [6].

3.2. Impact of Ciprofloxacin on Microbial Community Composition in Pond Water

Figure 2 illustrates the compositions and relative abundances of bacterial communities in System-2 water samples. Proteobacteria, Actinobacteria, Bacteroidetes, Firmicutes, and Verrucomicrobia constituted the prominent phyla in System-2’s microbial communities, each surpassing 1% in relative abundance (Figure 2b). This discovery is consistent with prior studies investigating the impact of antibiotic exposure on the microbial makeup of soil [42]. At the phylum level, the structure of the bacterial community was similar to that of System-1. The results suggested that those two types of antibiotics did not confer any fundamental advantages to changing the structure of the bacterial community at the phylum level. Like System-1, the microbial community abundance and structure was also affected by the difference in antibiotics concentrations at the phylum level. For instance, at 30 d, the relative abundance of Proteobacteria among the control group, the Cip (IC10)-treated group, and the Cip (IC50)-treated group was 68.41%, 70.35% and 52.89%, respectively. Since Proteobacteria carried ARGs and were more helpful in decomposing ciprofloxacin, the growth of the Proteobacteria under the treatment of IC10 ciprofloxacin is reasonable [43]. However, high concentrations of antibiotics might cause greater environmental stress on the Proteobacteria, inhibiting its growth in the Cip (IC50)-treated group [42].
Moreover, the composition and relative abundances of bacterial communities at the genus level in System-2 are displayed in Figure 2a. The abundance of 10 genera exceeded 2.0%, whereas the remaining genera exhibited abundances lower than 2.0% and were collectively categorized as others. The main genera were Sphaerotilus, Gemmobacter, Aeromonas, uncultured_bacterium_f_Rhizobiales_Incertae_Sedis, and Rhodoferax. In addition, we discovered that Gemmobacter was positively correlated with Phreatobacter (Supplementary Figure S3). The structure of the microbial community in System-2 was altered when compared to System-1 at the generic level. For instance, uncultured_bacterium_f_Rhizobiales_Incertae_Sedis, Rhodoferax, Arenimonas and Aurantimicrobium were added to the top ten dominant genera in System-2. Also, the average relative abundance of Acinetobacter decreased from 10.16% in System-1 down to 3.06% in System-2. This phenomenon indicated that different types of antibiotics might have different effects on shaping the microbial communities of aquatic systems [44,45].
Additionally, it was observed that the relative abundance of these genera also varied in response to the application of antibiotic ciprofloxacin at two concentrations (i.e., IC10 and IC50). For example, the filamentous bacterium Sphaerotilus, known for its sludge bulking [46], belonged to the phylum Proteobacteria. At 30 d, compared with the control group (2.34%), the relative abundance of Sphaerotilus in the Cip (IC10)-treated group was decreased to 1.60% but the Cip (IC50)-treated group appeared to be enhanced to 15.55% in terms of relative abundance.
At the genus level, the nine samples exhibited clear clustering patterns in the analysis of microbial populations, forming three distinct categories. The first cluster was the control 30 d, the Cip (IC10) 30 d, and the Cip (IC50) 30 d. The second cluster was the control 5 d and the Cip (IC50) 5 d. The third cluster was the control 15 d, the Cip (IC50) 15 d, the Cip (IC10) 15 d and the Cip (IC10) 5 d (Figure 2a). The clustering analysis of System-2 at the genus level displayed similarities with that of System-1, indicating that temporal variations had a more significant impact on the structural composition of the microbial community compared to fluctuations in antibiotic concentration. To further corroborate this finding, we analyzed variations in the structure of microbial communities at each sample site within System-2 using NMDS (Figure 2c). We found that microbial communities tend to be clustered together at all times. The clustering of sample sites indicated that the distribution of microbial communities in samples taken at different times varied greatly (Stress = 0.0597 < 0.2). Additionally, this result was further confirmed by the analysis of differences between groups (Figure 2d).
In addition, as in System-1, low concentrations of ciprofloxacin altered population function (Supplementary Figure S4). For example, In the Cip (IC10)-treated group on day 30, “Energy metabolism” and “Amino acid metabolism” exhibited down-regulation compared to the control group. (Supplementary Figure S4). This was largely due to variations in metabolic pathways to diminish its biological toxicity in the environment [47].

3.3. Impacts of Antibiotics on Microbial Community Resistance in Pond Water

The observation indicates that sub-inhibitory concentrations of antibiotics could facilitate the generation and dissemination of ARGs as a consequence of environmental stress [48]. To measure the effects of tetracycline on ARGs and intI1, the absolute abundances of tetA, tetC and intI1 were evaluated according to the experimental design shown in Figure 3a. The absolute abundances of tetA and tetC were significantly enhanced under tetracycline pressure (p < 0.001), compared with the control (Figure 3b). It was noted that tetracycline dosages in IC10 more significantly facilitated the absolute abundance of tetA and tetC compared with the Tet (IC50)-treated group (Figure 3b, p < 0.001). As an illustration, the Tet (IC10)-treated group, at 30 d, exhibited the highest value, with a remarkable 44-fold increase in absolute abundance compared to the Tet (IC50)-treated group (Figure 3c, p < 0.01). This was consistent with the results of a study showing that the abundance of tetracycline-resistant genes was enhanced due to the sub-MIC tetracycline being chronically exposed to the advanced biological wastewater treatment system (ABWWTS) [49].
Notably, mobile genetic elements (MGEs), such as intI1, were commonly associated with genes conferring resistance to antibiotics, disinfectants, and heavy metals, and assigned to a wide range of pathogenic and non-pathogenic bacteria. This was considered a proxy for environmental pollution [33]. Here, we found that exposure to the antibiotic tetracycline caused significantly increased absolute abundances of intl1 (Figure 4, p < 0.001). These results match the observations of earlier studies [50]. Additionally, the maximum absolute abundance was detected during exposure to IC10 tetracycline on 30 d. This showed a more than 2.06-fold increase in absolute abundance in comparison to the Tet (IC50)-treated group (Figure 4, p < 0.001).
In addition, the effect of ciprofloxacin on the absolute abundance of ARGs was studied, as shown in Figure 5a. The results suggested that the two concentrations of ciprofloxacin did not significantly change the absolute abundance of qnrA (Figure 5b, p > 0.05). On 5 d, 15 d and 30 d, the absolute abundances in the three groups were maintained at around 5.3 × 104 copies/mL, 4.7 × 104 copies/mL, and 6.0 × 104 copies/mL, respectively. Additionally, the absolute abundance of qnrS was investigated to evaluate whether ciprofloxacin and tetracycline had the same effect on the abundance of ARGs (Figure 5c). It was seen that the absolute abundance was also significantly enhanced in the presence of ciprofloxacin (Figure 5c, p < 0.01). For example, compared with the control group, the absolute abundance of qnrS increased > 15.6 times under the exposure of IC10 ciprofloxacin on 15 d. In addition, the absolute abundance of qnrS also reached the maximum in the Cip (IC10)-treated group at 30 d, showing a 41.2 times higher absolute abundance compared to the control group. (Figure 5c, p < 0.001).
Collectively, it was concluded that tetracycline had a better ability to induce ARGs than ciprofloxacin. This might be attributed to the different antimicrobial mechanisms of the two drugs. Tetracycline, as a protein synthesis inhibitor, interferes with bacterial protein production. However, ciprofloxacin primarily disrupts bacterial DNA replication. This interference might generate greater environmental selective pressure and disrupt the intricate network of gene regulation within bacteria, leading to the activation of specific ARGs. Also, most tet genes were found on mobile units, which exacerbated the spread and abundance of tet genes [8]. Additionally, the introduction of antibiotic pharmaceuticals at low concentrations (IC10) led to a notable rise in the absolute abundance of both ARGs and intI1 (p < 0.01), in comparison to high concentrations (IC50) (except the qnrA). This result might be attributed to the long-term exposure to a higher concentration of antibiotics in water, leading to a more stressful environment for the survival of microorganisms. In addition, according to earlier studies, reactive oxygen species (ROS) in bacterial cells could be induced by a low concentration of antibiotics, leading to genetic recombination and mutation, which reduces the susceptibility of bacteria to antibiotic molecules [51]. Hence, a heightened risk to public health arises from the diminished levels of exogenous antibiotics, as they have the potential to induce a more pronounced contamination of natural aquatic ecosystems with ARGs. Meanwhile, the high abundance of ARGs influenced the metabolic systems of microorganisms in the environment, causing alterations in the normal physiological functions and population dynamics of microbial communities. Through horizontal transfer, they spread between pathogenic and non-pathogenic bacteria, driving a gradual increase in ARB in the environment and posing a potential threat to human health through the food chain. This also induced bacteria to develop multiple drug resistances, thereby amplifying the challenges posed by infectious diseases to existing healthcare systems [52].

3.4. Interrelationship among Microbial Community, intI1, and ARGs

To gain deeper insights into the interactions among intI1, ARGs, and microbial communities during exposure to exogenous antibiotics, a clustering Heatmap was generated utilizing the absolute abundance of intI1 and ARGs, along with their potential host bacteria (the top 30 at the genera level) (Figure 6).
Bacteria known to be strongly and significantly associated with ARGs or MGEs could be considered potential hosts [53]. In particular, intI1 demonstrated a positive correlation with over 10 generals; however, among those, Flavihumibacter (p < 0.05) and Hydrogenophaga (p < 0.05) were the only genera that showed significant and positive associations. This is also in accordance with earlier observations, which showed that Hydrogenophaga plays an important role in the spread and variation of ARGs and intI1 in freshwater environments [54]. For the tetA, 10 of the 30 genera were positively correlated, but only Acinetobacter was significantly and positively correlated (p < 0.01), which has been earlier identified as a host for ARGs in aquaculture ponds [55]. TetC was significantly and positively related to several bacteria, such as Pseudomonas (p < 0.01), Acidovorax (p < 0.05), Aeromonas (p < 0.05) and Acinetobacter (p < 0.001). Notably, previous studies indicated that Pseudomonas was a potentially pathogenic bacterium [53]. Aeromonas, Acinetobacter and Acidovorax were also found to carry multiple ARGs [56,57]. This further increased the health risk of spreading ARGs in landscape pond water. qnrS showed a significant and strongly positive correlation with Unclassified_Comamonadaceae (p < 0.01), Malikia (p < 0.001) and Zoogloea (p < 0.001). Of these, Zoogloea was shown in previous studies to be a potential host for ARGs and heavy-metal-resistance genes (HMRGs) [58]. Additionally, qnrA presented a significantly positive correlation with unclassified_Rhizobiales_Incertae_Sedis (p < 0.01), Aurantimicrobium (p < 0.05), and Others (p < 0.05). It was concluded that these various antibiotics might transfer the host bacteria of ARGs. This phenomenon could be attributed to variations in the structure of microbial communities in water under antibiotic treatment [59]. This would undoubtedly further accelerate the frequency of ARGs in water, resulting in an increase in the amount of ARGs in environmental media, jeopardizing human health and causing greater environmental risks.

4. Conclusions

This research highlighted that temporal changes had a stronger impact on bacterial population composition and abundance than variations in antibiotic concentration. This provided a novel perspective into antibiotic resistance. In addition, distinct varieties and levels of externally introduced antibiotics enhanced the absolute abundance of ARGs and intI1 in urban landscape pond water. In comparison to the high levels of antibiotics, lower concentrations exhibited a heightened potential for elevating the prevalence of ARGs and intI1. These findings serve as a crucial reminder to re-think and re-evaluate the potential environmental impact of antibiotic concentrations. Furthermore, the absolute abundance of ARGs and intI1 was assessed in water samples over a 30-day timeframe subsequent to antibiotic exposure. Notably, tetracycline exhibited a greater capacity for inducing ARGs compared to ciprofloxacin. Another innovative finding of this study was the diverse composition of host bacteria associated with ARGs, which could be attributed to the distinct antibiotic types that were employed.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/w15173047/s1, Figure S1: The species-related network analysis of experimental groups of tetracycline at the genus level. The circle represents the species, the size of the circle represents the average abundance of the species. The line represents the correlation between the two species. The thickness of the line represents the strength of the correlation and the colour of the line: orange represents a positive correlation and green represents a negative correlation; Figure S2: The heat map of functional profiles at level 2 KEGG on tetracycline experimental group samples; Figure S3: The species-related network analysis of experimental groups of ciprofloxacin at the genus level. The circle represents the species, the size of the circle represents the average abundance of the species. The line represents the correlation between the two species. The thickness of the line represents the strength of the correlation and the colour of the line: orange represents a positive correlation and green represents a negative correlation; Figure S4: The heat map of functional profiles at level 2 KEGG on ciprofloxacin experimental group samples; Table S1: Primers used for the detection of ARGs [60,61,62,63,64]; Table S2: Quality assessment of genetic sequencing data.

Author Contributions

Conceptualization, H.F. and S.Z.; data curation, Q.M. and Y.B.; formal analysis, L.T.; funding acquisition, Y.M.; investigation, Q.M. and Y.B.; methodology, H.F. and S.Z.; project administration, Y.M.; resources, Y.M.; supervision, Y.H. and N.Y.; visualization, Z.Y., D.H. and X.W.; writing—original draft, L.T. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China [No. 41975172].

Data Availability Statement

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Acknowledgments

The authors are grateful to the support from the National Natural Science Foundation of China (No. 41975172).

Conflicts of Interest

The authors declare that they have no competing interest.

References

  1. Halling-Sorensen, B.; Nors Nielsen, S.; Lanzky, P.F.; Ingerslev, F.; Holten Lutzhoft, H.C.; Jorgensen, S.E. Occurrence, fate and effects of pharmaceutical substances in the environment: A review. Chemosphere 1998, 36, 357–393. [Google Scholar] [CrossRef] [PubMed]
  2. Qiao, M.; Ying, G.G.; Singer, A.C.; Zhu, Y.G. Review of antibiotic resistance in China and its environment. Environ. Int. 2018, 110, 160–172. [Google Scholar] [CrossRef] [PubMed]
  3. Wang, X.; Lin, Y.; Zheng, Y.; Meng, F. Antibiotics in mariculture systems: A review of occurrence, environmental behavior, and ecological effects. Environ. Pollut. 2022, 293, 118541. [Google Scholar] [CrossRef]
  4. Kemper, N. Veterinary antibiotics in the aquatic and terrestrial environment. Ecol. Indic. 2008, 8, 1–13. [Google Scholar] [CrossRef]
  5. Nappier, S.P.; Liguori, K.; Ichida, A.M.; Stewart, J.R.; Jones, K.R. Antibiotic resistance in recreational waters: State of the science. Int. J. Environ. Res. Public Health 2020, 17, 8034. [Google Scholar] [CrossRef]
  6. Liu, L.; Wang, Q.; Lin, H.; Das, R.; Wang, S.; Qi, H.; Yang, J.; Xue, Y.; Mao, D.; Luo, Y. Amoxicillin increased functional pathway genes and beta-lactam resistance genes by pathogens bloomed in intestinal microbiota using a simulator of the human intestinal microbial ecosystem. Front. Microbiol. 2020, 11, 1213. [Google Scholar] [CrossRef]
  7. Qin, K.N.; Wei, L.L.; Li, J.J.; Lai, B.; Zhu, F.Y.; Yu, H.; Zhao, Q.L.; Wang, K. A review of ARGs in WWTPs: Sources, stressors, and elimination. Chin. Chem. Lett. 2020, 31, 2603–2613. [Google Scholar] [CrossRef]
  8. Chopra, I.; Roberts, M. Tetracycline antibiotics: Mode of action, applications, molecular biology, and epidemiology of bacterial resistance. Microbiol. Mol. Biol. Rev. 2001, 65, 232–260. [Google Scholar] [CrossRef]
  9. Pruden, A.; Pei, R.; Storteboom, H.; Carlson, K.H. Antibiotic resistance genes as emerging contaminants: Studies in northern Colorado. Environ. Sci. Technol. 2006, 40, 7445–7450. [Google Scholar] [CrossRef]
  10. Li, S.N.; Zhang, C.F.; Li, F.X.; Hua, T.; Zhou, Q.X.; Ho, S.H. Technologies towards antibiotic resistance genes (ARGs) removal from aquatic environment: A critical review. J. Hazard. Mater. 2021, 411, 125148. [Google Scholar] [CrossRef]
  11. Zhang, S.; Wang, Y.; Lu, J.; Yu, Z.; Song, H.; Bond, P.L.; Guo, J. Chlorine disinfection facilitates natural transformation through ROS-mediated oxidative stress. ISME J. 2021, 15, 2969–2985. [Google Scholar] [CrossRef]
  12. Zainab, S.M.; Junaid, M.; Xu, N.; Malik, R.N. Antibiotics and antibiotic resistant genes (ARGs) in groundwater: A global review on dissemination, sources, interactions, environmental and human health risks. Water Res. 2020, 187, 116455. [Google Scholar] [CrossRef] [PubMed]
  13. Hu, H.W.; Wang, J.T.; Li, J.; Shi, X.Z.; Ma, Y.B.; Chen, D.; He, J.Z. Long-term nickel contamination increases the occurrence of antibiotic resistance genes in agricultural soils. Environ. Sci. Technol. 2017, 51, 790–800. [Google Scholar] [CrossRef] [PubMed]
  14. Zhang, Q.; Liu, Q.; Han, Y.N. Assessing the fishery resource status of China’s coastal waters using surplus production models. J. Ocean Univ. China 2021, 20, 1236–1244. [Google Scholar] [CrossRef]
  15. Santos, L.; Ramos, F. Analytical strategies for the detection and quantification of antibiotic residues in aquaculture fishes: A review. Trends Food Sci. Technol. 2016, 52, 16–30. [Google Scholar] [CrossRef]
  16. Ning, K.; Ji, L.; Zhang, L.; Zhu, X.; Wei, H.; Han, M.; Wang, Z. Is rice-crayfish co-culture a better aquaculture model: From the perspective of antibiotic resistome profiles. Environ. Pollut. 2022, 292, 118450. [Google Scholar] [CrossRef]
  17. Wang, C.; Liu, X.; Yang, Y.; Wang, Z. Antibiotic and antibiotic resistance genes in freshwater aquaculture ponds in China: A meta-analysis and assessment. J. Clean. Prod. 2021, 329, 129719. [Google Scholar] [CrossRef]
  18. Xu, J.Y.; Sangthong, R.; McNeil, E.; Tang, R.; Chongsuvivatwong, V. Antibiotic use in chicken farms in northwestern China. Antimicrob. Resist. Infect. Control 2020, 9, 10. [Google Scholar] [CrossRef]
  19. Zinia, N.J.; McShane, P. Urban ecosystems and ecosystem services in megacity Dhaka: Mapping and inventory analysis. Urban Ecosyst. 2021, 24, 915–928. [Google Scholar] [CrossRef]
  20. Wang, H.; Mei, C.; Liu, J.; Shao, W. A new strategy for integrated urban water management in China: Sponge city. Sci. China Technol. Sci. 2018, 61, 317–329. [Google Scholar] [CrossRef]
  21. Chen, S.; van de Ven, F.H.M.; Zevenbergen, C.; Verbeeck, S.; Ye, Q.; Zhang, W.; Wei, L. Revisiting China’s sponge city planning approach: Lessons from a case study on qinhuai district, Nanjing. Front. Environ. Sci. 2021, 9, 748231. [Google Scholar] [CrossRef]
  22. Luis Martinez, J. Environmental pollution by antibiotics and by antibiotic resistance determinants. Environ. Pollut. 2009, 157, 2893–2902. [Google Scholar] [CrossRef]
  23. Mo, W.Y.; Chen, Z.; Leung, H.M.; Leung, A.O.W. Application of veterinary antibiotics in China’s aquaculture industry and their potential human health risks. Environ. Sci. Pollut. Res. 2017, 24, 8978–8989. [Google Scholar] [CrossRef]
  24. Bunnajirakul, S.; Pavasutthipaisit, S.; Steinhagen, D. Pathological alterations due to motile Aeromonas infection in red swordtail fish. Tieraerztl. Prax. K. H. 2015, 43, 434–438. [Google Scholar] [CrossRef] [PubMed]
  25. Cabello, F.C.; Godfrey, H.P.; Tomova, A.; Ivanova, L.; Doelz, H.; Millanao, A.; Buschmann, A.H. Antimicrobial use in aquaculture re-examined: Its relevance to antimicrobial resistance and to animal and human health. Environ. Microbiol. 2013, 15, 1917–1942. [Google Scholar] [CrossRef]
  26. Pham, T.D.M.; Ziora, Z.M.; Blaskovich, M.A.T. Quinolone antibiotics. MedChemComm 2019, 10, 1719–1739. [Google Scholar] [CrossRef]
  27. Wang, Y.; Lu, J.; Mao, L.; Li, J.; Yuan, Z.; Bond, P.L.; Guo, J. Antiepileptic drug carbamazepine promotes horizontal transfer of plasmid-borne multi-antibiotic resistance genes within and across bacterial genera. ISME J. 2019, 13, 509–522. [Google Scholar] [CrossRef]
  28. Du, R.J.; Qu, Y.J.; Zhao, M.; Liu, Y.H.; Qi, P.X.; Sun, X.B. Logistic modeling to predict the minimum inhibitory concentration (MIC) of olive leaf extract (OLE) against Listeria monocytogenes. PLoS ONE 2022, 17, e0263359. [Google Scholar] [CrossRef]
  29. Hou, W.; Sun, S.; Wang, M.; Gu, B.; Li, X.; Zhang, C.; Jia, R. Variations in stable carbon and nitrogen isotopes of particulate organic matter in surface waters of water-receiving area of Eastern Route of South-to-North Water Transfer Project, China. Environ. Sci. Pollut. Res. 2020, 27, 2805–2818. [Google Scholar] [CrossRef]
  30. Liu, X.W.; Lv, K.; Deng, C.X.; Yu, Z.M.; Shi, J.H.; Johnson, A.C. Persistence and migration of tetracycline, sulfonamide, fluoroquinolone, and macrolide antibiotics in streams using a simulated hydrodynamic system. Environ. Pollut. 2019, 252, 1532–1538. [Google Scholar] [CrossRef]
  31. Sun, R.; He, L.; Li, T.; Dai, Z.; Sun, S.; Ren, L.; Liang, Y.Q.; Zhang, Y.; Li, C. Impact of the surrounding environment on antibiotic resistance genes carried by microplastics in mangroves. Sci. Total Environ. 2022, 837, 155771. [Google Scholar] [CrossRef] [PubMed]
  32. Yang, Q.; Ren, S.; Niu, T.; Guo, Y.; Qi, S.; Han, X.; Liu, D.; Pan, F. Distribution of antibiotic-resistant bacteria in chicken manure and manure-fertilized vegetables. Environ. Sci. Pollut. Res. 2014, 21, 1231–1241. [Google Scholar] [CrossRef]
  33. Gillings, M.R.; Gaze, W.H.; Pruden, A.; Smalla, K.; Tiedje, J.M.; Zhu, Y.G. Using the class 1 integron-integrase gene as a proxy for anthropogenic pollution. ISME J. 2015, 9, 1269–1279. [Google Scholar] [CrossRef] [PubMed]
  34. Ding, C.; He, J. Effect of antibiotics in the environment on microbial populations. Appl. Microbiol. Biotechnol. 2010, 87, 925–941. [Google Scholar] [CrossRef] [PubMed]
  35. Lundstrom, S.V.; Ostman, M.; Bengtsson-Palme, J.; Rutgersson, C.; Thoudal, M.; Sircar, T.; Blanck, H.; Eriksson, K.M.; Tysklind, M.; Flach, C.F.; et al. Minimal selective concentrations of tetracycline in complex aquatic bacterial biofilms. Sci. Total Environ. 2016, 553, 587–595. [Google Scholar] [CrossRef]
  36. Henderson, M.; Ergas, S.J.; Ghebremichael, K.; Gross, A.; Ronen, Z. Occurrence of antibiotic-resistant genes and bacteria in household greywater treated in constructed wetlands. Water 2022, 14, 758. [Google Scholar] [CrossRef]
  37. Hou, Y.; Li, B.; Feng, G.; Zhang, C.; He, J.; Li, H.; Zhu, J. Responses of bacterial communities and organic matter degradation in surface sediment to macrobrachium nipponense bioturbation. Sci. Total Environ. 2021, 759, 143534. [Google Scholar] [CrossRef]
  38. Gibson, M.K.; Forsberg, K.J.; Dantas, G. Improved annotation of antibiotic resistance determinants reveals microbial resistomes cluster by ecology. ISME J. 2015, 9, 207–216. [Google Scholar] [CrossRef]
  39. Somerfield, P.J.; Clarke, K.R.; Gorley, R.N. Analysis of similarities (ANOSIM) for 2-way layouts using a generalised ANOSIM statistic, with comparative notes on permutational multivariate analysis of variance (PERMANOVA). Austral Ecol. 2021, 46, 911–926. [Google Scholar] [CrossRef]
  40. He, H.; Xie, Y. Effect of different hemodialysis methods on microbiota in uremic patients. Biomed. Res. Int. 2020, 2020, 6739762. [Google Scholar] [CrossRef]
  41. Tong, C.; Hu, H.; Chen, G.; Li, Z.; Li, A.; Zhang, J. Chlorine disinfectants promote microbial resistance in Pseudomonas sp. Environ. Res. 2021, 199, 111296. [Google Scholar] [CrossRef] [PubMed]
  42. Lin, H.; Jin, D.; Freitag, T.E.; Sun, W.; Yu, Q.; Fu, J.; Ma, J. A compositional shift in the soil microbiome induced by tetracycline, sulfamonomethoxine and ciprofloxacin entering a plant-soil system. Environ. Pollut. 2016, 212, 440–448. [Google Scholar] [CrossRef] [PubMed]
  43. Liao, X.B.; Li, B.X.; Zou, R.S.; Dai, Y.; Xie, S.G.; Yuan, B.L. Biodegradation of antibiotic ciprofloxacin: Pathways, influential factors, and bacterial community structure. Environ. Sci. Pollut. Res. 2016, 23, 7911–7918. [Google Scholar] [CrossRef]
  44. Kang, J.; Zhang, Z.; Chen, Y.; Zhou, Z.; Zhang, J.; Xu, N.; Zhang, Q.; Lu, T.; Peijnenburg, W.J.G.M.; Qian, H. Machine learning predicts the impact of antibiotic properties on the composition and functioning of bacterial community in aquatic habitats. Sci. Total Environ. 2022, 828, 154412. [Google Scholar] [CrossRef]
  45. Liu, F.; Wu, J.; Ying, G.G.; Luo, Z.; Feng, H. Changes in functional diversity of soil microbial community with addition of antibiotics sulfamethoxazole and chlortetracycline. Appl. Microbiol. Biotechnol. 2012, 95, 1615–1623. [Google Scholar] [CrossRef]
  46. Du, B.; Yang, Q.; Li, X.; Yuan, W.; Chen, Y.; Wang, R. Impacts of long-term exposure to tetracycline and sulfamethoxazole on the sludge granules in an anoxic-aerobic wastewater treatment system. Sci. Total Environ. 2019, 684, 67–77. [Google Scholar] [CrossRef]
  47. Zhang, X.; Zhang, Y.; Wu, N.; Li, W.; Song, X.; Ma, Y.; Niu, Z. Colonization characteristics of bacterial communities on plastic debris: The localization of immigrant bacterial communities. Water Res. 2021, 19, 116883. [Google Scholar] [CrossRef]
  48. Grenni, P.; Ancona, V.; Caracciolo, A.B. Ecological effects of antibiotics on natural ecosystems: A review. Microchem. J. 2018, 136, 25–39. [Google Scholar] [CrossRef]
  49. Zhang, Y.; Pei, M.; Zhang, B.; He, Y.; Zhong, Y. Changes of antibiotic resistance genes and bacterial communities in the advanced biological wastewater treatment system under low selective pressure of tetracycline. Water Res. 2021, 207, 117834. [Google Scholar] [CrossRef]
  50. Gao, P.; Xu, W.; Ruan, X.; Qian, Y.; Xue, G.; Jia, H. Long-term impact of a tetracycline concentration gradient on the bacterial resistance in anaerobic-aerobic sequential bioreactors. Chemosphere 2018, 205, 308–316. [Google Scholar] [CrossRef]
  51. Kohanski, M.A.; DePristo, M.A.; Collins, J.J. Sublethal antibiotic treatment leads to multidrug resistance via radical-induced mutagenesis. Mol. Cell 2010, 37, 311–320. [Google Scholar] [CrossRef] [PubMed]
  52. Jutkina, J.; Marathe, N.P.; Flach, C.F.; Larsson, D.G.J. Antibiotics and common antibacterial biocides stimulate horizontal transfer of resistance at low concentrations. Sci. Total Environ. 2018, 616, 172–178. [Google Scholar] [CrossRef] [PubMed]
  53. Guo, W.; Huang, C.; Xi, B.; Tang, Z.; Tan, W.; Li, W.; Zhang, Y.; Li, W. The maturity period is the main stage of antibiotic resistance genes reduction in aerobic composting process of swine manure in sub-scale farms. Bioresour. Technol. 2021, 319, 124139. [Google Scholar] [CrossRef] [PubMed]
  54. Fang, H.; Ye, N.; Huang, K.; Yu, J.; Zhang, S. Mobile genetic elements drive the antibiotic resistome alteration in freshwater shrimp aquaculture. Water 2021, 13, 1461. [Google Scholar] [CrossRef]
  55. Chen, J.; Yang, Y.; Jiang, X.; Ke, Y.; He, T.; Xie, S. Metagenomic insights into the profile of antibiotic resistomes in sediments of aquaculture wastewater treatment system. J. Environ. Sci. 2022, 113, 345–355. [Google Scholar] [CrossRef]
  56. Roberts, M.C.; Schwarz, S. Tetracycline and phenicol resistance genes and mechanisms: Importance for agriculture, the environment, and humans. J. Environ. Qual. 2016, 45, 576–592. [Google Scholar] [CrossRef]
  57. Yuan, Q.B.; Huang, Y.M.; Wu, W.B.; Zuo, P.; Hu, N.; Zhou, Y.Z.; Alvarez, P.J.J. Redistribution of intracellular and extracellular free & adsorbed antibiotic resistance genes through a wastewater treatment plant by an enhanced extracellular DNA extraction method with magnetic beads. Environ. Int. 2019, 131, 104986. [Google Scholar]
  58. Wang, Z.; Yuan, S.; Deng, Z.; Wang, Y.; Deng, S.; Song, Y.; Sun, C.; Bu, N.; Wang, X. Evaluating responses of nitrification and denitrification to the co-selective pressure of divalent zinc and tetracycline based on resistance genes changes. Bioresour. Technol. 2020, 314, 123769. [Google Scholar] [CrossRef]
  59. Zhu, N.; Jin, H.; Ye, X.; Liu, W.; Li, D.; Shah, G.M.; Zhu, Y. Fate and driving factors of antibiotic resistance genes in an integrated swine wastewater treatment system: From wastewater to soil. Sci. Total Environ. 2020, 721, 137654. [Google Scholar] [CrossRef]
  60. Ng, L.K.; Martin, I.; Alfa, M.; Mulvey, M. Multiplex PCR for the detection of tetracycline resistant genes. Mol. Cell. Probe. 2001, 15, 209–215. [Google Scholar] [CrossRef]
  61. Aminov, R.I.; Chee-Sanford, J.C.; Garrigues, N.; Teferedegne, B.; Krapac, I.J.; White, B.A.; Mackie, R.I. Development, validation, and application of PCR primers for detection of tetracycline efflux genes of gram-negative bacteria. Appl. Environ. Microb. 2002, 68, 1786–1793. [Google Scholar] [CrossRef] [PubMed]
  62. Wu, D.; Huang, Z.; Yang, K.; Graham, D.; Xie, B. Relationships between Antibiotics and Antibiotic Resistance Gene Levels in Municipal Solid Waste Leachates in Shanghai, China. Environ. Sci. Technol. 2015, 49, 4122–4128. [Google Scholar] [CrossRef] [PubMed]
  63. Zhu, Y.G.; Johnson, T.A.; Su, J.Q.; Qiao, M.; Guo, G.X.; Stedtfeld, R.D.; Hashsham, S.A.; Tiedje, J.M. Diverse and abundant antibiotic resistance genes in Chinese swine farms. Proc. Natl. Acad. Sci. USA 2013, 110, 3435–3440. [Google Scholar] [CrossRef] [PubMed]
  64. Marti, E.; Luis Balcazar, J. Real-Time PCR Assays for Quantification of qnr Genes in Environmental Water Samples and Chicken Feces. Appl. Environ. Microb. 2013, 79, 1743–1745. [Google Scholar] [CrossRef] [PubMed]
Figure 1. (a) Assessment of microbial community similarities at the genus level. (b) Constituent makeup of the prevailing phylum within microbial collectives. (c) Presentation of principal coordinate analysis diagrams for OTUs (Unweighted_unifrac distance matrix. (d) Beta diversity inter-group variation analysis of microbial communities (abund_jaccard distance matrix).
Figure 1. (a) Assessment of microbial community similarities at the genus level. (b) Constituent makeup of the prevailing phylum within microbial collectives. (c) Presentation of principal coordinate analysis diagrams for OTUs (Unweighted_unifrac distance matrix. (d) Beta diversity inter-group variation analysis of microbial communities (abund_jaccard distance matrix).
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Figure 2. (a) Assessment of microbial community similarities at the genus level. (b) Constituent makeup of the prevailing phylum within microbial collectives. (c) Presentation of principal coordinate analysis diagrams for OTUs (Unweighted_unifrac distance matrix). (d) Beta diversity inter-group variation analysis of microbial communities (abund_jaccard distance matrix).
Figure 2. (a) Assessment of microbial community similarities at the genus level. (b) Constituent makeup of the prevailing phylum within microbial collectives. (c) Presentation of principal coordinate analysis diagrams for OTUs (Unweighted_unifrac distance matrix). (d) Beta diversity inter-group variation analysis of microbial communities (abund_jaccard distance matrix).
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Figure 3. (a) A comprehensive illustration depicting the impact of tetracycline on both ARB and ARGs. The absolute abundance patterns of tetA (b) and tetC (c) within the water samples, encompassing instances of no drug treatment, IC10 tetracycline exposure, and IC50 tetracycline exposure (“**” represent p < 0.01, “***” represent p < 0.001).
Figure 3. (a) A comprehensive illustration depicting the impact of tetracycline on both ARB and ARGs. The absolute abundance patterns of tetA (b) and tetC (c) within the water samples, encompassing instances of no drug treatment, IC10 tetracycline exposure, and IC50 tetracycline exposure (“**” represent p < 0.01, “***” represent p < 0.001).
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Figure 4. The absolute abundance patterns of intI1 within the water samples, encompassing instances of no drug treatment, IC10 tetracycline exposure, and IC50 tetracycline exposure (“***” represent p < 0.001).
Figure 4. The absolute abundance patterns of intI1 within the water samples, encompassing instances of no drug treatment, IC10 tetracycline exposure, and IC50 tetracycline exposure (“***” represent p < 0.001).
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Figure 5. (a) A comprehensive illustration depicting the impact of ciprofloxacin on both ARB and ARGs. The absolute abundance patterns of qnrA (b) and qnrS (c) within the water samples, encompassing instances of no drug treatment, IC10 ciprofloxacin exposure, and IC50 ciprofloxacin exposure (“*” represent p < 0.05, “**” represent p < 0.01, “***” represent p < 0.001).
Figure 5. (a) A comprehensive illustration depicting the impact of ciprofloxacin on both ARB and ARGs. The absolute abundance patterns of qnrA (b) and qnrS (c) within the water samples, encompassing instances of no drug treatment, IC10 ciprofloxacin exposure, and IC50 ciprofloxacin exposure (“*” represent p < 0.05, “**” represent p < 0.01, “***” represent p < 0.001).
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Figure 6. Network analysis between ARGs and their potential host bacteria (“*” represent p < 0.05, “**” represent p < 0.01, “***” represent p < 0.001).
Figure 6. Network analysis between ARGs and their potential host bacteria (“*” represent p < 0.05, “**” represent p < 0.01, “***” represent p < 0.001).
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Tian, L.; Fang, H.; Mao, Q.; Bai, Y.; Ye, Z.; Hu, D.; Wang, X.; Hou, Y.; Ye, N.; Zhang, S.; et al. Low Concentrations of Antibiotics Alter Microbial Communities and Induce High Abundances of Antibiotic-Resistant Genes in Ornamental Water. Water 2023, 15, 3047. https://doi.org/10.3390/w15173047

AMA Style

Tian L, Fang H, Mao Q, Bai Y, Ye Z, Hu D, Wang X, Hou Y, Ye N, Zhang S, et al. Low Concentrations of Antibiotics Alter Microbial Communities and Induce High Abundances of Antibiotic-Resistant Genes in Ornamental Water. Water. 2023; 15(17):3047. https://doi.org/10.3390/w15173047

Chicago/Turabian Style

Tian, Lingyun, Hao Fang, Qianbo Mao, Yi Bai, Zirui Ye, Dingjun Hu, Xiaoheng Wang, Yiyu Hou, Nan Ye, Shuai Zhang, and et al. 2023. "Low Concentrations of Antibiotics Alter Microbial Communities and Induce High Abundances of Antibiotic-Resistant Genes in Ornamental Water" Water 15, no. 17: 3047. https://doi.org/10.3390/w15173047

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