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The correlation between antibiotic resistance gene abundance and microbial community resistance in pig farm wastewater and surrounding rivers
Ecotoxicology and Environmental Safety 182 (2019) 109452
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Ecotoxicology and Environmental Safety journal homepage: www.elsevier.com/locate/ecoenv
The correlation between antibiotic resistance gene abundance and microbial community resistance in pig farm wastewater and surrounding rivers
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Yiwen Yanga, Zixiao Liua, Sicheng Xinga, Xindi Liaoa,b,c,* a
College of Animal Science, South China Agricultural University, Guangzhou, 510642, China Key Laboratory of Tropical Agricultural Environment, Ministry of Agriculture, South China Agricultural University, Guangzhou, 510642, China c Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agriculture University, Guangzhou, 510642, China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Antimicrobial resistance gene (ARG) Microbial resistance (MR) Wastewater Pig farm
Antimicrobial resistance gene (ARG) abundance and microbial resistance (MR) are often used as important indicators of pollution risk; however, the relationship between ARGs abundance and MR in pig farm wastewater remains unknown. In this study, the raw pig farm wastewater, effluent water, upstream river water, domestic wastewater and downstream river water samples were collected. The concentration of 20 subtypes of ARGs and 2 integrons, minimal inhibit concentration (MIC), and bacterial communities were investigated. In this study, 20 subtypes of ARGs and integrons were detected in all sampling sites. The highest abundance of 17 of the 20 subtypes of ARGs was detected in raw pig farm wastewater, and ermA had the maximum average abundance of 108 copies/mL, with up to 2.41 ± 0.12 × 108 copies/mL. There was no significant correlation between MR to three antibiotics (ciprofloxacin, streptomycin and tetracycline hydrochloride) and the abundance of their corresponding ARGs (P > 0.05), and a large difference was detected between the types of ARGs co-occur bacteria and resistance co-occur bacteria in the 5 sampling sites. And the pig farm wastewater treatment (WWT) could effectively reduce the ARGs and MR to the 3 antibiotics. The results presented here show that there may be no obvious correlation between ARGs and MCR in pig farm wastewater and surrounding rivers, which may be due to various environmental factors, highlighting the urgent need for a comprehensive evaluation of relationship between ARGs abundance and MR.
1. Introduction A large amount of wastewater is continuously produced during the pig production process. Pig farm wastewater contains a large amount of pollutants such as organic matter, suspended solids, ammonia nitrogen, heavy metals and antibiotics (Ben et al., 2017a,b; Ward et al., 2018; Zhi et al., 2018). Most wastewater contaminants are effectively treated with the pig farm wastewater treatment systems (WWTs) process, including solid-liquid separation, aerobic, anaerobic and biochemical treatment (Szögi et al., 2004; Zhi et al., 2018). However, the residual pollutants in wastewater such as ARGs and pathogen, which could not be eliminated by the WWT will be recycled or discharged into surrounding water bodies, which may cause pollution in the surrounding environment (Fang et al., 2018; Resende et al., 2014). Generally, there are a large number of microorganisms in sewage, including pathogenic microorganisms and resistant microorganisms, pose a threat to aquatic organisms and human health. Accordingly, there is increased public concern regarding the potential health impact of pig farms on
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surrounding water. Pig farm wastewater is an important source of antimicrobial resistance genes (ARGs), which are considered a new type of environmental pollutant (Karkman et al., 2018; Pruden et al., 2006; Yuan et al., 2018). Some studies have indicated that wastewater treatment systems can potentially reduce heavy metals, antibiotics, ARGs, mobile genetic components and human pathogenic bacteria (HPBs) in pig farm wastewater (Ben et al., 2017a,b; Munck et al., 2015). However, other studies reported that the abundance of some ARGs remained nearly the same or even increased because WWTs may have had a preference for individual ARGs (Couch et al., 2019; Rafraf et al., 2016). Importantly, residual ARGs in water can travel a long distance with water flow, causing a wide ecological risk in aquatic environments. In addition, water is an essential element for the growth of animals, plants and microorganisms. Through the penetration, drinking and absorption of water, resistance genes can reach organisms and humans, posing a serious threat to health (Purohit et al., 2017). Pig farm wastewater contains various types of ARGs, including
Corresponding author. College of Animal Science, South China Agricultural University, Guangzhou, 510642, China. E-mail address: [email protected] (X. Liao).
https://doi.org/10.1016/j.ecoenv.2019.109452 Received 13 June 2019; Received in revised form 12 July 2019; Accepted 17 July 2019 0147-6513/ © 2019 Elsevier Inc. All rights reserved.
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those resistant to tetracyclines, β-lactams, sulphonamides, macrolides, fluoroquinolones, and multi-drugs, which are different types of antibiotics (Burch et al., 2017; Jia et al., 2017; Li et al., 2016). Different types of ARGs confer microbial resistance to different antibiotics (Alekshun and Levy, 2007; Lin et al., 2015; Nguyen et al., 2014). In a sense, the diversity of ARGs in environment can reflect the resistance of environmental microorganisms. However, the pig farm wastewater environment also contains heavy metals and antibiotics, which affect microbial resistance (Ben et al., 2017a,b; Han et al., 2019; Zhi et al., 2018). Therefore, the relationship between ARGs and microbial resistance (MR) in pig farm wastewater required a more comprehensive evaluation. Among the veterinary antibiotics used in pig farms, tetracyclines, sulphonamides, quinolones and macrolides are the most widely used types of antibiotics, which play an important role in ensuring the healthy growth of pigs. Ciprofloxacin (CIP), streptomycin (STM) and tetracycline hydrochloride (TCH) are the three most commonly used veterinary antibiotics, belonging to the class of quinolones, sulfonamides and tetracyclines. And higher concentrations of CIP, STM and TCH were detected in pig farm wastewater and rivers, which may cause stress on environmental microorganisms and induce the evolution of microbial resistance. Therefore, CIP, STM and TCH were selected to evaluate MR in the pig farm wastewater and surrounding rivers in this study. To address the current gaps in knowledge of the relationship between ARG abundance and MR in the aquatic environment, quantitative real-time PCR (qPCR) was used to detect the concentrations of 23 genes, including 16 S rRNA, 2 integrons and 20 ARGs, in 25 water samples from pig farm wastewater and surrounding rivers. Bacterial communities were investigated by 16 S rRNA gene sequencing. The minimal inhibit concentrations (MICs) of CIP, STM and TCH were also determined to evaluate microbial resistance. The purposes of this study were to characterize the abundance and diversity of ARGs and MR and to identify the relationships between them in pig farm wastewater and surrounding rivers.
antibiotic concentration in wells 1–8 was 512, 256, 128, 64, 32, 16, 8 and 4 μg/mL, respectively. The 9th well was used as a blank control (Luria-Bertani (LB) culture medium containing no antibiotics) and stored at 4 °C in the dark. The water sample was diluted 103 times with 0.9% sodium chloride solution. The 100 μl of the dilution was applied to LB agar plates and cultured for 24 h (37 °C). For each water sample, 100 single colonies were randomly picked from LB agar plates into LB broth. The bacterial solution was incubated at 37 °C for 24 h on a shaker at 140 rpm for MIC determination. There are 100 single bacteria samples per water sample. A total of 2500 single bacteria were tested for MIC. Average MIC = ∑ the MIC to each single bacteria/total number of bacteria. 2.3. DNA extraction and bacterial 16 S rRNA gene sequencing DNA was extracted from the water samples using a DNeasy PowerWater Kit (Qiagen, Germany) according to the manufacturer's instructions. The hypervariable regions (V3-4) of the bacterial 16 S rRNA gene were amplified using the bacteria-specific primers 338 F and 806 R with a barcode. After the reaction, mixed PCR products were purified with a GeneJET™ Gel Extraction Kit (Thermo Scientific). Then, sequencing libraries were generated using an Ion Plus Fragment Library Kit 48 (Thermo Scientific) following the manufacturer's recommendations. Library quality was assessed on a Qubit 2.0 Fluorometer (Thermo Scientific). Finally, the library was sequenced on an Ion S5™ XL platform, and 400 bp/600 bp single-end reads were generated. Single-end reads were assigned to samples based on their unique barcode and truncated by cutting off the barcode and primer sequence. Quality filtering of the raw reads was performed under specific filtering conditions to obtain high-quality clean reads with Cutadapt (V1.9.1, http:// cutadapt.readthedocs.io/en/stable/). The reads were compared with the reference database (Silva database) to detect chimaeric sequences, and the chimaeric sequences were then removed. The clean reads were finally obtained for operational taxonomic unit (OTU) clustering and species annotation. The OTU abundance data were normalized using a standard sequence number corresponding to the sample with the fewest sequences. Subsequent analyses of alpha diversity, beta diversity and function prediction were all performed with these normalized data.
2. Material and methods 2.1. Sample collection
2.4. Quantification of antimicrobial resistance genes Samples were collected from a large-scale pig farm (2000 breeding sows) located in Zhaoqing, China, on October 2018. This farm is a breeding and finishing farm with boars, gilts, pregnant sows and nursery pigs. The wastewater generated at each pig category production step enters an anoxic/oxic (A/O) WWT. The treated wastewater is discharged into nearby rivers. The raw wastewater (W1, 5 samples, influent water of the WWT) and effluent water (W2, 5 samples), upstream river water (W3, 5 samples), domestic wastewater (W4, 5 samples) and downstream river water (W5, 5 samples) samples were collected. The upstream positions and domestic wastewater discharge channel were each approximately 250 m away from the pig farm (Fig. S1). The downstream position was located approximately 400 m downstream of the pig farm. At each sampling site, the replicate samples were collected at least 1 m apart. Each 4 L sample was collected from the surface water (10 cm) and transported on ice to the Animal Ecology Laboratory of South China Agricultural University within 4 h. The samples were filtered through a 0.45-μm membrane to capture and concentrate bacteria (Jia et al., 2017). Then, the membranes were carefully placed in sterile tubes and stored in a −80 °C refrigerator until DNA extraction.
The qPCR was run on a Bio-Rad CFX96 PCR System. In this study, 8 “ARG types” including 20 “ARG subtypes” were analysed. Of the subtypes, 10 were tetracycline resistance genes (tetA, tetC, tetG, tetH, tetL, tetM, tetO, tetQ, tetW and tetX), 2 were sulphonamide resistance genes (sul1 and sul2), 2 were macrolide resistance genes (ermA and ermB), 2 were multi-drug resistance genes (cfr and oqxB), one was an aminoglycoside resistance gene (strB), one was a quinolone resistance gene (qnrS), one was a chloramphenicol resistance gene (cmlA) and one was a β-lactam resistance gene (blaTEM). Furthermore, 16 S rRNA and 2 classes of integrons (intl1 and intl2) were also analysed. After PCR, the products of all genes were subjected to gel electrophoresis and recovered using a Gel Extraction Kit (OMEGA). The recovered product was ligated into the pMD18-T vector and transformed into DH5α competent cells. The plasmid of each bacterial solution was extracted and subjected to PCR detection. The recombinant plasmids that tested positive were selected as the labelling template for calculating the absolute abundance of genes in the water sample. Each reaction (25 μL) contained 12.5 μL of SYBR Premix Ex Taq (Takara), 10.5 μL of ddH2O, 0.5 μL of each primer (10 μM) (Table S1), and 1 μL of sample DNA. The thermal cycle was as follows: initial denaturation at 95 °C for 4 min; 40 cycles of denaturation at 95 °C for 5 s and annealing at 60 °C for 30 s; melting curve analysis at 95 °C for 10 s; annealing at 65 °C for 5 s; and finally, 95 °C for 5 s. The absolute abundance of a sample was calculated as follows: absolute abundance (copies/ mL) = labelling template abundance (copies/μL) × sample DNA
2.2. MIC determination According to standards of the Clinical and Laboratory Standards Institute (CLSI), antibiotic (CIP, STM and TCH) cultures were prepared and added to sterile 96-well polystyrene plates at 100 μL per well. The 2
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water bodies in present study indicated that the wastewater was a source of continuous emission of ARGs into the environment, and wastewater treatment practices should apply improved controls of ARG transmission.
elution volume (μL)/sample volume (mL). 2.5. Data analysis Box plots and bar charts were generated in GraphPad Prism 7.0. Heatmaps were generated in R 3.41. Redundancy analysis (RDA) plots were generated using the Canoco 5.0 software package. Using SPSS 22.0 (IBM, USA), Spearman's correlation coefficient was calculated, and a statistical test was conducted, with r > 0.8 and P < 0.05 indicating significance.
3.2. Variation in the abundance and diversity of ARGs The absolute abundances of ARGs, 16 S rRNA, intl1 and intl2 were determined by qPCR to compare gene pollution among different sampling sites in detail (Fig. 1). In the 25 samples of the five sampling sites, the average abundances of ermA were generally higher than those of other genes, with the highest abundance of 2.41 ± 0.12 × 108 copies/ mL detected in the raw wastewater of pig farm. This result may be due to a large number of ermA resistance genes in pig manure (Hoang et al., 2013; Yin et al., 2017). As a major macrolide resistance gene, ermA is ubiquitous in air, soil, and water (Drudge et al., 2012; Huang et al., 2017). High absolute abundances of tetracycline, sulphonamide, macrolide, multi-drug, aminoglycoside, quinolone, chloramphenicol, and βlactam resistance genes (103–107) were generally detected in the pig farm wastewater and surrounding rivers, probably because they are ubiquitous in aquatic environments (Gupta et al., 2018). These ARGs were also detected at high levels in wastewater from other pig farms (Barkovskii and Bridges, 2012; Birkegård et al., 2018). The highest bacterial abundance was observed in the domestic wastewater, with 9.73 ± 1.93 × 108 copies/mL, followed by raw wastewater of pig farm (8.05 ± 0.79 × 108 copies/mL), downstream river water (4.31 ± 0.27 × 108 copies/mL), effluent water of pig farm (1.56 ± 0.23 × 108 copies/mL), and upstream river water (1.46 ± 0.37 × 108 copies/mL). Integrons are important factors in the horizontal transmission of antibiotic resistance genes (Ravi et al., 2014). The abundance of intl1 and intl2 was also measured. In the current study, the concentration of intl2 in the raw wastewater and in domestic wastewater was the highest (~106 copies/mL), indicating a pressing need to monitor mobile genetic elements in pig farm and domestic wastewater (Agerso and Sandvang, 2005; Fang et al., 2019). In terms of absolute abundance, raw wastewater contained the highest concentration of ARGs, followed by domestic wastewater, the effluent water, the downstream river water and the upstream river water (Fig. 1). The raw wastewater from the pig farm was located at the start of the WWT and was a repository of ARGs (Karkman et al., 2018). After treatment by the WWT, the concentration of ARGs in the pig farm wastewater (W2) was significantly reduced, indicating that the wastewater treatment system effectively removed the ARGs and should be popularized (Ben et al., 2017a,b; Munck et al., 2015). In contrast, the domestic wastewater in this study was directly discharged into the river without treatment, which may be one of the reasons for the significant increase in the concentration of ARGs in the river downstream and urgent need to be our attention. Unexpectedly, there was no consistent pattern of ARG relative abundance among the sampling points. For example, the relative concentrations of the ARGs cmlA, sul1, sul2, tetC and tetG in effluent water were significantly higher than they in raw wastewater (influent water) (Table S2), indicating that the WWT was not effective in reducing the proportion of resistant microorganisms.
3. Results and discussion 3.1. ARGs detected in the water around the pig farm Water samples were collected from pig farm wastewater and surrounding rivers, and the absolute concentrations of 10 tetracycline resistance genes, 2 sulphonamide resistance genes, 2 macrolide resistance genes, 2 multi-drug resistance genes, 1 aminoglycoside resistance gene, 1 quinolone resistance gene, 1 chloramphenicol resistance gene, 1 βlactam resistance gene, 16 S rRNA, and 2 integrons were determined using qPCR (Fig. 1). All 23 genes were detected in water samples of pig farm wastewater and surrounding rivers. ARGs, resistant microorganisms and pathogenic microorganisms can survive in water containing carbon, nitrogen, and metal elements from the environment, such as pig farm wastewater, industrial wastewater, rivers and drinking water, and then be dispersed farther through the flow of water, resulting in more complex pollution (Figueira et al., 2011; Ma et al., 2018; Sun et al., 2016). The presence of ARGs in pig farm wastewater and surrounding
3.3. Correlation between MR and ARGs Water samples were collected from five sampling sites, and MR (in terms of the MIC value) to the antibiotics CIP, STM and TCH in the samples was determined. CIP, STM and TCH belong to the quinolone, aminoglycoside and tetracycline groups of antibiotics, respectively, which correspond to three different types of antibiotic resistance genes. In the 25 samples of the five sampling sites, MR to CIP is the lowest, with the average MIC values all less than 4 μg/mL (Fig. 2AD), probably due to the lower use of ciprofloxacin in the area. Microorganisms were most commonly resistant to STM, with the average MIC value was 10.17 ± 2.00 μg/mL (Fig. 2AB). Moreover, the MR to STM in domestic
Fig. 1. Concentrations of 16 S rRNA, subtypes of ARGs, and integrons in various sampling sites. The grey background indicates the gene concentrations at the five sampling sites, with darker shades indicating a higher concentration. In the box plots, boxes frame the upper and lower quartiles, lines represent the medians, whiskers denote ranges, and “□” symbols represent the means. 3
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Fig. 2. The MR (in terms of the MIC value) to the antibiotics CIP, STM and TCH in various sampling sites. (A) Average value of MIC. The * indicated a significant difference in the average MIC of all sampling points. (B) The percentage of MIC to STM. (C) The percentage of MIC to TCH. (D) The percentage of MIC to CIP.
between the absolute abundance of 23 genes and the MR to TCH and CIP (P > 0.05), including 20 ARGs, 2 integrons and 16 S rRNA (Fig. 3D). Only the absolute abundance of blaTEM, inti1, oqxB, sul1, tetA and tetC, and the relative abundance of oqxB and qnrS were significantly positively correlated with MR to STM (P < 0.05). Others, the relative abundance of cmlA and sul2 were significantly negative correlated with MR to TCH and CIP, respectively. Then, we performed an RDA between environmental factors, ARGs abundance and MR (Fig. 3BCE). We found that the correlation between pH and MR to CIP, and the correlation between NH3–N and TCH was the highest (Fig. 3B). However, there was no significant correlation between pH and quinolones ARGs abundance, and no significant correlation between NH3–N and tetracyclines ARGs abundance (Fig. 3C). The above results indicated that environmental factors may play an important role between MR and ARGs abundance. Total ARGs in water include intracellular ARGs (iARGs) and extracellular ARGs (eARGs)(Zhang et al., 2013). However, eARGs do not confer microbial resistance and the abundance of eARGs is affected by environmental factors such as TN and temperature (Hao et al., 2019). Therefore, we speculated that environmental factors such as pH and NH3–N may be important factors affecting the correlation between MR and ARGs abundance in pig farm wastewater and surrounding rivers. In addition, microbial communities are directly linked to MR and ARGs, so the next step in this study was to analyse the microbial structural diversity in the aquatic environment.
wastewater and raw wastewater of pig farm, which had the worst water quality, was higher than that in the upstream river water, which had the highest water quality, indicating that the pig farm wastewater and domestic wastewater may be the important sources of contamination for STM resistant microorganisms. The MR to TCH in the pig farm wastewater was higher than that in domestic wastewater (Fig. 2AC). This finding may be because tetracycline antibiotics are widely used in the pig industry (Cheng et al., 2019), resulting in increased microbial resistance to pig farm wastewater. Fortunately, the results of this study found that the MR in effluent water was significantly lower than that in raw wastewater from pig farm, indicating that the pig farm WWT significantly controlled the antibiotic resistance pollution of wastewater. Similarly, other WWTs have also been found to remove resistant microorganisms (Guo et al., 2018; Nõlvak et al., 2018; Park et al., 2018). In addition, it is of concern that the upstream river water contained a high proportion of highly resistant bacteria (Fig. 2BCD). The proportion of microorganisms resistant to STM, TCH and CIP (MIC ≥ 64 μg/mL) were as high as 1.5%, 4.0% and 1.3%, respectively. Generally, the upstream river water sample should be the relatively cleanest natural sample among all samples in present study, in addition, previous studies mentioned that there were plenty of resistant bacteria in natural water bodies such as river water and drinking water (Machado and Bordalo, 2014; Figueira et al., 2011), thus the natural aquatic environment may also is a hotbed of resistant bacteria (Mahnert et al., 2019), this phenomenon could be due to that the intrinsic resistome is a naturally occurring, ancient phenotype, present in all bacterial species (Cox and Wright, 2013). ARGs provide microorganisms with the ability to tolerate antibiotic (Blair et al., 2014), so many studies assess the risk of environmental contamination with antibiotic-resistant microorganisms by measuring the abundance of ARGs in the environment. However, the significant correlation between the abundance of ARGs and MR has yet to be systematically investigated. This gap in knowledge led us to question the correlation between ARG abundance and MCR. In present study, the results of correlation analysis of MR and ARG abundance are shown in Fig. 3. Contrary to what we expected, there was no significant connection between the quinolone ARGs and the MR to CIP, and no significant connection between the abundance of tetracycline ARGs and the MR to TCH (Fig. 3A). And there was no significant correlation
3.4. Correlation between bacterial resistance and bacterial composition The composition of bacteria, including 8 potential HPBs (Gao et al., 2018; Hong et al., 2012), from 5 different sampling sites was analysed (Fig. 4). According to the OTUs and Shannon index, bacterial richness was highest in the downstream river water, while bacterial diversity was highest in the effluent water of WWT. The 5 dominant taxonomic groups (Fig. S2) in different sampling sites were commonly Acinetobacter, Clostridiales, Rhodocyclaceae, Bacteroides and Arcobacter. Acinetobacter and Rhodocyclaceae were the most abundant groups in the polluted water (W1 and W4) and surrounding water (W2, W3 and W5), respectively. Acinetobacter is known to contain resistant bacteria and HPBs (Camargo and Bruder-Nascimento, 2013). The relative abundance of Acinetobacter was higher in the raw wastewater and in domestic 4
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Fig. 3. Correlation between ARGs abundance and MR. (A) Redundancy analysis (RDA) of ARGs and MR. The red arrows represent the MR to ciprofloxacin streptomycin and tetracycline hydrochloride. (B) RDA of environmental factors and MR. (C) RDA of environmental factors and ARGs. (D) Heatmap of the correlation between ARG abundance and MCR. Red indicates significant positive correlation (r > 0.8, P < 0.05), dark blue indicates not significant, and yellow indicates significant negative correlation (r < −0.8, P < 0.05). CIP, STM and TCH represent the MCR to ciprofloxacin streptomycin and tetracycline hydrochloride. Tetracycline, sulphonamide, macrolide, multi-drug, aminoglycoside, quinolone, chloramphenicol, and β-lactam, represent the corresponding ARGs.
Fig. 4. Diversity of bacteria and relative abundance of potential HPBs. (AB) OTUs and Shannon index of bacteria. (C) Relative abundances of potential HPBs (log value). (D) Microbial functional diversity of the 25 samples. 5
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4. Conclusion This study demonstrated that the pig farm was an important source of ARGs in surrounding rivers and the ARGs could travel far with the flow of water, which may pose a public health risk. The pig farm WWT could effectively reduce the absolute abundance of ARGs and the MR to CIP, STM and TCH, but it could not effectively reduce the relative abundance of certain ARGs, such as cmlA, sul1, sul2, tetC and tetG. In pig farm wastewater and surrounding rivers, there was no significant correlation between ARG abundance and MR, a large difference between the types of ARGs co-occur bacteria and resistance co-occur bacteria in the pig farm wastewater and surrounding rivers, which may be due to differences in various environmental factors. In future research, the effects of the environmental factors such as nitrogen sources, carbon sources, and heavy metals on the ARG and MR in the environment need to be focused on. Considering that microbes vary among areas with different seasonal climates, to comprehensively understand the relationship between environmental ARGs and environmental microbial resistance, further detailed studies should be conducted in more environments with different climates in more regions, even on a global scale. Of course, current methods for calculating microbial resistance are limited to culturable microorganisms, requiring an application of a method suitable for all microorganisms.
Fig. 5. Relationship between the types of ARGs co-occur bacteria and resistance co-occur bacteria. CIP, STM and TCH represent the resistance of bacteria to ciprofloxacin streptomycin and tetracycline hydrochloride. Red indicates a significant positive correlation (P < 0.05), dark blue indicates not significant, and yellow indicates a significant negative correlation (P < 0.05).
wastewater than in the other samples (Fig. 3C), while there was no significant difference between the other samples (P < 0.01). However, the other 7 HPBs did not have the same distribution pattern as Acinetobacter. For example, the relative abundance of Arcobacter was higher in the downstream river water and the effluent water of WWT than in the raw wastewater of the pig farm. Although wastewater treatment is considered an effective way to reduce HPBs, no decreases in the abundance of the 9 potential HPBs were detected in the water from the pig farm and surrounding areas. In the functional analysis of microbial communities (Fig. 4D), the abundance of the microbes related to metabolic diseases in pig farm wastewater (W1) was higher than that in domestic wastewater and the river areas (W3, W4 and W5), and even after wastewater treatment, the abundance of the microbes related to metabolic diseases had not changed obviously (W2). In contrast, the abundance of microbes associated with infectious diseases, cancer and neurodegenerative diseases in the effluent water and in the river water sample was higher than that in the raw wastewater of pig farm. Both of these worrying results indicated that the natural waters in the area contained many HPBs and that the treatment system was not effective for certain HPBs. Similarly, HPBs have also been detected in other natural waters (Jurado et al., 2002; Mannapperuma et al., 2013), indicating the need to pay attention to the safety and health of aquatic environments. In general, ARGs and antibiotic resistance do not directly affect human health, but resistant HPBs can seriously threaten human health (Peterson and Kaur, 2018; Debabov, 2013). For example, Acinetobacter that has acquired the ARG Omp33-36 exhibits high resistance to colistin, resulting in enhanced pathogenicity. Correlation analysis was performed on the microbial composition and ARG abundance at the five sampling sites (Fig. 5). Among the 9 HPBs, Mycobacterium was significantly negatively correlated with 10 drug resistance genes (cfr, cmlA, ermA, ermB, tetH, tetL, tetO, tetQ, tetW and tetX), and the abundance of the other 8 HPBs did not show a significant correlation with the abundance of ARGs. In the current study, there was no direct evidence that HPBs contain ARGs or for a direct relationship between the ARGs of bacteria and resistance of bacteria. The abundances of bacteria such as Tissierella, Desulfovibrio and Terrisporobacter showed a significant positive correlation with the abundance of more ARGs, but they did not show a significant correlation with the three types of antibiotic resistance, which further indicated that there may be no significant correlation between the abundance of some ARGs and MR in the water samples.
Conflicts of interest The authors have no conflicts of interest to declare. Acknowledgements The study was financially supported by the Research on Key Technologies for Deep Treatment of Effluent by Covered Lagoon (Wens Group, h2015226) and Guangdong Technological Innovation Strategy of Special Funds (Key Areas of Research and hDevelopment Program, Grant No. 2018B020205003). Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.ecoenv.2019.109452. References Agerso, Y., Sandvang, D., 2005. Class 1 integrons and tetracycline resistance genes in Alcaligenes, Arthrobacter, and Pseudomonas spp. isolated from pigsties and manured soil. Appl. Environ. Microbiol. 71, 7941–7947. Alekshun, M.N., Levy, S.B., 2007. Molecular mechanisms of antibacterial multidrug resistance. Cell 128, 1037–1050. Barkovskii, A.L., Bridges, C., 2012. Persistence and profiles of tetracycline resistance genes in swine farms and impact of operational practices on their occurrence in farms' vicinities. Water Air Soil Pollut. 223, 49–62. Ben, W., et al., 2017a. Dissemination of antibiotic resistance genes and their potential removal by on-farm treatment processes in nine swine feedlots in Shandong Province, China. Chemosphere 167, 262–268. Ben, W., et al., 2017b. Distribution of antibiotic resistance in the effluents of ten municipal wastewater treatment plants in China and the effect of treatment processes. Chemosphere 172, 392–398. Birkegård, A.C., et al., 2018. Persistence of antimicrobial resistance genes from sows to finisher pigs. Prev. Vet. Med. 149, 10–14. Blair, J.M.A., et al., 2014. Molecular mechanisms of antibiotic resistance. Nat. Rev. Microbiol. 13, 42–51. Burch, T.R., et al., 2017. Effect of different treatment Technologies on the fate of antibiotic resistance genes and class 1 integrons when residual municipal wastewater solids are applied to soil. Environ. Sci. Technol. 51, 14225–14232. Camargo, C.H., Bruder-Nascimento, A., 2013. Defining resistance in Acinetobacter calcoaceticus-Acinetobacter baumannii complex strains. Antimicrob. Agents Chemother. 57 2442-2442. Cheng, D., et al., 2019. Dynamics of oxytetracycline, sulfamerazine, and ciprofloxacin and related antibiotic resistance genes during swine manure composting. J. Environ. Manag. 230, 102–109. Couch, M., et al., 2019. Abundances of tetracycline resistance genes and tetracycline
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The correlation between antibiotic resistance gene abundance and microbial community resistance in pig farm wastewater and surrounding rivers
T
Yiwen Yanga, Zixiao Liua, Sicheng Xinga, Xindi Liaoa,b,c,* a
College of Animal Science, South China Agricultural University, Guangzhou, 510642, China Key Laboratory of Tropical Agricultural Environment, Ministry of Agriculture, South China Agricultural University, Guangzhou, 510642, China c Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agriculture University, Guangzhou, 510642, China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Antimicrobial resistance gene (ARG) Microbial resistance (MR) Wastewater Pig farm
Antimicrobial resistance gene (ARG) abundance and microbial resistance (MR) are often used as important indicators of pollution risk; however, the relationship between ARGs abundance and MR in pig farm wastewater remains unknown. In this study, the raw pig farm wastewater, effluent water, upstream river water, domestic wastewater and downstream river water samples were collected. The concentration of 20 subtypes of ARGs and 2 integrons, minimal inhibit concentration (MIC), and bacterial communities were investigated. In this study, 20 subtypes of ARGs and integrons were detected in all sampling sites. The highest abundance of 17 of the 20 subtypes of ARGs was detected in raw pig farm wastewater, and ermA had the maximum average abundance of 108 copies/mL, with up to 2.41 ± 0.12 × 108 copies/mL. There was no significant correlation between MR to three antibiotics (ciprofloxacin, streptomycin and tetracycline hydrochloride) and the abundance of their corresponding ARGs (P > 0.05), and a large difference was detected between the types of ARGs co-occur bacteria and resistance co-occur bacteria in the 5 sampling sites. And the pig farm wastewater treatment (WWT) could effectively reduce the ARGs and MR to the 3 antibiotics. The results presented here show that there may be no obvious correlation between ARGs and MCR in pig farm wastewater and surrounding rivers, which may be due to various environmental factors, highlighting the urgent need for a comprehensive evaluation of relationship between ARGs abundance and MR.
1. Introduction A large amount of wastewater is continuously produced during the pig production process. Pig farm wastewater contains a large amount of pollutants such as organic matter, suspended solids, ammonia nitrogen, heavy metals and antibiotics (Ben et al., 2017a,b; Ward et al., 2018; Zhi et al., 2018). Most wastewater contaminants are effectively treated with the pig farm wastewater treatment systems (WWTs) process, including solid-liquid separation, aerobic, anaerobic and biochemical treatment (Szögi et al., 2004; Zhi et al., 2018). However, the residual pollutants in wastewater such as ARGs and pathogen, which could not be eliminated by the WWT will be recycled or discharged into surrounding water bodies, which may cause pollution in the surrounding environment (Fang et al., 2018; Resende et al., 2014). Generally, there are a large number of microorganisms in sewage, including pathogenic microorganisms and resistant microorganisms, pose a threat to aquatic organisms and human health. Accordingly, there is increased public concern regarding the potential health impact of pig farms on
*
surrounding water. Pig farm wastewater is an important source of antimicrobial resistance genes (ARGs), which are considered a new type of environmental pollutant (Karkman et al., 2018; Pruden et al., 2006; Yuan et al., 2018). Some studies have indicated that wastewater treatment systems can potentially reduce heavy metals, antibiotics, ARGs, mobile genetic components and human pathogenic bacteria (HPBs) in pig farm wastewater (Ben et al., 2017a,b; Munck et al., 2015). However, other studies reported that the abundance of some ARGs remained nearly the same or even increased because WWTs may have had a preference for individual ARGs (Couch et al., 2019; Rafraf et al., 2016). Importantly, residual ARGs in water can travel a long distance with water flow, causing a wide ecological risk in aquatic environments. In addition, water is an essential element for the growth of animals, plants and microorganisms. Through the penetration, drinking and absorption of water, resistance genes can reach organisms and humans, posing a serious threat to health (Purohit et al., 2017). Pig farm wastewater contains various types of ARGs, including
Corresponding author. College of Animal Science, South China Agricultural University, Guangzhou, 510642, China. E-mail address: [email protected] (X. Liao).
https://doi.org/10.1016/j.ecoenv.2019.109452 Received 13 June 2019; Received in revised form 12 July 2019; Accepted 17 July 2019 0147-6513/ © 2019 Elsevier Inc. All rights reserved.
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those resistant to tetracyclines, β-lactams, sulphonamides, macrolides, fluoroquinolones, and multi-drugs, which are different types of antibiotics (Burch et al., 2017; Jia et al., 2017; Li et al., 2016). Different types of ARGs confer microbial resistance to different antibiotics (Alekshun and Levy, 2007; Lin et al., 2015; Nguyen et al., 2014). In a sense, the diversity of ARGs in environment can reflect the resistance of environmental microorganisms. However, the pig farm wastewater environment also contains heavy metals and antibiotics, which affect microbial resistance (Ben et al., 2017a,b; Han et al., 2019; Zhi et al., 2018). Therefore, the relationship between ARGs and microbial resistance (MR) in pig farm wastewater required a more comprehensive evaluation. Among the veterinary antibiotics used in pig farms, tetracyclines, sulphonamides, quinolones and macrolides are the most widely used types of antibiotics, which play an important role in ensuring the healthy growth of pigs. Ciprofloxacin (CIP), streptomycin (STM) and tetracycline hydrochloride (TCH) are the three most commonly used veterinary antibiotics, belonging to the class of quinolones, sulfonamides and tetracyclines. And higher concentrations of CIP, STM and TCH were detected in pig farm wastewater and rivers, which may cause stress on environmental microorganisms and induce the evolution of microbial resistance. Therefore, CIP, STM and TCH were selected to evaluate MR in the pig farm wastewater and surrounding rivers in this study. To address the current gaps in knowledge of the relationship between ARG abundance and MR in the aquatic environment, quantitative real-time PCR (qPCR) was used to detect the concentrations of 23 genes, including 16 S rRNA, 2 integrons and 20 ARGs, in 25 water samples from pig farm wastewater and surrounding rivers. Bacterial communities were investigated by 16 S rRNA gene sequencing. The minimal inhibit concentrations (MICs) of CIP, STM and TCH were also determined to evaluate microbial resistance. The purposes of this study were to characterize the abundance and diversity of ARGs and MR and to identify the relationships between them in pig farm wastewater and surrounding rivers.
antibiotic concentration in wells 1–8 was 512, 256, 128, 64, 32, 16, 8 and 4 μg/mL, respectively. The 9th well was used as a blank control (Luria-Bertani (LB) culture medium containing no antibiotics) and stored at 4 °C in the dark. The water sample was diluted 103 times with 0.9% sodium chloride solution. The 100 μl of the dilution was applied to LB agar plates and cultured for 24 h (37 °C). For each water sample, 100 single colonies were randomly picked from LB agar plates into LB broth. The bacterial solution was incubated at 37 °C for 24 h on a shaker at 140 rpm for MIC determination. There are 100 single bacteria samples per water sample. A total of 2500 single bacteria were tested for MIC. Average MIC = ∑ the MIC to each single bacteria/total number of bacteria. 2.3. DNA extraction and bacterial 16 S rRNA gene sequencing DNA was extracted from the water samples using a DNeasy PowerWater Kit (Qiagen, Germany) according to the manufacturer's instructions. The hypervariable regions (V3-4) of the bacterial 16 S rRNA gene were amplified using the bacteria-specific primers 338 F and 806 R with a barcode. After the reaction, mixed PCR products were purified with a GeneJET™ Gel Extraction Kit (Thermo Scientific). Then, sequencing libraries were generated using an Ion Plus Fragment Library Kit 48 (Thermo Scientific) following the manufacturer's recommendations. Library quality was assessed on a Qubit 2.0 Fluorometer (Thermo Scientific). Finally, the library was sequenced on an Ion S5™ XL platform, and 400 bp/600 bp single-end reads were generated. Single-end reads were assigned to samples based on their unique barcode and truncated by cutting off the barcode and primer sequence. Quality filtering of the raw reads was performed under specific filtering conditions to obtain high-quality clean reads with Cutadapt (V1.9.1, http:// cutadapt.readthedocs.io/en/stable/). The reads were compared with the reference database (Silva database) to detect chimaeric sequences, and the chimaeric sequences were then removed. The clean reads were finally obtained for operational taxonomic unit (OTU) clustering and species annotation. The OTU abundance data were normalized using a standard sequence number corresponding to the sample with the fewest sequences. Subsequent analyses of alpha diversity, beta diversity and function prediction were all performed with these normalized data.
2. Material and methods 2.1. Sample collection
2.4. Quantification of antimicrobial resistance genes Samples were collected from a large-scale pig farm (2000 breeding sows) located in Zhaoqing, China, on October 2018. This farm is a breeding and finishing farm with boars, gilts, pregnant sows and nursery pigs. The wastewater generated at each pig category production step enters an anoxic/oxic (A/O) WWT. The treated wastewater is discharged into nearby rivers. The raw wastewater (W1, 5 samples, influent water of the WWT) and effluent water (W2, 5 samples), upstream river water (W3, 5 samples), domestic wastewater (W4, 5 samples) and downstream river water (W5, 5 samples) samples were collected. The upstream positions and domestic wastewater discharge channel were each approximately 250 m away from the pig farm (Fig. S1). The downstream position was located approximately 400 m downstream of the pig farm. At each sampling site, the replicate samples were collected at least 1 m apart. Each 4 L sample was collected from the surface water (10 cm) and transported on ice to the Animal Ecology Laboratory of South China Agricultural University within 4 h. The samples were filtered through a 0.45-μm membrane to capture and concentrate bacteria (Jia et al., 2017). Then, the membranes were carefully placed in sterile tubes and stored in a −80 °C refrigerator until DNA extraction.
The qPCR was run on a Bio-Rad CFX96 PCR System. In this study, 8 “ARG types” including 20 “ARG subtypes” were analysed. Of the subtypes, 10 were tetracycline resistance genes (tetA, tetC, tetG, tetH, tetL, tetM, tetO, tetQ, tetW and tetX), 2 were sulphonamide resistance genes (sul1 and sul2), 2 were macrolide resistance genes (ermA and ermB), 2 were multi-drug resistance genes (cfr and oqxB), one was an aminoglycoside resistance gene (strB), one was a quinolone resistance gene (qnrS), one was a chloramphenicol resistance gene (cmlA) and one was a β-lactam resistance gene (blaTEM). Furthermore, 16 S rRNA and 2 classes of integrons (intl1 and intl2) were also analysed. After PCR, the products of all genes were subjected to gel electrophoresis and recovered using a Gel Extraction Kit (OMEGA). The recovered product was ligated into the pMD18-T vector and transformed into DH5α competent cells. The plasmid of each bacterial solution was extracted and subjected to PCR detection. The recombinant plasmids that tested positive were selected as the labelling template for calculating the absolute abundance of genes in the water sample. Each reaction (25 μL) contained 12.5 μL of SYBR Premix Ex Taq (Takara), 10.5 μL of ddH2O, 0.5 μL of each primer (10 μM) (Table S1), and 1 μL of sample DNA. The thermal cycle was as follows: initial denaturation at 95 °C for 4 min; 40 cycles of denaturation at 95 °C for 5 s and annealing at 60 °C for 30 s; melting curve analysis at 95 °C for 10 s; annealing at 65 °C for 5 s; and finally, 95 °C for 5 s. The absolute abundance of a sample was calculated as follows: absolute abundance (copies/ mL) = labelling template abundance (copies/μL) × sample DNA
2.2. MIC determination According to standards of the Clinical and Laboratory Standards Institute (CLSI), antibiotic (CIP, STM and TCH) cultures were prepared and added to sterile 96-well polystyrene plates at 100 μL per well. The 2
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water bodies in present study indicated that the wastewater was a source of continuous emission of ARGs into the environment, and wastewater treatment practices should apply improved controls of ARG transmission.
elution volume (μL)/sample volume (mL). 2.5. Data analysis Box plots and bar charts were generated in GraphPad Prism 7.0. Heatmaps were generated in R 3.41. Redundancy analysis (RDA) plots were generated using the Canoco 5.0 software package. Using SPSS 22.0 (IBM, USA), Spearman's correlation coefficient was calculated, and a statistical test was conducted, with r > 0.8 and P < 0.05 indicating significance.
3.2. Variation in the abundance and diversity of ARGs The absolute abundances of ARGs, 16 S rRNA, intl1 and intl2 were determined by qPCR to compare gene pollution among different sampling sites in detail (Fig. 1). In the 25 samples of the five sampling sites, the average abundances of ermA were generally higher than those of other genes, with the highest abundance of 2.41 ± 0.12 × 108 copies/ mL detected in the raw wastewater of pig farm. This result may be due to a large number of ermA resistance genes in pig manure (Hoang et al., 2013; Yin et al., 2017). As a major macrolide resistance gene, ermA is ubiquitous in air, soil, and water (Drudge et al., 2012; Huang et al., 2017). High absolute abundances of tetracycline, sulphonamide, macrolide, multi-drug, aminoglycoside, quinolone, chloramphenicol, and βlactam resistance genes (103–107) were generally detected in the pig farm wastewater and surrounding rivers, probably because they are ubiquitous in aquatic environments (Gupta et al., 2018). These ARGs were also detected at high levels in wastewater from other pig farms (Barkovskii and Bridges, 2012; Birkegård et al., 2018). The highest bacterial abundance was observed in the domestic wastewater, with 9.73 ± 1.93 × 108 copies/mL, followed by raw wastewater of pig farm (8.05 ± 0.79 × 108 copies/mL), downstream river water (4.31 ± 0.27 × 108 copies/mL), effluent water of pig farm (1.56 ± 0.23 × 108 copies/mL), and upstream river water (1.46 ± 0.37 × 108 copies/mL). Integrons are important factors in the horizontal transmission of antibiotic resistance genes (Ravi et al., 2014). The abundance of intl1 and intl2 was also measured. In the current study, the concentration of intl2 in the raw wastewater and in domestic wastewater was the highest (~106 copies/mL), indicating a pressing need to monitor mobile genetic elements in pig farm and domestic wastewater (Agerso and Sandvang, 2005; Fang et al., 2019). In terms of absolute abundance, raw wastewater contained the highest concentration of ARGs, followed by domestic wastewater, the effluent water, the downstream river water and the upstream river water (Fig. 1). The raw wastewater from the pig farm was located at the start of the WWT and was a repository of ARGs (Karkman et al., 2018). After treatment by the WWT, the concentration of ARGs in the pig farm wastewater (W2) was significantly reduced, indicating that the wastewater treatment system effectively removed the ARGs and should be popularized (Ben et al., 2017a,b; Munck et al., 2015). In contrast, the domestic wastewater in this study was directly discharged into the river without treatment, which may be one of the reasons for the significant increase in the concentration of ARGs in the river downstream and urgent need to be our attention. Unexpectedly, there was no consistent pattern of ARG relative abundance among the sampling points. For example, the relative concentrations of the ARGs cmlA, sul1, sul2, tetC and tetG in effluent water were significantly higher than they in raw wastewater (influent water) (Table S2), indicating that the WWT was not effective in reducing the proportion of resistant microorganisms.
3. Results and discussion 3.1. ARGs detected in the water around the pig farm Water samples were collected from pig farm wastewater and surrounding rivers, and the absolute concentrations of 10 tetracycline resistance genes, 2 sulphonamide resistance genes, 2 macrolide resistance genes, 2 multi-drug resistance genes, 1 aminoglycoside resistance gene, 1 quinolone resistance gene, 1 chloramphenicol resistance gene, 1 βlactam resistance gene, 16 S rRNA, and 2 integrons were determined using qPCR (Fig. 1). All 23 genes were detected in water samples of pig farm wastewater and surrounding rivers. ARGs, resistant microorganisms and pathogenic microorganisms can survive in water containing carbon, nitrogen, and metal elements from the environment, such as pig farm wastewater, industrial wastewater, rivers and drinking water, and then be dispersed farther through the flow of water, resulting in more complex pollution (Figueira et al., 2011; Ma et al., 2018; Sun et al., 2016). The presence of ARGs in pig farm wastewater and surrounding
3.3. Correlation between MR and ARGs Water samples were collected from five sampling sites, and MR (in terms of the MIC value) to the antibiotics CIP, STM and TCH in the samples was determined. CIP, STM and TCH belong to the quinolone, aminoglycoside and tetracycline groups of antibiotics, respectively, which correspond to three different types of antibiotic resistance genes. In the 25 samples of the five sampling sites, MR to CIP is the lowest, with the average MIC values all less than 4 μg/mL (Fig. 2AD), probably due to the lower use of ciprofloxacin in the area. Microorganisms were most commonly resistant to STM, with the average MIC value was 10.17 ± 2.00 μg/mL (Fig. 2AB). Moreover, the MR to STM in domestic
Fig. 1. Concentrations of 16 S rRNA, subtypes of ARGs, and integrons in various sampling sites. The grey background indicates the gene concentrations at the five sampling sites, with darker shades indicating a higher concentration. In the box plots, boxes frame the upper and lower quartiles, lines represent the medians, whiskers denote ranges, and “□” symbols represent the means. 3
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Fig. 2. The MR (in terms of the MIC value) to the antibiotics CIP, STM and TCH in various sampling sites. (A) Average value of MIC. The * indicated a significant difference in the average MIC of all sampling points. (B) The percentage of MIC to STM. (C) The percentage of MIC to TCH. (D) The percentage of MIC to CIP.
between the absolute abundance of 23 genes and the MR to TCH and CIP (P > 0.05), including 20 ARGs, 2 integrons and 16 S rRNA (Fig. 3D). Only the absolute abundance of blaTEM, inti1, oqxB, sul1, tetA and tetC, and the relative abundance of oqxB and qnrS were significantly positively correlated with MR to STM (P < 0.05). Others, the relative abundance of cmlA and sul2 were significantly negative correlated with MR to TCH and CIP, respectively. Then, we performed an RDA between environmental factors, ARGs abundance and MR (Fig. 3BCE). We found that the correlation between pH and MR to CIP, and the correlation between NH3–N and TCH was the highest (Fig. 3B). However, there was no significant correlation between pH and quinolones ARGs abundance, and no significant correlation between NH3–N and tetracyclines ARGs abundance (Fig. 3C). The above results indicated that environmental factors may play an important role between MR and ARGs abundance. Total ARGs in water include intracellular ARGs (iARGs) and extracellular ARGs (eARGs)(Zhang et al., 2013). However, eARGs do not confer microbial resistance and the abundance of eARGs is affected by environmental factors such as TN and temperature (Hao et al., 2019). Therefore, we speculated that environmental factors such as pH and NH3–N may be important factors affecting the correlation between MR and ARGs abundance in pig farm wastewater and surrounding rivers. In addition, microbial communities are directly linked to MR and ARGs, so the next step in this study was to analyse the microbial structural diversity in the aquatic environment.
wastewater and raw wastewater of pig farm, which had the worst water quality, was higher than that in the upstream river water, which had the highest water quality, indicating that the pig farm wastewater and domestic wastewater may be the important sources of contamination for STM resistant microorganisms. The MR to TCH in the pig farm wastewater was higher than that in domestic wastewater (Fig. 2AC). This finding may be because tetracycline antibiotics are widely used in the pig industry (Cheng et al., 2019), resulting in increased microbial resistance to pig farm wastewater. Fortunately, the results of this study found that the MR in effluent water was significantly lower than that in raw wastewater from pig farm, indicating that the pig farm WWT significantly controlled the antibiotic resistance pollution of wastewater. Similarly, other WWTs have also been found to remove resistant microorganisms (Guo et al., 2018; Nõlvak et al., 2018; Park et al., 2018). In addition, it is of concern that the upstream river water contained a high proportion of highly resistant bacteria (Fig. 2BCD). The proportion of microorganisms resistant to STM, TCH and CIP (MIC ≥ 64 μg/mL) were as high as 1.5%, 4.0% and 1.3%, respectively. Generally, the upstream river water sample should be the relatively cleanest natural sample among all samples in present study, in addition, previous studies mentioned that there were plenty of resistant bacteria in natural water bodies such as river water and drinking water (Machado and Bordalo, 2014; Figueira et al., 2011), thus the natural aquatic environment may also is a hotbed of resistant bacteria (Mahnert et al., 2019), this phenomenon could be due to that the intrinsic resistome is a naturally occurring, ancient phenotype, present in all bacterial species (Cox and Wright, 2013). ARGs provide microorganisms with the ability to tolerate antibiotic (Blair et al., 2014), so many studies assess the risk of environmental contamination with antibiotic-resistant microorganisms by measuring the abundance of ARGs in the environment. However, the significant correlation between the abundance of ARGs and MR has yet to be systematically investigated. This gap in knowledge led us to question the correlation between ARG abundance and MCR. In present study, the results of correlation analysis of MR and ARG abundance are shown in Fig. 3. Contrary to what we expected, there was no significant connection between the quinolone ARGs and the MR to CIP, and no significant connection between the abundance of tetracycline ARGs and the MR to TCH (Fig. 3A). And there was no significant correlation
3.4. Correlation between bacterial resistance and bacterial composition The composition of bacteria, including 8 potential HPBs (Gao et al., 2018; Hong et al., 2012), from 5 different sampling sites was analysed (Fig. 4). According to the OTUs and Shannon index, bacterial richness was highest in the downstream river water, while bacterial diversity was highest in the effluent water of WWT. The 5 dominant taxonomic groups (Fig. S2) in different sampling sites were commonly Acinetobacter, Clostridiales, Rhodocyclaceae, Bacteroides and Arcobacter. Acinetobacter and Rhodocyclaceae were the most abundant groups in the polluted water (W1 and W4) and surrounding water (W2, W3 and W5), respectively. Acinetobacter is known to contain resistant bacteria and HPBs (Camargo and Bruder-Nascimento, 2013). The relative abundance of Acinetobacter was higher in the raw wastewater and in domestic 4
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Fig. 3. Correlation between ARGs abundance and MR. (A) Redundancy analysis (RDA) of ARGs and MR. The red arrows represent the MR to ciprofloxacin streptomycin and tetracycline hydrochloride. (B) RDA of environmental factors and MR. (C) RDA of environmental factors and ARGs. (D) Heatmap of the correlation between ARG abundance and MCR. Red indicates significant positive correlation (r > 0.8, P < 0.05), dark blue indicates not significant, and yellow indicates significant negative correlation (r < −0.8, P < 0.05). CIP, STM and TCH represent the MCR to ciprofloxacin streptomycin and tetracycline hydrochloride. Tetracycline, sulphonamide, macrolide, multi-drug, aminoglycoside, quinolone, chloramphenicol, and β-lactam, represent the corresponding ARGs.
Fig. 4. Diversity of bacteria and relative abundance of potential HPBs. (AB) OTUs and Shannon index of bacteria. (C) Relative abundances of potential HPBs (log value). (D) Microbial functional diversity of the 25 samples. 5
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4. Conclusion This study demonstrated that the pig farm was an important source of ARGs in surrounding rivers and the ARGs could travel far with the flow of water, which may pose a public health risk. The pig farm WWT could effectively reduce the absolute abundance of ARGs and the MR to CIP, STM and TCH, but it could not effectively reduce the relative abundance of certain ARGs, such as cmlA, sul1, sul2, tetC and tetG. In pig farm wastewater and surrounding rivers, there was no significant correlation between ARG abundance and MR, a large difference between the types of ARGs co-occur bacteria and resistance co-occur bacteria in the pig farm wastewater and surrounding rivers, which may be due to differences in various environmental factors. In future research, the effects of the environmental factors such as nitrogen sources, carbon sources, and heavy metals on the ARG and MR in the environment need to be focused on. Considering that microbes vary among areas with different seasonal climates, to comprehensively understand the relationship between environmental ARGs and environmental microbial resistance, further detailed studies should be conducted in more environments with different climates in more regions, even on a global scale. Of course, current methods for calculating microbial resistance are limited to culturable microorganisms, requiring an application of a method suitable for all microorganisms.
Fig. 5. Relationship between the types of ARGs co-occur bacteria and resistance co-occur bacteria. CIP, STM and TCH represent the resistance of bacteria to ciprofloxacin streptomycin and tetracycline hydrochloride. Red indicates a significant positive correlation (P < 0.05), dark blue indicates not significant, and yellow indicates a significant negative correlation (P < 0.05).
wastewater than in the other samples (Fig. 3C), while there was no significant difference between the other samples (P < 0.01). However, the other 7 HPBs did not have the same distribution pattern as Acinetobacter. For example, the relative abundance of Arcobacter was higher in the downstream river water and the effluent water of WWT than in the raw wastewater of the pig farm. Although wastewater treatment is considered an effective way to reduce HPBs, no decreases in the abundance of the 9 potential HPBs were detected in the water from the pig farm and surrounding areas. In the functional analysis of microbial communities (Fig. 4D), the abundance of the microbes related to metabolic diseases in pig farm wastewater (W1) was higher than that in domestic wastewater and the river areas (W3, W4 and W5), and even after wastewater treatment, the abundance of the microbes related to metabolic diseases had not changed obviously (W2). In contrast, the abundance of microbes associated with infectious diseases, cancer and neurodegenerative diseases in the effluent water and in the river water sample was higher than that in the raw wastewater of pig farm. Both of these worrying results indicated that the natural waters in the area contained many HPBs and that the treatment system was not effective for certain HPBs. Similarly, HPBs have also been detected in other natural waters (Jurado et al., 2002; Mannapperuma et al., 2013), indicating the need to pay attention to the safety and health of aquatic environments. In general, ARGs and antibiotic resistance do not directly affect human health, but resistant HPBs can seriously threaten human health (Peterson and Kaur, 2018; Debabov, 2013). For example, Acinetobacter that has acquired the ARG Omp33-36 exhibits high resistance to colistin, resulting in enhanced pathogenicity. Correlation analysis was performed on the microbial composition and ARG abundance at the five sampling sites (Fig. 5). Among the 9 HPBs, Mycobacterium was significantly negatively correlated with 10 drug resistance genes (cfr, cmlA, ermA, ermB, tetH, tetL, tetO, tetQ, tetW and tetX), and the abundance of the other 8 HPBs did not show a significant correlation with the abundance of ARGs. In the current study, there was no direct evidence that HPBs contain ARGs or for a direct relationship between the ARGs of bacteria and resistance of bacteria. The abundances of bacteria such as Tissierella, Desulfovibrio and Terrisporobacter showed a significant positive correlation with the abundance of more ARGs, but they did not show a significant correlation with the three types of antibiotic resistance, which further indicated that there may be no significant correlation between the abundance of some ARGs and MR in the water samples.
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