Increased occurrence of heavy metals, antibiotics and

Science of the Total Environment 635 (2018) 995–1003

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Increased occurrence of heavy metals, antibiotics and resistance genes in surface soil after long-term application of manure Ting Guo a, Chenlu Lou a, Weiwei Zhai a, Xianjin Tang a, Muhammad Z. Hashmi b, Rabbia Murtaza b, Yong Li a, Xingmei Liu a,⁎, Jianming Xu a a Institute of Soil, Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Zhejiang University, Hangzhou 310058, China b Center for Climate Research and Development, COMSATS Institute of Information Technology, Islamabad Campus, Park Road, Chak Shahzad, Islamabad, Pakistan

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• Metal concentrations in soil with manure application were higher than that without application. • Chlortetracycline was the predominant antibiotic among three tetracyclines, while sulfonamides were not detected. • The abundance of most tet and sul genes of sites with manure application was significantly higher than that of site without application. • The abundance of ARGs positively correlated with soil organic matter, antibiotics, Cu, As and Zn levels.

a r t i c l e

i n f o

Article history: Received 5 January 2018 Received in revised form 10 April 2018 Accepted 14 April 2018 Available online xxxx Editor: J Jay Gan Keywords: Soil Pig manure Heavy metals Antibiotics ARGs Field study

a b s t r a c t The purpose of this study was to investigate the impact of long-term application of pig manure on the accumulation of heavy metals, antibiotics and ARGs in surface soil sampled from the Jiaxing long-term field experimental site with three manure treatments, N-PM (0 kg/ha/y, dw), L-PM (7720 kg/ha/y, dw), and H-PM (11,580 kg/ha/y, dw), in 2013 and 2014. The results showed that most serious metal pollution of Zn and Cu was recorded in all manured samples in both years, and their contents exceeded the soil quality standards. Among the three tetracyclines, chlortetracycline was the predominant antibiotic detected with a range of 3.04–98.03 μg·kg−1 in 2013 and 28.67–344.74 μg·kg−1 in 2014 after long-term pig manure application. Q-PCR results showed that the average accumulation of ribosomal protection protein genes (tetM, tetO, tetQ and tetW) was lower than most of the efflux pump genes (tetA and tetG). The abundance of tet and sul genes of those sites with manure application was significantly higher than that of sites without manure application in both years. Metagenomics analysis of ARGs revealed that the abundance of multidrug resistance genes was the most abundant subtype, followed by fluoroquinolone, bacitracin, sulfonamide and tetracycline. There was a positive correlation between the levels of ARGs; soil organic matter, antibiotics, Cu, As, and Zn levels in both years. These results may shed light on the mechanism underlining the effects of long-term manure application on the occurrence and dissemination of ARGs in surface soil. © 2018 Elsevier B.V. All rights reserved.

1. Introduction ⁎ Corresponding author. E-mail address: [email protected]. (X. Liu).

https://doi.org/10.1016/j.scitotenv.2018.04.194 0048-9697/© 2018 Elsevier B.V. All rights reserved.

A large amount of livestock waste, especially pig manure, was produced every year in China and worldwide due to the emerging livestock

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T. Guo et al. / Science of the Total Environment 635 (2018) 995–1003

industry production (Cheng et al., 2013; Peng et al., 2015). Manure application to agriculture fields is believed to be a good and inexpensive way to treat livestock waste and has been numerously reported in Germany (Heuer et al., 2011), the Netherland (Schmitt et al., 2006) and China (Ji et al., 2012; Cheng et al., 2013; Peng et al., 2015). As manure is a reservoir of resistant bacteria and antibiotic compounds (Ji et al., 2012), its application to agricultural soils is assumed to significantly increase the ARGs and resistant bacterial populations in soil (Fahrenfeld et al., 2014; Peng et al., 2015). A large amount of metals and antibiotics are annually used in concentrated animal feeding operations (CAFOs) worldwide to treat animal diseases and promote animal growth, and for prophylactic, metaphylactic, and therapeutic purposes in animal husbandry (Hamscher et al., 2005). The use of antibiotics has been banned in the European Union since 2006, and recently its use for non-therapeutic purposes in the US has been stopped. However, antibiotics are still used in some parts of China. A previous report revealed that approximately 97,000 tons of antibiotics were used in the animal industry in China (Collignon and Voss, 2015). Most of these pollutants cannot be absorbed by animals and are released in the feces and urine, which then persist and accumulate in soils after manure application, causing a concerned problem (Ji et al., 2012). Surface soil is considered as a pool of residual antibiotics (Negreanu et al., 2012). Tetracyclines and sulfonamides have been frequently measured in soil and manure (Fang et al., 2015; X. Zhang et al., 2016, H. Zhang et al., 2016; Pan and Chu, 2017). Once added to the soil, antibiotics interact with the soil solid phase and are prone to microbial transformation. This biotransformation may result in a retransformation of metabolites into the parent compound similarly as in manure (Foerster et al., 2009; Zarfl et al., 2009). Meanwhile, low and elevated levels of antibiotics in the environment trigger the development of antibiotic-resistant microbial populations (Boxall et al., 2003). These antibiotics may even cause serious allergies or may be toxic to humans at significant concentrations (Kumar et al., 2005). However, surface runoff and particle-facilitated transport may disperse all antibiotics in the environment (Larsbo et al., 2008; Joy et al., 2013; Popova et al., 2013). Zn and Cu were used as feed additives for animal growth promotion through antimicrobial activity mechanisms, and the highest accumulations found in animal manures were 4333.8 and 730.1 mg·kg−1, respectively (Ji et al., 2012). Anjum et al. (2011) detected Cr, Zn, Ni, Fe, Cu and Cd concentrations of 36.2, 42.5, 43.2, 241, 13.3, and 11.20 mg·kg−1, respectively, in cultivated soils. In the research of Ogiyama et al., the reported concentration of Zn and Cu in a manure-amended arable field ranged from 72 mg·kg−1 to 170 mg·kg−1, and 18 mg·kg−1 to 109 mg·kg−1, respectively (Ogiyama et al., 2005). In addition to emerging nontraditional ARGs contaminants, anthropogenic-derived sources of metals represent a major source of contamination in agricultural soils (Stepanauskas et al., 2005). Different from antibiotics, metals are not subject to degradation and can represent a long-term selection pressure. However, most studies of the effects of manure amendment on the occurrence of metals and antibiotics employ the investigation of grab samples or short-term laboratory studies. Meanwhile, most studies have focused on the total heavy metals with less attention to the bioavailable heavy metals. Antibiotic resistance genes (ARGs) are considered as new emerging contaminants (Pruden et al., 2006). The diversity and abundance of various ARGs have been investigated in soils with manure application (Ji et al., 2012; Zhu et al., 2013). Hong and colleagues (Hong et al., 2013) observed an increased abundance of tetracycline resistance genes in soil after pig manure injection, and these genes remained elevated for up to 16 months. Antibiotics can be divided into nine categories, and in general, aminoglycosides, tetracyclines, sulfonamide, florfenicol, and quaternary ammonium compound resistance genes are measured. Sulfonamide and tetracycline resistance genes are frequently detected in soils from many sites (Schmitt et al., 2006; Walsh et al., 2011). Zhou et al. (2017) detected the spatial distribution of eight major genes (tetO, tetQ, tetW, tetM, tetB, tetT, sulI, sulII) in agricultural soil

across China finding that the northeast region of China was a hot spot of sulfonamide resistance genes. tet and sul genes have also been reported as the most frequently detected ARGs in animal manures and livestock lagoons (Ji et al., 2012). The top three ARGs in the fecal samples from Chinese dairy farms are cfxA, tetQ and tetW (Zhou et al., 2016). At least 40 different tetracycline resistance (tet) genes have been characterized (Roberts, 2005), and three mechanisms have been identified: antibiotic efflux pumps, target modification with ribosomal protection protein, and antibiotic inactivation (Lambert, 2005). Instead of ARGs detection through qPCR, several other studies focused on the metagenomic approaches to study ARGs, because it provides detailed insights into ARGs information. Metagenomic techniques have been used to study ARGs for estuary, deep ocean sediments (Chen et al., 2013), activated sludge, coastal sediments (Cai and Zhang, 2013), domestic wastewater (Christgen et al., 2015), and long-term field application of sewage sludge (Chen et al., 2016). Soil becomes the primary sink of metals and antibiotics because some classes of antibiotics, such as tetracyclines (TCs), can be absorbed to soil particles strongly and are resistant to biodegradation (Ji et al., 2012), which may cause the more serious problem of ARGs in soil and the surrounding environment (Pruden et al., 2013). Recently, an increasing number of reports have suggested that the increasing abundance of ARGs may be due to waste releases of heavy metals; there are known links between heavy metals and antibiotic resistance maintenance and proliferation (Stepanauskas et al., 2006; Knapp, 2011; Ji et al., 2012). sulI and sulII are strongly correlated with the levels of Cu, Zn and Hg. Many ARGs are positively correlated with soil Cu levels, with approximately half being highly significant (P b 0.05). Moreover, Cr, Ni, Pb and Fe are also significantly correlated with specific ARGs (Knapp et al., 2011). However, some other studies have demonstrated that the presence of ARGs is relatively independent of their respective antibiotic inducer (Ji et al., 2012). Overall, an understanding of the occurrence of metals, antibiotics, and ARGs in soil in relation to manure application at known rates is important in the development of manure management practices. In the present study, we determined the impact of long-term (9 years) pig manure application on (1) the residual amounts of different heavy metals and classes of antibiotics; (2) the diversity and abundance of ARGs; (3) the correlations between antibiotic residues, ARGs, soil properties, and heavy metals. The results of the study will provide a comprehensive understanding of the ecological risk caused by long-term pig manure application. 2. Materials and method 2.1. General description of long-term field experiment site and sampling The field experimental site in Jiaxing, China (30°50′19.78″ N, 120°43′4.59″ E), operated since 2005, with the cropping system of rice–rape rotation, was selected in the present study. Rice-growing season runs from June to November and rape-growing season runs from November to May of the second year. The soil type is gleyed paddy soil with a weakly alkalescent pH. The mean annual precipitation in this site is 1200 mm and the average annual temperature is 15.7 °C. Three treatments, namely, N-PM, L-PM, and H-PM representing no manure, low amount of manure, and high amount of manure applied, respectively, were arranged in a completely randomized block design, each with three replicates. The manure was applied into the soil surface with a large bucket and a small shovel and was ploughed for incorporation with the soil, similar to the local farmers' procedures. Generally, manure was applied twice a year, and total applied manure was 7720 and 11,580 kg/ha/y dw in the L-PM and H-PM treatments, respectively. Concentrations of heavy metals and antibiotics in the manure are given in Table S3. Detailed information of the soil texture, properties, and manure applications is given in Table 1. Surface soils were collected at a depth of 0–5 cm after crops were harvested in November 2013 and


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Table 1 Soil texture, properties, and manure application at the Jiaxing long-term field experiment site. Treatments

N-PM L-PM H-PM

Manure applied (kg/ha/y, dw)

0 7720 11,580

Mineral fertilization (kg/ha/y)

0 144 N 216 N

pH

SOM (‰)

Soil texture (Sand, %)

Soil texture (silt, %)

Soil texture (clay, %)

2013

2014

2013

2014

2013

2014

2013

2014

2013

2014

6.98 7.51 7.68

6.91 7.67 7.55

46.6 76.9 90

44.5 66.5 73.7

23.4 31.9 31.3

21.0 29.0 30.8

41.7 36.7 35.0

41.5 39.0 39.5

34.9 31.4 33.7

37.5 32.0 29.8

May 2014. Soils were collected from five points between rows in each plot and combined as one sample. All samples were then lyophilized, ground and sieved (b60 mesh), and stored at −20 °C before analysis. 2.2. Heavy metal analysis Seven typical heavy metals (Cd, Pb, Cu, Ni, Zn, As and Cr) in soil were determined. After freeze drying, 0.2 g of sample was digested in a ternary mixture of HF, HNO3 and HClO3 acid in a Teflon crucible. The resulting solutions were diluted to 50 mL in volumetric flasks, filtered, and stored at 4 °C prior to analysis. The bioavailable concentration of heavy metals was carried out using a single step extraction procedure of DTPA extraction, while bioavailable As was extracted by 0.5 M NaH2PO4. The DTPA-TEA extraction solution consisted of 0.005 M DTPA with 0.01 M CaCl2 and 0.1 M triethanolamine (TEA). The metal concentrations were then quantified by inductively coupled plasma– mass spectroscopy (ICP-MS).

quantified by real-time qPCR. First, total DNA was extracted from fresh soil samples (0.25 g fresh weight) by using the PowerSoil DNA Isolation Kit (MO BIO, Cat.No.12888, USA). The specific primers and references used in this study for qPCR are listed in Table S1 (Ng et al., 2001; Gaze et al., 2011; Luo et al., 2011). Absolute quantification standard curve method was used to quantify the ARGs abundance in the soil samples. PCR reaction solution with a final volume of 25 μL was employed as 2× SYBR 12.5 μL, 5 μM primer F 0.5 μL, 5 μM primer R 0.5 μL, 10 ng/μL DNA template 1.0 μL, and RNase-free water 10.5 μL. ARGs abundance was quantified by qPCR assays with the Applied Biosystems 7500 Fast Real-Time PCR System (ABI, USA). The amplification program was set as follows: initial temperature was 50 °C for 2 min to activate the UNG, and denaturing at 95 °C for 5 min, followed by 40 cycles at 95 °C for 15 s, at annealing temperature for 20 s, 72 °C for 31 s, and a final step for the melting curve for the quantification of ARGs. Standard curve method was used to obtain the copy number of the ARGs, with the amplification efficiency ranging from 88% to 100% and the correlation coefficients (R2) of the standard curves N0.99.

2.3. Antibiotics analysis by LC-MS/MS 2.5. Metagenomics analysis Three tetracyclines (CTC, OTC, TC) and three sulfonamides (SMZ, SMX, SDZ) were extracted and measured similar to our previous study (Tang et al., 2015b). First, 1.0 g of soil was ultrasonically extracted in polypropylene tubes with 5 mL of methanol/EDTA–Mcilvaine buffer (V:V = 1:1, pH = 4.0) for 15 min and centrifuged for 10 min at 4000 rpm. After repeating the extraction for another two times, batches of supernatants were gathered, diluted with ultrapure water (ρ = 18.2 MΩ·cm), and then cleaned up using SPE vacuum manifolds (Supelco, USA). HLB cartridges (60 mg/3 mL, Waters, USA) preconditions were maintained with methanol and ultrapure water successively. Diluents were then passed through cartridges at a flow rate of approximately 1 mL/min. After rinsing with 15 mL of ultrapure water, the cartridges were eluted with 6 mL of methanol containing 0.1% formic acid. The collected effluents were concentrated to 5.0 mL under a gentle N2 stream and stored in glass vials at −20 °C before analysis. Antibiotic concentrations were determined by using an Agilent 6460 Triple Quadrupole LC/MS System (Agilent, USA) with electron spray ionization (ESI) in a multiple reaction monitoring (MRM) mode. A Zorbax XDB C18 column (2.1 mm × 150 mm, 3.5 μm) with a column temperature of 40 °C was used to separate the antibiotics with an injection volume of 10 μL. Water with 0.1% formic acid and acetonitrile were used as mobile phase A and B. With a flow rate of 0.4 mL/min, the flow gradient was adjusted as follows: 0–1.5 min linear gradient from 10% B to 30% B, 1.5–4 min linear gradient to 95% B and maintained for 4 min. The column was re-equilibrated for another 3 min before the next injection. The limits of detection and limits of quantification were also detected. Calculation was conducted as we described previously (Tang et al., 2015b). A standard addition recovery experiment was conducted to evaluate the effectiveness of the antibiotic extraction and measurement method. The recoveries of tetracyclines and sulfonamides were 58.6%–74.5% and 73.0%–91.2% in the soil samples, respectively. 2.4. ARGs analysis by qPCR The 16S ribosomal RNA (16S rRNA) gene, tetracycline genes (tetA, tetG, tetM, tetO, tetQ, tetW), and sulfonamide genes (sulI, sulII) were

The three soil samples with N-PM and H-PM treatments of each year (2013 and 2014) were mixed as a combined sample, and then were sent to Personal bio (Shanghai, China) for metagenomic de novo sequencing, which was performed on a HiSeq2000 platform (Illumina, USA) using a paired-end (2 × 250 bp) sequencing strategy. Raw data were primarily estimated with fastqc. Sequence quality was filtrated with DynamicTrim.pl, and sequences with quality score b15 were truncated for Hiseq platform. Sequences that completed quality filtration were selected, and those with length b75 bp were removed. Raw reads with average quality score of 50 continuous bases above 20, and with length N100 were reserved for Miseq platform. Sequences were screened after quality filtration, with incorporative two terminal sequences for analysis. ARGs data were downloaded from the Antibiotic Resistance Database (ARDB, http://ardb.cbcb.umd. edu/). The redundant sequences were removed using a self-written script. The BLASTX programs were used to align clean reads of acquired data set against the antibiotic resistance genes database (ARDB) (Liu et al., 2009). The metagenomic iTags from each sample were searched against the nonredundant ARDB by using BLASTX with an Evalue b1 × 10−5. An iTag sequence was annotated as an ARG-like sequence when its best hit had ≥90% amino acid identity and an alignment length ≥25 amino acids (75 bp). Fifteen subdatabases were established for the ARG subtypes, wherein multidrug meant the resistance gene versus multiple antibiotics, and others meant resistance genes versus unspecific antibiotics. The 13 other subtypes included aminoglycoside, bacitracin, β-lactam, chloramphenicol, fluoroquinolone, fosmidomycin, lincomycin, macrolide, macrolide-lincomycin-streptogramin (MLS), peptides, sulfonamide, tetracycline, trimethoprim. 2.6. Data statistics analysis All data were analyzed using SPSS version 16.0 and Microsoft Excel. Pearson correlations between the concentrations of antibiotics, ARGs and soil properties were also analyzed. P values b0.05 were considered to be significant.

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Table 2 Heavy metals in surface soil from Jiaxing field experiment sites (0–5 cm, mg/kg dw). Sampling no.

pH

T/B

Zn

Cu

Pb

Cd

Cr

Ni

As

2013

N-PM

6.98

L-PM

7.51

H-PM

7.68

N-PM

6.91

L-PM

7.67

T B T B T B T B T B T B T

131.55 ± 36.88 c 46.32 ± 1.74 B 355.85 ± 35.91 b 117.56 ± 21.13 A 495.10 ± 99.00 a 97.26 ± 8.02 A 113.61 ± 12.52 b 43.49 ± 1.08 B 326.71 ± 38.98 a 115.02 ± 21.77 A 364.71 ± 24.08 a 89.19 ± 6.20 A 250

41.7 ± 11.69 c 27.06 ± 0.91 B 136.74 ± 17.68 b 71.81 ± 6.54 A 194.5 ± 16.25 a 75.98 ± 2.51 A 32.75 ± 5.29 c 26.07 ± 0.56 B 144.08 ± 9.47 b 73.05 ± 7.32 A 184.61 ± 29.68 a 73.23 ± 0.81 A 100

18.15 ± 1.09 b 8.53 ± 0.44 A 20.32 ± 0.57 ab 8.27 ± 1.51 A 19.81 ± 1.21 ab 8.20 ± 0.43 A 27.43 ± 7.27 b 7.72 ± 0.64 A 27.86 ± 1.8 ab 7.33 ± 0.37 A 26.6 ± 1.93 ab 7.64 ± 0.70 A 300

0.25 ± 0.02 c 0.10 ± 0.02C 0.41 ± 0.08 a 0.29 ± 0.04 A 0.36 ± 0.03 ab 0.20 ± 0.03 B 0.18 ± 0.02 b 0.10 ± 0.02C 0.35 ± 0.05 a 0.29 ± 0.04 A 0.25 ± 0.03 b 0.19 ± 0.03 B 0.6

60.94 ± 7.81 a 0.04 ± 0.01A 62.24 ± 4.32 a 0.03 ± 0.00 B 61.78 ± 4.07 a 0.03 ± 0.00 B 99.33 ± 7.44 a 0.03 ± 0.01 A 97.08 ± 7.57 a 0.02 ± 0.00 B 95.49 ± 4.46 a 0.02 ± 0.00 B 300

36.63 ± 5.5 a 0.91 ± 0.16 A 33.47 ± 5.03 a 1.14 ± 0.18 A 32.31 ± 12.13 a 1.17 ± 0.22 A 37.39 ± 6.77 a 0.91 ± 0.14 A 38.91 ± 2.76 a 1.17 ± 0.18 A 37.89 ± 2.82 a 1.19 ± 0.22 A 50

7.28 ± 0.71 c 1.76 ± 0.28 B 9.65 ± 0.4 b 4.10 ± 0.56 A 11.66 ± 0.51 a 4.49 ± 0.72 A 8.39 ± 0.53 c 1.70 ± 0.41 B 10.15 ± 0.61 b 3.64 ± 0.28 A 12.43 ± 0.66 a 4.40 ± 0.22 A 25

2014

H-PM Soil quality standard

7.55 a

6.5–7.5

T and B are abbreviations of total heavy metals and bioavailable heavy metals, respectively. Values are means ± standard errors of three replications. Values in the same column at the same pollution condition followed by different letters are significantly different (P b 0.05). Small letters indicate significant difference of total heavy metals, and capital letters show significant difference of bioavailable heavy metals among different treatments. a Soil quality standard (Class II) is provided by national environmental quality standard for soils (GB15618–1995).

concentrations of 36.2, 42.5, 43.2, 13.3 and 11.20 mg·kg−1, respectively, in cultivated soils. These results indicate the common presence of heavy metal contamination in manure-amended soil. Moreover, manure application might increase the concentrations of bioavailable heavy metals in soil due to the high concentrations of heavy metals in manure. It has also been found that the application of organic wastes and swine compost could increase the contents of bioavailable metals in the soil (López-Mosquera et al., 2005; Zhao et al., 2006), while Tang et al. (2015a) found that the concentration of available Pb was slightly reduced in soil after application of pig manure.

3. Results and discussion 3.1. Heavy metals Table 2 shows the concentrations of total and bioavailable heavy metals in the soil samples together with soil quality standards of China. The results indicated that Zn, Cu, Pb, Cr and Ni were the most abundant metals found in the soil samples. Among the heavy metals, Zn and Cu were the most significant metal pollutants in Jiaxing field in both years, with the contents exceeding the soil quality standards in L-PM and H-PM treatments, respectively. The H-PM treatment showed the most significant pollution of all metals followed by L-PM in both years, and bioavailable concentrations of most heavy metals were also higher in sites L-PM and H-PM than in N-PM, suggesting that long-term pig manure land application may lead to heavy metal contamination. The concentration of Zn, Cu and As, which are commonly used as feed additives, was increased with increased amount of pig manure applied. The relatively high concentrations of Zn and Cu in animal manures were most likely caused by feed additives; the animals are always fed elevated concentrations of Cu and Zn to promote growth (Cang et al., 2004). The mechanism of growth promotion for these elements is attributed to their antimicrobial activities; as they have similar properties to antibiotics (Ji et al., 2012). In their presence, gut microbiota are altered to reduce fermentation that leads to a loss of nutrients and suppression of gut pathogens. Obviously, elevated Cu and Zn are poorly absorbed by the animal gut and are excreted in the feces. They persist and are accumulated in soils after repeated manure applications. In a survey of 55 samples from typical intensive feedlots of seven provinces or municipalities in China, livestock and poultry manures contained Cu, Zn, Cr, and As in ranges of 1017–1591, 7113–8710, 0–688, and 0.01–65.4 mg·kg−1, respectively (Zhang et al., 2005). Anjum et al. (2011) reported that Cr, Zn, Ni, Cu and Cd were detected at

3.2. Antibiotics Residuals of antibiotics in surface soil from the Jiaxing site are given in Table 3. Among the three TCs, CTC was the most predominant antibiotic, detected with a range of n.d. to 224.97 μg/kg in 2013 and 16.60–460.80 μg/kg in 2014, respectively. However, OTC and TC were not detected in most samples in 2013. TCs were recorded with the highest detection in the H-PM treatment (8.20 μg/kg in 2013 and 29.70 μg/kg in 2014). The residues of CTC, OTC and TC in this studied area were significantly lower compared with previous studies in swine manure, detected in the Shandong Province of China (Pan et al., 2011), farmlands of Pearl River Delta (Li et al., 2011), and north Turkey (Karci and Balcioglu, 2009), but comparable with those in Shanghai (Ji et al., 2012) and some European countries between 2002 and 2005 (CheeSanford et al., 2009). Previously, the concentration of antibiotics was found to be high in manure-amended agricultural soils. (Tamtam et al., 2011; Peng et al., 2015). Furthermore, local differences in soil type, irrigation water source and patterns, and fertilization might influence the distribution and fate of antibiotics over time and space (Knapp et al., 2010). The cropping system of the Jiaxing field experiment site was rice–rape rotation. Soil was sampled during rice-growing season

Table 3 Residuals of antibiotics in surface soil from the Jiaxing field experiment site (μg/kg dw, n = 3). Treatments

2013

2014

CTC

N-PM L-PM H-PM N-PM L-PM H-PM

OTC

TC

Ave.

Range

Ave.

Range

Ave.

Range

3.04 a 64.65 a 98.03 a 28.67 a 304.99 b 344.74 bc

n.d.⁎–9.12 41.77–99.02 25.88–224.97 16.60–39.48 238.10–355.20 247.72–460.80

n.d. n.d. 6.13 19.71 a 30.09 ab 40.27 b

n.d. n.d. n.d.–18.38 n.d.–19.51 17.78–42.22 38.04–41.27

1.14 a 1.61 a 8.20 a 15.46 a 27.32 a 29.70 a

n.d.–3.41 n.d.–4.24 n.d.–22.26 9.18–18.90 15.21–34.58 18.26–40.46

⁎ n.d., not detected. Values in the same column at the same pollution condition followed by different letters are significantly different (P b 0.05).

SMZ

SMX

SDZ

n.d.⁎ n.d. n.d. n.d. n.d. n.d.

n.d. n.d. n.d. n.d. n.d. n.d.

n.d. n.d. n.d. n.d. n.d. n.d.


in 2013, whereas it was sampled during rape-growing season in 2014, possibly resulting in slight differences of the antibiotics distribution in two years. 3.3. Antibiotic resistance genes abundance and diversity We used qPCR to measure the ARGs abundance in the soil samples. The copy number of tetracycline and sulfonamide resistance genes in surface soil detected by qPCR is shown in Fig. 1. The abundance of most tet and sul genes of these sites increased as the application amount of pig manure. Variance analysis revealed that the relative abundance of most tet and sul genes of N-PM was significantly different with L-PM and H-PM treatments (Table S2) in both years. In all sampling points and different time periods, the relative abundance of tet genes varied greatly based on the different resistance mechanisms. The average abundance of ribosomal protection protein (RPP) genes (tetQ, tetM, tetW, and tetO) was lower than most of the efflux pump genes (tetA and tetG). For all tet genes, tetG had the highest relative abundance in all samples in both two years, followed by tetA, tetO, tetM, and tetQ, with tetW having the least relative abundance. tetG has been detected in swine manure (Selvam et al., 2012), waste lagoons (Chee-Sanford et al., 2009), river sediments (Luo et al., 2010), and activated sludge (Auerbach et al., 2007). Zhu et al. (2013) found similar results in their study, wherein tetG showed greater abundance than other tet genes in swine manure. The abundance of the three other RPP genes

Fig. 1. Relative abundance of tetracycline (A) and sulfonamide (B) resistance genes in surface soil of Jiaxing field experiment site in 2013 and 2014.

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could be attributed to their predominance in the gastrointestinal tracts of animals (Aminov et al., 2001) and their close relationship with mobile genetic elements (Chopra and Roberts, 2001; Roberts, 2005). However, the low relative abundance of the efflux pump genes, such as tetA and tetG genes may be attributed to their limited distribution in a comparatively few confined genera (Roberts, 2005; Ghosh et al., 2009; Zhang et al., 2009). Sulfonamide, sulI and sulII, gene copy number followed irregular trends in both years after the long-term application of pig manure, and their copy number increased from N-PM to H-PM treatment. The highest sulI and sulII gene copy number was found in H-PM treatment in both years. The abundance of tet and sul genes of sites L-PM and H-PM was significantly higher than that of site N-PM in 2013, whereas the abundance of tet and sul genes of site H-PM was significantly higher than that of sites N-PM and L-PM in 2014 except tetM and tetO (Table S2). These results confirm that the long-term impact of pig manure, particularly high amount of manure may serve as an important reservoir for tet and sul genes and thus merits considerable attention. We used metagenomics technique to gain full insight into the abundance of ARGs as most of the ARGs were unable to be detected by qPCR. Metagenomics approaches detected many other types of ARGs along with sulfonamide and tetracycline ARGs, and the ARGs exhibited fluctuating trends in abundance and diversity (Fig. 2). Metagenomics study showed that the total ARGs relative abundance of sites L-PM (8.48 ppm) and H-PM (8.38 ppm) in 2013 was almost equal, but both higher than that of site N-PM (4.86 ppm). The total ARGs relative abundance in 2014 for H-PM was 6.20 ppm, 5.14 ppm for L-PM, and 4.14 ppm for N-PM, showing that the total ARGs relative abundance in 2014 increased with the application of pig manure. Among the 15 ARG subtypes in 2013 and 2014, multidrug was the most abundant subtype accounting for 37.09%–49.49%, followed by fluoroquinolone, bacitracin, and sulfonamide, accounting for 11.56%–20.75%, 7.83%– 15.75%, and 0.53%–15.84%, respectively. Meanwhile, the relative abundance of chloramphenicol, tetracycline and other ARGs accounted for 5%, respectively. The relative abundance of multidrug resistance genes gradually increased with increasing manure application, but its proportion in total antibiotic resistance genes gradually decreased. The multidrug resistance genes relative abundance for N-PM treatment during 2013 and 2014 were 2.26 and 2.05 ppm, and increased to 3.56 and 2.30 ppm, with the percentage in total antibiotic resistance genes decreasing from 46.45% and 49.49% to 42.45% and 37.09% respectively. The result suggested that veterinary antibiotics introduced into soil via pig manure selected antibiotic resistance of indigenous microflora in soil specifically, leading to the decrease of the relative proportion of multiple antibiotic resistance genes. X. Zhang et al. (2016) and H. Zhang et al. (2016) also detected elevated tetracycline, macrolide, and multidrug resistance sequences in mobile metagenome studies on aerobic sequence bioreactors. Furthermore, the percentage of fluoroquinolone resistance gene also decreased with increasing amount of pig manure, similar to the decreased percentage from 18.77% (N-PM) to 11.56% (H-PM) in 2013. While the proportion of tetracycline and sulfonamide resistance genes increased significantly with the increasing amount of pig manure. The proportion of sulfonamide resistance genes increased from 7.45% to 15.84% in 2013, and from 0.53% to 12.52% in 2014. Moreover, the proportion of tetracycline resistance gene increased from 7.70% to 16.37% in 2013 and from 0.55% to 13.16% in 2014. These findings were consistent with the increased concentrations of most sulfonamide and tetracycline resistance genes in sites N-PM and L-PM in both years based on qPCR data. The percentage of four other antibiotic resistance genes, aminoglycoside, β-lactam, fosmidomycin, trimethoprim, also increased slightly with increasing amount of pig manure. Given that only three tetracyclines (CTC, OTC, TC) and three sulfonamides (SMZ, SMX, SDZ) were quantified in this study, the concentrations of other kinds of antibiotics were unknown in pig manure. However, studies have shown that pig manure usually contains other

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Fig. 2. Relative abundance of ARGs in surface soil of Jiaxing field experiment site in 2013 and 2014. (A) Relative abundance of ARGs normalized to the total number of reads in the metagenome. (B) Relative percentage of different ARG subtypes in surface soil.

antibiotics, such as trimethoprim (Ho et al., 2012), fluoroquinolones, and macrolides (Huang et al., 2013), having an impact on antibioticresistant microbes when taken into the soil. The antibiotic resistance genes are mostly present in mobile genetic elements, affecting the composition of antibiotic resistance by co-selection or cross-selection. Previous studies on the genetic diversity of antibiotic resistance genes mostly focused on antibiotic resistance genes with known primers. For example, Zhu et al. (2013) analyzed 244 ARGs covering major ARGs through high-capacity qPCR. In recent years, studies on environmental samples by using metagenomics for analysis have increased. Peng et al. (2015) studied cloned library and phylogeny of tetracycline resistance gene tetG in soil added with fresh manure or compost, suggesting that the soil added with fresh manure had higher tetG genotype diversity than that added with compost, but there was no difference compared with the soil with no fertilization. Su et al. (2014) used four pig manure agricultural soils and sediment samples to establish metagenomics library and found that pig manure can increase the diversity of antibiotic resistance genes of soil microbial community. In greenhouse soil, the relative abundance of ARGs also increased with the years of chicken manure application (Fang et al., 2015). 3.4. Correlation analysis Correlation analysis of soil pH, organic matter, antibiotics, ARGs, and heavy metals in Jiaxing is shown in Table 4. Significant positive correlations (P b 0.05) were found between ARGs and soil pH, organic matter, and heavy metals, such as As, Cd, Cu and Zn. Tetracyclines were positively related to heavy metals, which may be attributed to their same source: manure application. Tetracyclines are prone to chelate with

heavy metals (Ji et al., 2012), which can in turn influence the mobility of antibiotics. Notably, similar with the positive correlation of ARGs and total heavy metals, relative abundance of ARGs was also positively correlated with bioavailable heavy metals, such as the significantly positive correlation of tetO and bioavailable As, Cd, Cu and Zn in 2013. The relative abundance of antibiotic resistance genes positively correlated with soil pH, SOM and heavy metals like As, Cd, Cu, Zn. However, there was no significant correlation between ARGs and tetracyclines, which was consistent with the study of Wu et al. (2015). Antibiotic resistance genes versus one antibiotic varies (Christgen et al., 2015); for example, there were N20 tetracycline resistance genes. Thus, the objective tet genes in this study did not happen to correlated with tetracyclines. In addition, antibiotics, which were directly selective factors for ARGs, should have a relationship with ARGs. However, a number of studies showed that other pollutants and environment factors can influence the relative abundance of ARGs. Antibiotics and heavy metals both exhibit synergistic selection pressure of ARGs (Chapman, 2003; Amachawadi et al., 2015). After entering into soil, antibiotics will be degraded, absorbed, or chelated, and their selection pressure to microbes is temporary. The significantly positive relation between heavy metals and ARGs indicated that heavy metal can be the principal factor that enhanced the abundance of ARGs (Ji et al., 2012). In the Haihe basin, the abundance of sulI and sulII was correlated with the concentration of sulfonamides (Luo et al., 2010), possibly because interference effects were less in water environment than in soil, sludge or landfill leachate which, contained more pollutants. Recent data have shown a negative relationship between tet genes and pH (Storteboom et al., 2007). The levels of total tet gene copies were significantly correlated to the total concentrations of tetracyclines


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Table 4 Correlation analysis among soil properties, heavy metals, antibiotics and ARGs in two years from the Jiaxing site. ARGs tetA tetG tetM tetO tetQ tetW sulI sulII Tet (qPCR) Sul (qPCR) Tet(meta) Sul(meta)

2013 2014 2013 2014 2013 2014 2013 2014 2013 2014 2013 2014 2013 2014 2013 2014 2013 2014 2013 2014 2013 2014 2013 2014

pH

SOM

As (T/B)

Cd (T/B)

Cr (T/B)

Cu (T/B)

Ni (T/B)

Pb (T/B)

Zn (T/B)

CTC

OTC

TC

0.67⁎ 0.45 0.6 0.6 0.64 0.38 0.90⁎⁎

0.78⁎⁎ 0.64 0.49 0.73⁎ 0.65 0.57 0.88⁎⁎ 0.70⁎

0.83⁎⁎/0.44 0.67/0.61 0.57/0.74 0.84⁎⁎/0.80 0.73⁎/0.33 0.59/0.66 0.71⁎/0.88⁎

0.6/−0.01 0.21/−0.04 0.38/0.31 0.26/0.02 0.58/0.39 0.17/−0.07 0.85⁎⁎/0.91⁎

0.82⁎⁎/0.57 0.63/0.36 0.55/0.58 0.82⁎⁎/0.56 0.76⁎/0.50 0.59/0.39 0.79⁎/0.89⁎

0.48/0.33 0.12/−0.74 0.07/−0.24 0.63/0.05 0.26/0.01 0.59/−0.25 0.24/−0.02 0.72/0.49 0.32/0.08 0.67⁎/0.54 0.25/0.00 0.72⁎/0.39 0.3/0.05 0.97/1.00⁎

0.02/0.07 0.01/−0.36 0.16/−0.55 0.18/−0.13 0.11/0.03 0.04/−0.32 −0.48/0.12 0.14/−0.41 0.35/0.41 0.05/−0.07 0.15/0.89⁎ 0.09/−0.26 −0.04/0.43 0.17/−0.02 −0.17/0.85⁎ 0.15/−0.19 −0.04/−0.35 0.1/−0.27 −0.15/0.84⁎ 0.16/−0.15 −0.51/0.77 0.89/0.98 −0.97/0.99 0.85/0.96

0.63/−0.39 −0.04/0.83⁎ 0.77⁎/−0.24 −0.29/0.60 0.6/−0.55 −0.11/0.77 0.55/0.18 −0.16/0.75 0.08/−0.38 −0.31/0.55 0.37/−0.59 −0.07/0.76 0.4/−0.26 −0.25/0.52 0.24/−0.36 −0.14/0.66 0.83⁎⁎/−0.20 −0.13/0.74 0.28/−0.37 −0.17/0.63 0.94/−0.70 −0.44/−0.43 0.9/−1.00⁎⁎

0.73⁎⁎/0.36 0.58/−0.06 0.33/0.21 0.75⁎/0.15 0.63/0.38 0.52/−0.04 0.85⁎⁎/0.88⁎⁎

0.53/0.61 0.56/−0.68 0.71⁎/0.68 0.73⁎/−0.33 0.65/0.69 0.91⁎⁎/0.00 0.87⁎⁎/0.80 0.75⁎/0.09 0.78⁎/0.78 0.80⁎/0.89⁎ 0.72⁎/0.71 0.81⁎/0.08 0.81⁎/0.79 0.5/0.76 0.95/1.00⁎⁎

0.25/0.31 −0.32/−0.03 0.4/0.51 −0.34/0.14 0.40/0.40 −0.24/0.00 −0.58/0.31 −0.29/0.26 0.22/0.15 −0.26/−0.07 0.26/0.09 −0.26/0.05 0.18/0.59 −0.27/0.12 0.1/0.34 −0.3/0.16 0.31/0.53 −0.31/0.07 0.12/0.40 −0.29/0.15 0.98/0.11 −0.99/0.83 0.56/0.79 −1.0/0.79

0.39 0.38 0.01 0.59 0.32 0.27 0.73⁎ 0.46 0.08 0.3 0.39 0.36 0.53 0.57 0.69⁎

0.06 0.67⁎ −0.25 0.75⁎ 0.13 0.63 0.53 0.65 0 0.57 0.19 0.64 0.34 0.72⁎

0.06 0.33 −0.13 0.33 0.16 0.14 0.51 0.48 −0.09 0.01 0.11 0.23 0.28 0.29 0.52 0.35 0.1 0.3 0.48 0.33 0.02 0.99 0.73 0.98

0.64 0.12 0.33 0.49 0.48 0.68⁎ 0.55 0.68⁎ 0.6 0.84⁎⁎ 0.52 0.70⁎ 0.59 0.7 0.91 1.00⁎⁎ 0.88

0.37 0.53 0.67⁎ 0.64 0.85⁎⁎ 0.74⁎ 0.78⁎ 0.75⁎ 0.79⁎ 0.68⁎ 0.82⁎⁎ 0.75⁎ 0.65 1.00⁎ 1.00⁎ 0.99

0.97/0.99 0.97/1.00⁎⁎

0.58/0.70 0.86/0.75 0.52/0.65

0.59/0.49 0.5/−0.30 0.68⁎/0.40 0.76⁎/0.23 0.66/0.46 0.89⁎⁎/0.42 0.83⁎⁎/0.57 0.83⁎⁎/0.62 0.79⁎/0.56 0.81⁎⁎/0.80 0.71⁎/0.47 0.87⁎⁎/0.63 0.80⁎⁎/0.57 0.61/0.80 1.00⁎/0.96 0.99/0.99 1.00/0.94

−0.51/−0.36

0.62/0.15 0.44/−0.31 0.55/−0.01 0.73⁎/0.47 0.59/0.05 0.88⁎⁎/0.28 0.74⁎/0.18 0.90⁎⁎/0.81⁎ 0.72⁎/0.15 0.67⁎/0.50 0.65/0.04 0.92⁎⁎/0.78 0.73⁎/0.16 0.58/0.96 0.99/0.80 1.00/−1.00⁎⁎ 0.98/0.75

0.54 0.33 0.45 0.68⁎ 0.55 0.61 0.99 0.99 0.97

0.59 0.74⁎ 0.03 0.70⁎ 0.56 0.74⁎ −0.04 0.97 0.69 0.99

⁎ P b 0.05, correlation is significant. ⁎⁎ P b 0.01, correlation is highly significant. T and B are abbreviations of total heavy metals and bioavailable heavy metals, respectively.

with an r2 value of 0.45 (P b 0.05) in previous studies (Wu et al., 2010). However, some studies observed a strong relationship (Smith et al., 2004). Similar results were also found in Peak et al.'s study, and they hypothesized that the weak correlation was possibly related to different environmental fates and transport mechanisms of resistance genes versus tetracycline after release (Peak et al., 2007). However, Pei and colleagues did not observe a significant correlation between the concentrations of antibiotics and resistance gene numbers in sediment (Pei et al., 2006). Anyway, the results in the present study are consistent with the hypothesis that resistance genes would be positively selected after greater exposure to TCs. The relative abundance of tet and sul genes from metagenomic study was significantly positively correlated with soil properties, such as soil pH, SOM and Cu. However, no significant correlation was observed between ARGs and tetracyclines, which were consistent with the result of qPCR. Compared with the positive correlation between ARGs and heavy metals like As, Cd, Cu, Zn, both tetracycline and sulfonamide resistance genes have a negative, even significantly negative, correlation with the heavy metal Cr in 2014 on the basis of qPCR and metagenomics results. However, some studies observed positive correlation between ARGs and Cr (Wu et al., 2015; X. Zhang et al., 2016, H. Zhang et al., 2016). Heavy metals and antibiotic resistance are frequently combined in the same mobile gene elements, such as plasmid. Hence, the abundance of ARGs might be enhanced by long-standing co-selection pressure imposed by metal. In addition, there was positive correlation between ARGs and bioavailable heavy metals. Previous study also found the positive correlation between ARGs and bioavailable As during composting (Cui et al., 2016). Consequently, when assessing the effect of manure application on the occurrence of ARGs and heavy metals in soil, the bioavailable heavy metal should also be considered since the bioavailable metals might reflect the real co-selection of heavy metals on ARGs.

of pig manure could give rise to serious heavy metal pollution, especially Zn and Cu pollution, and cause residual antibiotics. Among tet genes, the average accumulation of RPP genes (tetQ, tetM, tetW, and tetO) was lower than most of the efflux pump genes (tetA and tetG). The abundance of most tet and sul genes of these sites increased as the application amount of pig manure in both years. Meanwhile, the relative abundance of total ARGs at sites L-PM and H-PM was higher than that at site N-PM and the most abundant subtype was the multidrug. Many ARGs were positively correlated with soil organic matter, antibiotics, Cu, As, and Zn level indicating that the relative abundance of ARGs was influenced by heavy metals and environmental factors. In the future, the application practice of pig manure should be vigilant and the mechanism underlining the effects of long-term manure application on the occurrence and dissemination of ARGs needs in-depth exploration. Acknowledgements This work was supported by the National Science and Technology Support Program of China (2015BAD05B04), Natural Science Foundation of Zhejiang Province (LY16D030001) and National Natural Science Foundation of China (41671312, 41722111). Special thanks are also given to Prof. Xinqiang Liang in College of Environmental and Resource Sciences, Zhejiang University for his assistance during the soil sampling. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.scitotenv.2018.04.194. References

4. Conclusion This study comprehensively demonstrated the impact of long-term application of pig manure on the accumulation of heavy metals, antibiotics and ARGs in soil suggesting that pig manure is a major reservoir, which increases the potential risk of environmental pollution and threatens public health. The results showed that long-term application

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