Environ Sci Pollut Res DOI 10.1007/s11356-014-2685-2

RESEARCH ARTICLE

Soil microbial systems respond differentially to tetracycline, sulfamonomethoxine, and ciprofloxacin entering soil under pot experimental conditions alone and in combination Junwei Ma & Hui Lin & Wanchun Sun & Qiang Wang & Qiaogang Yu & Yuhua Zhao & Jianrong Fu

Received: 30 November 2013 / Accepted: 19 February 2014 # Springer-Verlag Berlin Heidelberg 2014

Abstract This study investigated soil microbial responses to the application of tetracycline (TC), sulfamonomethoxine (SMM), and ciprofloxacin (CIP) alone and in combination in a soil culture pot experiment conducted at Hangzhou, China. Multiple approaches were applied for a better and complete depiction. Among the three antibiotics, SMM has a lowest dissipation and shows a most dramatic inhibition on microbial community and metabolism diversity. The combined application (AM) of SMM, CIP, and TC improved the dissipation of each antibiotic; similarly, SMM- and CIPresistant bacteria showed larger populations in the AM than all single applications. Soils accumulated a large content of NO3–N at day 20 after multi-antibiotics perturbation. All antibiotics stimulated soil basal respirations and inhibited soil metabolism diversity, whereas the interruption exerted by SMM and AM lasted for a longer time. Six nitrogen-cycling genes including chiA, amoA, nifH, nirK, nirS, and narG were quantified and found to decrease owing to both single- and multi-antibiotics perturbation. Overall, AM was most interruptive for soils, followed by SMM perturbation, while other antibiotics could be less interruptive. These results provide systematic insights into how soil microbial systems would shift under each single- or multi-antibiotics perturbation. Responsible editor: Robert Duran Junwei Ma and Hui Lin contributed equally to this work. Electronic supplementary material The online version of this article (doi: 10.1007/s11356-014-2685-2 ) contains supplementary material, which is available to authorized users. J. Ma : H. Lin : W. Sun : Q. Wang : Q. Yu : J. Fu (*) Institute of Environment Resource and Soil Fertilizer, Zhejiang Academy of Agriculture Science, Hangzhou 310021, China e-mail: [email protected] Y. Zhao Institute of Microbiology, College of Life Sciences, Zhejiang University, Hangzhou 310058, China

Keywords Veterinary antibiotics . Soil microbial system . Multiple antibiotics . Antibiotics dissipation . MicroResp . Nitrogen-cycling gene

Introduction Veterinary antibiotics (VAs) are frequently used in livestock farming to promote growth and prevent disease. However, as much as 30–90 % of the antibiotics could not be digested by the animals, which were often excreted as parent compounds or metabolites into environments with manures (Sarmah et al. 2006). Presently, it is not surprising to find elevated concentrations of antibiotics either as metabolite or parent compound in dung and manure and subsequently in agricultural soils. Some reported antibiotic concentrations in manures reach the level of micrograms per kilogram (Kumar et al. 2012; Zhao et al. 2010). VAs entering the agricultural lands via application of animal manures and manure-based fertilizers have been a main source of antibiotics effluence to the surrounding environments (Sarmah et al. 2006). Considering the high release of VAs into soil environments, there has been increasing interest regarding the impacts of antibiotics perturbation with respect to the persistence of antibiotics and their effects on environments. Reproductive effects and adverse impacts on plants have been reported in the presence of antibiotic residues (Liu et al. 2009; Migliore et al. 2003). Environmental effects of antibiotics have been identified on microbial biomass (Thiele-Bruhn and Beck 2005), activity (Kleineidam et al. 2010a; Liu et al. 2009), and community structures (Halling-Sorensen et al. 2002; Hammesfahr et al. 2008; Reichel et al. 2013; Zielezny et al. 2006). By detecting colony forming units (CFUs) in agar plates, Yang et al. (2009) showed that the exposure of wheat rhizosphere soil to different oxytetracycline concentrations highly affected its microbial community structure and soil

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enzymatic activities. In a greenhouse experiment, Ollivier et al. (2010) investigated the impact of sulfadiazine (SDZ)contaminated pig manure on functional microbial communities involved in key processes of the nitrogen (N) cycle in the root–rhizosphere complexes of maize and clover. In addition, development and expansion of antibiotic-resistant bacteria have been studied because of antibiotics contamination (Halling-Sorensen et al. 2002). Environmental fates (i.e., dissipation, degradation, transformation, mobility, sorption, and sequestration) of residual VAs such as sulfamonomethoxine (SMM), SDZ, or chlortetracycline (CTC) in soils after application are also covered in recent investigations (Kumar et al. 2012; Wang et al. 2006). Despite considerable efforts to elucidate the fates and effects of VAs in soil environments, a large knowledge gap still exists. For example, most studies on the effects and dissipation of VAs are difficult to compare, as no two studies were similar in terms of the antibiotics used and the experimental conditions. Studies about antibiotic effects on soil microbial community and function are commonly conducted in dark incubation trials, and responding behaviors of realistic planting soil environments are limitedly reported. Morever, effects of multiple antibiotics, though often present in the natural habitat, have not received enough notice so far. The overall objective of this study was to identify the impacts of typical VAs on the soil microbial structure and function in a soil culture pot experiment. Tetracyclines (TC), sulfonamides, and fluoroquinolones are frequently used in livestock farming; TC, SMM, and ciprofloxacin (CIP) were selected in this work to investigate variations in the antibiotics dissipation, the soil N, the resistance expansion, the soil respiration, and the Ncycling genes after antibiotics perturbation.

Materials and methods Experimental properties and soil sampling The paddy soil, collected from Yangdu Scientific Innovation Base at Haining (China), is classified into loam (42 % sand, 38 % silt, and 20 % clay) and has a water holding capacity of 35 %. The soil was air-dried, and large pieces of plant materials and soil animals were removed by screening through a 6mm sieve. The soil had an organic matter content of 1.29 %, a pH (CaCl2) of 6.31, a EC of 234 μS cm−1 (25 °C) and an available N content of 650 mg kg−1. Pot experiments were conducted in later July with an average day length of about 13.5 h, at Hangzhou, Zhejiang Province, China. For pot experiments, 9.2 kg of air-dried soil (sieved at 6 mm) was filled into each polypropylene container (32 cm×24 cm× 14 cm). After an equilibration phase at 25–30 °C and 50– 65 % maximum water holding capacity of the soil for 10 days, Brassica chinensis seeds were sown per pot. The average

distance between plants was approximately 10 cm. About 2 weeks after germination, antibiotic solutions were applied to the soil surface by watering. TC hydrochloride, CIP hydrochloride, and sodium SMM was directly added in an aqueous solution, respectively; these resulted in TC, CIP, and SMM solutions with a detected antibiotic concentration of 1 g l−1 (HPLC-MS/MS), respectively. Five treatments with different antibiotic applications were established as follows: (1) individual TC test, 100 mg kg−1 TC; (2) individual CIP test, 100 mg kg−1 CIP; (3) individual SMM test, 100 mg kg−1 SMM; (4) combined antibiotics (AM) treatment, 100 mg kg−1 TC+100 mg kg−1 CIP+100 mg kg−1 SMM; and (5) CK, the control soil without antibiotics addition. Water losses were replenished three times per week using a garden sprayer. Bulk soils were sampled from 0 to 10 cm below the soil surface at days 7 and 20 after application of antibiotics using a small spoon. Six sampling sites were distributed between B. chinensis rows within pot and were then fully mixed to form a single sample. All soil samples were airdried and homogenized by sieving to less than 2 mm before the analysis for organic matter and mineral N (NH4, NO3, and NO2). The average aboveground biomass of B. chinensis collected at day 20 was 135.37 g in the CK treatment, 22.91 g in the TC treatment, 78.18 g in the CIP treatment, 7.46 g in the SMM treatment, and 5.12 g in the AM treatment, respectively. Determination of antibiotic concentrations HPLC-MS/MS was applied to determine the concentrations of TC, CIP, and SMM in soils as previously reported (Selvam et al. 2012) but after some modifications. Extraction recoveries for TC, CIP, and SMM from soils were studied and optimized. The mean recovery for TC, CIP, and SMM can be kept at 90, 95, and 102 %, respectively. The extraction and detection procedures were described in detail as follows: TC and CIP were extracted from soil samples using 5 g soil and 25 ml EDTA solution (pH adjusted to 12 before addition), whereas SMM contained in soil samples was extracted using phosphate buffer with pH adjusted to 4.0. All the mixtures were sonic for 30 min before centrifugation. This extraction was repeated once and extracts were then pooled together. Before filtration through a 0.22-μm filter, antibiotic extracts were concentrated using 6 cm3/200 mg Oasis® HLB cartridge (Waters). The concentrated extracts of CIP and SMM were separated using Phenomenex C18 column (3 μm, 2.0 mm× 150 mm) in a Thermo Finnigan Surveyor HPLC system (Thermo Scientific, Waltham, MA, USA), whereas the extract of TC were separated using Endeaorsill C18 column (1.8 μm, 2.1 mm×50 mm). The mobile phase was a mixture of methanol and water+0.1 % formic acid at a ratio of 40:60 (v/v) for the analysis of both TC and SMM, and the flow rate was 0.25 ml min−1. A mixture of methanol and water+0.1 %

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formic acid at a ratio of 50:50 (v/v) was used for CIP analysis with a 5 ml min−1 flow rate. The MS detection was performed with a TSQ Quantum Ultra AM (Thermo Scientific, USA) equipped with an APCI ion source (Ion Max) operated in positive ion mode. Soil respiration measurements by MicroResp™ All CO 2 measurements were performed using the MicroResp™ system following the directions provided by the manufacturer and using subsamples of the same soil samples collected above. MicroResp™ is a colorimetric method based on the color change of a pH indicator dye caused by the release of CO2 by heterotrophic communities. The colorimetric detection plates were prepared according to the report of Campbell et al. (2003) and stored in a desiccator with soda lime until used. Five 96-well plates that contained different antibiotic-treated soils (total of 48 wells per soil) were prepared with detection plates. To determine the community level physiological profiles (CLPP) of soils, 15 CLPP substrates recommended by Campbell et al. (2003) were added into deep well plates with soil samples to measure the substrate-induced respiration (SIR). The substrates used for determination were 30 mg carbon (C)g−1 water in soil of L-cysteine HCl, citric acid, D-fructose, D-galactose, D-glucose, gamma amino butyric acid, L-lysine, L-malic acid, L-arabinose, oxalic acid, or trehalose and 7.5 mg C g−1 water in soil of L-alanine, arginine, N-acetyl-glucosamine, or 3,4-dihydroxybenzoic acid. Directly after the addition of the CLPP substrates (Campbell et al. 2003), the MicroResp™ system was sealed with the detection plate and incubated at 25 °C for 6 h. Preventing gas exchange and water loss from the soils is important during this incubation. When a plate was removed from the deep well setup, the absorbances were determined at 570 nm. SIR was calculated as the difference between micrograms CO2–C produced in soil with the addition of substrate and micrograms CO2–C produced in the same soil with only H2O added. PCA combined with Kaiser normalization was also performed on the 15 elements using SPSS 15. The variables with eigenvalues >1 were selected as principal factors. Analysis of antibiotic-resistant bacteria Antibiotic-resistant bacteria in soils after VAs application were analyzed using agar dilution method after collection. The numbers of TC-resistant bacteria in the TC-amended soil, SMM-resistant bacteria in the SMM-amended soil, and CIPresistant bacteria in the CIP-amended soil were measured. For the soil amended with TC, CIP, and SMM in combination and the control soil without VAs addition, the numbers of all the TC-, SMM-, and CIP-resistant bacteria should be analyzed. The experimental procedure was described in detail as follows: 5 g soil sample was put into 45 ml sterilized water in a

flask and stirred for 30 min. A tenfold serial dilution of the resulting soil suspension was prepared, and the diluted suspensions were subsequently spread on the surface of sterileresistant meat-peptone agars with different concentrations of antibiotics. The plates were incubated at 30 °C until colonies appeared. As the resistance plates containing 50 mg l−1 antibiotic exhibited clear colonies that can be easily counted for all treatments, the resistant plates containing 50 mg l−1 antibiotic are chosen for the determination of the CFUs (CFU g−1 dry soil) of cultivable bacteria. All analyses were repeated in triplicate. Quantification of N-cycling genes by real-time PCR Toxicological effects of VAs on the microbial ecology of N cycling in soil environments were examined through the quantification of six metabolic genes known to have a role in the biochemical cycling of N in soil (Fig. 1). All the functional genes selected have been reported as key Ncycling genes in soils to assess the response of the main components of N cycle to different environmental changes (Kleineidam et al. 2010b; Lindsay et al. 2010; Rotthauwe et al. 1997; Zhang et al. 2013). The target functional genes, their enzymes, and the corresponding primer sets used for real-time PCR (qPCR) are shown in Table 1. In soil ecosystems, chitin is one of the major C and N sources for a majority of microorganisms (Krsek and Wellington 2001). The bacterial chitinase gene chiA has been used as the target gene to assess decomposition–ammonification in several studies (Lindsay et al. 2010; Zhang et al. 2013) though a multitude of pathways exists in this process. Nitrification of soils was evaluated using specific primer set targeting amoA (ammoniaoxidizing bacteria (AOB)-amoA) involved in Proteobacteria, which have been considered most important contributors to ammonia oxidation for almost five decades (Bock and Wanger 2006; Rotthauwe et al. 1997). DNA was extracted from 0.5-g subsamples of soil using the E.Z.N.A.™ soil DNA kit (Omega Bio-tek, Inc., USA). The DNA content in the final extracts was determined with its concentration (nanograms per microliter) measured spectrophotometrically using a NanoDrop® ND-1000 spectrophotometer (NanoDrop Technologies). All determinations were conducted in triplicate. To achieve sample homogeneity, ten soil samples were used for each extraction and all DNA extracts were then fully mixed to form a single DNA sample. All the reactions for qPCR were performed in StepOnePlus Real-Time PCR Systems (v. 2.0, Applied Biosystems) in triplicate using MightyAmp for Real Time (SYBR plus) purchased from Takara Bio (Shiga, Japan). Primers were added to give 0.2 μM in the PCR master mixes for the quantification of the following genes: chiA, amoA, nifH, and nirS. Following hot-start activation, the PCR thermocycle conditions involved 40 cycles of 98 °C for 10 s and 60 °C for 15 s. Quantification of nirK was based on the

Environ Sci Pollut Res Fig. 1 Involved a simplified diagram shows part of the nitrogen cycle and key nitrogen cycle genes involved in this process

nirK-1F and nirK-5R primer set using 0.2 μM in the master mix. Following hot-start activation of MightyAmp DNA Polymerase (98 °C for 2 min), 40 cycles, with 1 cycle consisting of 98 °C for 10 s, 52.6 °C for 30 s, and 72 °C for 45 s, were used. The quantification of narG gene copies was based on the addition of 0.5 μM of its prime set. The PCR thermocycle conditions involved 40 cycles of 98 °C for 10 s, 56.7 °C for 30 s, and 72 °C for 30 s after hot-start activation. Negative controls containing no template DNA were subjected to the same procedure to exclude any possible contamination in all experiments. Moreover, melting curves should be checked to confirm the purity of the amplified products. Standard curves of known copy numbers of each gene were generated. The PCR product generated using each primer pair was cloned into the pMD19-T cloning vector. Plasmid containing the correct insert (determined by sequencing) was harvested from the recombinant Escherichia coli host. Plasmid DNA was extracted with its concentration (nanograms per microliter) measured spectrophotometrically using a NanoDrop® ND-1000 spectrophotometer (NanoDrop Technologies). Standard curves were further constructed by plotting Ct as a function of log of the copy number of the target DNA using the following formula: logT0 =−logE×Ct+ logK, where E is the PCR efficiency, T0 is the initial amount of DNA, and K is the calculated initial amount of DNA for a Ct value of 0. Tenfold serial dilutions of plasmid DNA were prepared to obtain a standard curve for each gene. As the

sequences of the vector and PCR inserts are known, the copy numbers of the target gene could be calculated from the concentration of extracted plasmid DNA. Effects of different VAs exposure on the existing abundance of N-cycling genes in soils were assessed by performing heat maps using MATLAB's clustergram function. The heat map demonstrates variation in the relative abundance of the different VAs treatments.

Results Decline in extractable antibiotics in soils As shown in Fig. 2, the levels of antibiotics in all the antibiotic treatments decreased with time after the application, but different VAs showed different dissipation rates. The residual TC in both single application and combined application became undetectable within 7 days. CIP can only be completely removed after the exposure time prolonged to 20 days, in both the single and combined applications. Among the three single antibiotics, SMM showed a lowest dissipation. After being added into soils for 20 days, 28.20 and 18.90 % of the parent SMM was still detectable in soils sampled from SMM treatments and in soils from AM treatments, respectively. Moreover, SMM, CIP, and TC showed increased dissipations in combined treatments in this pot experiment. CIP

Table 1 Real-time PCR primer sets for nitrogen cycling genes Target gene

Enzyme

Primer

Nucleotide sequence (5′-3′)

Reference

amoA

α-subunit of ammonia monooxygenase Chitinase

nifH

Nitrogenase reductase

nirS

Nitrite reductase

nirK

Nitrite reductase

narG

Nitrate reductase

GGGGTTTCTACTGGTGGT CCCCTCKGSAAAGCCTTCTTC CGTCGACATCGACTGGGARTDBCC ACGCCGGTCCAGCCNCKNCCRTA AAAGGYGGWATCGGYAARTCCACCAC TTGTTSGCSGCRTACATSGCATCAT GTSAACGTSAAGGARACSGG GASTTCGGRTGSGTCTTGA GGMATGGTKCCSTGGCA GCCTCGATCAGRTTRTGGTT TAYGTSGGSCARGARAA TTYTCRTACCABGTBGC

Rotthauwe et al. (1997)

chiA

amoA-1F amoA-2R GA1F GA1R nifH-1F nifH-1R nirScd3AF nirSR3cd NirK-1F NirK-5R narG1960F narG2650R

Williamson et al. (2000) Rosch et al. (2002) Throback et al. (2004) Braker et al. (1998) Philippot et al. (2002)

Environ Sci Pollut Res Fig. 2 Decline in extractable antibiotics in TC-, SMM-, CIP-, and AM-amended (TC+SMM+ CIP) soils. The residual concentration of antibiotic in soils was detected by HPLC-MS/MS. Bars indicated that the concentrations of TC (black), CIP (gray), and SMM (dark gray) remained in soil samples collected from different antibiotic tests

concentration reduced more rapidly in combined treatment compared with that in single CIP treatment. A similar phenomenon was also found for SMM; 64.10 % of SMM dissipated within 7 days in combined treatment, whereas the removal percentage of SMM was only 1.10 % in single SMM treatment after 7 days. The residual concentration of total antibiotics (TC+SMM+CIP) in combined antibiotics treated soils was 36.57 % lower than that in the single SMM-treated soils, after 7 days of incubation. Antibiotics resistance after VAs amendment Cultivable resistant bacteria in soils after application of different VAs were investigated (Fig. 3). Increments in the numbers of all the TC-, SMM-, and CIP-resistant bacteria occurred after a short-term (within 7 days) exposure to the corresponding antibiotic alone, whereas recovery subsequently appeared after the exposure time increased to 20 days. At day 7 after VAs application, TC-, CIP-, and SMM-amended soils exhibited 416.67 % higher TC-resistant bacteria, 69.44 % higher CIP-resistant bacteria, and 24.47 % higher SMM-resistant bacteria than control soils without any antibiotics addition. In respect to antibiotic resistance, multi-antibiotics including TC, CIP, and SMM did present a somewhat different pattern from when they are acting alone. The number of TC-, CIP-, and SMM-resistant bacteria in soils after combined application was, respectively, 218.89, 174.57, and 1,123.40 % higher than those after single application of corresponding antibiotics, at day 7 (Fig. 3). Consistent with that found after single applications, the number of resistant bacteria in the combined treatment exhibited a somewhat reduction after the exposure was time prolonged to 20 days. TC-, CIP-, and SMM-resistant

bacteria in the combined treatment were only 13.75, 29.87, and 328.26 %, respectively, more than that in the control soil, after antibiotics application for 20 days. The positive effect of combined treatment on the proliferation of SMM-resistant bacteria was found to be the strongest. Soil N after VAs amendment Application of antibiotics resulted in a shift in soil nutrients (Table 2). Although no apparent shifts were found in both TN and organic matter content after antibiotics application, there was a large spread in available N content among soil samples exposed to different antibiotics. After 20 days of both single SMM application and combined application, available N and NO3-N in soils accumulated in a high amount. Different from the soil after single TC application, soils after combined application has an extremely low content of NH4–N and NO2–N, which were the lowest among all soil samples regardless of exposure time. NO3–N is the predominant form of available N, maintained in a high concentration in all the soil samples. In this work, the dissipations of NO3–N in soils changed significantly (pCIP>TC>SMM>AM, which indicated that the combined application and the alone application of SMM exhibited the strongest inhibition on NO3–N utilization or NO3–N loss. Soil respiration activity after VAs amendment CO2 respiration activities of soil samples after the application of different VAs were determined using MicroResp™ system

Environ Sci Pollut Res Fig. 3 Numbers of cultivable resistant bacteria in control treatments without antibiotics application (dark gray), the single-antibiotic treatments (black), and the multi-antibiotics treatment (gray). The soils after the application of single TC, single SMM, and single CIP was used for the analysis of TC-, SMM-, and CIP-resistant bacteria, respectively. The numbers of all TC-, SMM-, and CIP-resistant bacteria were analyzed in the control soil and AM-amended soil

(Table S1); 0.235 μg CO2–C/g soil was released from the control soils collected at day 7; whereas 0.287, 0.366, 0.954, and 0.824 μg CO2–C/g soil was released from the TC-, CIP-, SMM-, and AM-amended soils, respectively. The CO2 respiration results of soil samples collected on the 20th day showed a similar trend with that collected on the 7th day. AMamended soil released a maximum amount of CO 2 (1.336 μg CO2–C/g soil), followed by the SMM-amended soil (0.604 μg CO2–C/g soil), CIP-amended soil (0.299 μg CO2–C/g soil), TC-amended soil (0.288 μg CO2–C/g soil), and control soil (0.261 μg CO2–C/g soil). All the antibioticspiked soils released higher CO2 than the control soil without antibiotics application. Among those applications, single SMM and combined applications stimulated soil respiration

highly. The CO2 evolutions in treatments after the single SMM and combined applications were, respectively, 1.3–3.0 and 2.5–4.1 times higher than that in control treatments. Whether antibiotics contamination affected microbial community structures of soils was also evaluated. Average SIR was corrected for basal respiration and calculated as the difference between micrograms CO2–C produced in soil with the addition of CLPP substrate and micrograms CO2–C produced in the same soil with only H2O added. PCA was applied to data set obtained with the various C sources, normalized by the SIR values for each soil samples (Fig. 4). CLPP was analyzed by three uncorrelated variables (first principal component (PC1), second principal component (PC2), and third principal component (PC3)). The first axis PC1 includes most

Table 2 Soil nutrients in different soil samples Time

Test

Organic matter (%)

TN (%)

Available nitrogen (mg kg−1)

NH4–N (mg kg−1)

NO3–N (mg kg−1)

NO2–N (mg kg−1)

7

CK TC CIP SMM AM CK TC CIP

1.29±0.00 1.19±0.11 1.16±0.17 1.24±0.06 1.19±0.14 1.19±0.18 1.18±0.12 1.20±0.09

0.12±0.01 0.12±0.02 0.10±0.02 0.11±0.01 0.11±0.01 0.09±0.01 0.11±0.02 0.10±0.01

444.20±2.55 482.60±38.30 235.68±20.12 306.72±12.38 330.82±4.08 207.43±13.67 409.72±5.87 129.53±17.87

5.15±0.39 12.19±0.74 8.69±0.39 7.72±0.34 3.73±0.05 7.47±0.10 7.04±0.07 11.00±0.12

431.09±9.79 448.75±5.63 232.60±2.57 139.49±2.14 190.70±0.61 143.61±3.31 389.54±1.71 109.34±0.12

0.54±0.05 0.49±0.03 0.57±0.04 0.56±0.06 0.39±0.04 0.52±0.01 0.55±0.04 0.74±0.04

SMM AM

1.16±0.11 1.19±0.18

0.10±0.01 0.12±0.02

197.80±6.77 616.97±14.55

8.49±0.11 3.71±0.17

169.67±21.30 383.39±9.67

0.56±0.01 0.43±0.01

20

Data were shown by mean±standard error AM antibiotic combination of TC, SMM, and CIP

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Fig. 4 PCA scores plot of the data of SIR measured with the MicroResp™ method and three replicates were used for per sampling soil. TC-7d amended soils sampled at day 7, TC-20d TC-amended soils sampled at day 20, CIP-7d CIP-amended soils sampled at day 7, CIP-20d CIP-amended soils sampled at day 20, SMM-7d SMM-added soils sampled at day 7, SMM-20d SMM-added soils sampled at day 20, AM-7d

AM-amended (TC+CIP+SMM) soils sampled at day 7, AM-20d AMamended (TC+CIP+SMM) soils sampled at day 20, CK-7d control soils without antibiotics addition sampled at day 7, CK-20d control soils without antibiotics addition sampled at day 20. Loadings plot of CLPP substrates. CLPP substrates influential in the component separation are labeled directly on the plot. a PC1 vs PC2; b PC1 vs PC3

substrates such as L-alanine, citric acid, D-fructose, D-glucose, L-malic acid, NAGA, oxalic acid, 3,4-dihydroxybenzoic acid, trehalose, and L-lysine (Fig. 4). The results in Fig. 4 also confirmed the marked difference between different antibiotic-spiked soils, separated principally with the PC1 axis. Single SMM and AM treatments presented the greatest differences from control treatments regardless of incubation time. The evenness that reflects diversity of soil microbial community and the richness data that reflects the ability of a population to metabolize all of substrates offered consistent patterns (Table S1). The metabolic richness calculated from SIR results was altered among different soil samples. More than half of the CLPP substrates could not be utilized effectively in soils after single SMM and combined applications, regardless of the exposure time. Accordingly, the metabolic richness were quite low in single SMM and combined treatments but differed less among TC, CIP, and CK treatments. All the abovementioned results indicated the apparent and negative effects of single SMM and combined applications on soil microbial metabolism diversity. Although responding behaviors of soil microbial metabolism diversity to combined application were quite consistent with that of the single SMM application, the synergistic effect of the combined contamination of SMM, CIP, and TC could still be observed from the PCA plots (Fig. 4). SIR of the substrates highly correlated with PC3 were improved in single SMM treatments but depressed in the combined treatment.

The abundance of N-cycling genes after VAs amendment Six metabolic genes known to have a role in the biochemical cycling of N in soil were quantified to investigate dffects of VAs application on the microbial ecology of N transformation (Fig. 1). All the primers used in this study had been demonstrated to maintain specificity. The real-time PCR data showed that a single melting peak corresponding to the standard DNA was observed for both soil samples and plasmids containing the target gene (data not shown). By sequencing analyses, plasmids containing the correct insert were selected (Table S2) to generate a standard curve-relating Ct to the number of gene copies. All standard curves had a high correlation coefficient and an effective PCR efficiency (Table S2). The selected N-cycling genes were then detected at all soil samples with the gene copy abundances shown in Fig. 5. With the exception of nifH, almost all the detected genes showed lower abundances in antibiotic-spiked soils than in control soils at day 7. Inhibitive effects of antibiotics application on functional gene abundances in soils reduced at day 20. Particularly in soils at 20 days after TC amendment, it is hard to find inhibition of TC on the abundance of all N-cycling genes. However, lasting effects of antibiotics application still existed and varied among different N-cycling genes. Soils after the application of single SMM for 20 days has higher copy numbers of chiA and narG compared with the control soils; CIP-amended soils showed an increased copy numbers of amoA and narG and a decreased

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Fig. 5 Real-time PCR quantization of chiA (a), amoA (b), nifH (c), nirS (d) nirK (e), and narG (f) in different soil samples collected after antibiotics application for 7 and 20 days. The details of the soil samples were described as follows: TC-7d TC-amended soils sampled at day 7, TC-20d TC-amended soils sampled at day 20, CIP-7d CIP-amended soils sampled at day 7, CIP-20d CIP-amended soils sampled at day 20, SMM-7d

SMM-added soils sampled at day 7, SMM-20d SMM-added soils sampled at day 20, AM-7d AM-amended (TC+CIP+SMM) soils sampled at day 7, AM-20d AM-amended (TC+CIP+SMM) soils sampled at day 20, CK-7d control soils without antibiotics addition sampled at day 7, CK20d:control soils without antibiotics addition sampled at day 20

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Our studies provided systematic results to answer the question, “how soil microbial systems within the soil-microbe-plant

environment would shift under each single- or multiantibiotics perturbation?” Reports on the toxicity effects of VAs on the environments are increasing in recent years. Consistent with these studies showing adverse impacts of antibiotics on plants (Liu et al. 2009; Migliore et al. 2003), all VAs applied in this work impaired the growth of B. chinensis; in addition, single treatment with TC greatly reduced plant biomass. There is abundant evidence that plants impact ecosystem function, which may be mediated by absorption, fireinduced losses, N leaching, N fixation, and so on (Knops et al. 2002). This indicated that soil functional and structural changes may have contributed to not only the inhibitive effects of VAs on soil microbes but also differences in plant quantity and quality caused by VAs application. Therefore, these idiosyncrasies we get may likely reflect complex feedbacks within the soil–microbe–plant system. Furthermore, the soil microbial system responded differentially to various VAs perturbations. SMM and AM (involving SMM) exhibited the largest and most lasting influences on the soil microbial systems. Moreover, the multi-antibiotic-spiked (i.e., AM) soils containing TC, CIP, and SMM do show distinct influences from single-antibiotic-spiked soils due to the feedback in soil–microbe–plant system. Variations in the decline in extractable antibiotics were observed in different treatments. HPLC-MS/MS studies (data not shown) demonstrated that the extraction recovery of TC, CIP, and SMM from soils was 90, 95, and 100 % in this work, respectively. Owing to the high extraction recovery, we supposed that variations in the decline in extractable antibiotics are primarily due to their different dissipation in soils. Overall, SMM exhibited a much lower dissipation than both TC and

Fig. 6 Heat map constructed by using MATLAB's clustergram function for demonstrating variation in copy numbers (Log10) of six N-cycling genes after the application of different antibiotics. Three soil samples were collected from one treatment for each N-cycling gene. The color of each window represents the normalized data of gene copy numbers, as indicated by the scale on the left. Rows and columns represented gene types and soil samples from different antibiotic treatments, respectively. TC-7d TC-amended soils

sampled at day 7, TC-20d TC-amended soils sampled at day 20, CIP-7d CIP-amended soils sampled at day 7, CIP-20d CIP-amended soils sampled at day 20, SMM-7d SMM-added soils sampled at day 7, SMM-20d SMMadded soils sampled at day 20, AM-7d AM-amended (TC+CIP+SMM) soils sampled at day 7, AM-20d AM-amended (TC+CIP+SMM) soils sampled at day 20, CK-7d control soils without antibiotics addition sampled at day 7, CK-20d control soils without antibiotics addition sampled at day 20

copy number of nirS. The effects of multi-antibiotics (i.e., AM) containing TC, CIP, and SMM do show differentiation from that of antibiotics acting alone. This could be observed clearly from Fig. 5. For example, the soils exposed to all the antibiotics alone would have a higher copy of narG compared with the control soil, while the soils exposed to AM, the combination of SMM, TC, and CIP has a lower copy of narG. Heat map generated from MATLAB's clustergram function give a visual presentation for these variation in each gene copy number after antibiotics applications (Fig. 6). Four clusters could be grouped for these treatments according to the different responses of gene copy numbers. The highest average copy number occurred in group 1, which contains control soils without antibiotics addition sampled at day 7 and TC-amended soils sampled at day 20; followed by group 2, which contains CIP-amended soils sampled at day 20, control soils without antibiotics addition sampled at day 20, and SMM-added soils sampled at day 20; and then group 3, which contains only AM-amended (TC+CIP+SMM) soils sampled at day 20, and finally group 4, which include AMamended (TC+CIP+SMM) soils sampled at day 7, SMMadded soils sampled at day 7, CIP-amended soils sampled at day 7, and amended soils sampled at day 7. A short-term exposure (within 7 days) to all the antibiotics exerted negative effects on the abundances of all detected genes, whereas only AM presented a lasting influence.

Discussion

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CIP. Several studies (Dolliver et al. 2008; Kotzerke et al. 2008; Kreuzig and Holtge 2005) have ever reported the recalcitrant of sulfonamides drugs. Kotzerke et al. (2008) showed in their reports that 20 % of the parent SDZ was still extractable from soils after being added into soils at an initial concentration of 100 mg kg−1 for 32 days. Similarly, in the study of Dolliver et al. (2008), degradation of CTC and sulfamethoxazole (SMZ) in poultry manure at 60 °C showed no degradation of SMZ, whereas CTC declined by 99 %. The recalcitrant of sulfonamide drugs that contributes to a long interruption time may be attributed to their stronger influences on organisms (e.g., plants and microbes) in agricultural environments. As antibiotics uptake in plants could be one of the channels for VAs removal from soil (Kumar et al. 2005), we can believe that the transfer of antibiotics to plant biomass would promote the removal of antibiotics from soils. This suggested that the low dissipation of SMM might associate with its high biotoxicity to some extent, as SMM and AM (involving SMM) showed a most strong inhibition on B. chinensis among the four VAs (including single- and multi-antibiotics) detected. In both single SMM treatment and the combined treatment involving SMM, most of the B. chinensis plants withered at day 20. Conversely, the feedback regulation of soil microbial communities would have the potential for great impacts on VAs removal due to biodegradation or biotransformation (Sarmah et al. 2006). There is a possible relationship between the low dissipation of SMM and the small population of SMM-resistant bacteria, as a majority of antibiotic-resistant microbial communities would be essential for the degradation and inactivation of antibiotics (Kumar et al. 2012). That synergistic effects of combined application of TC, CIP, and SMM found throughout this study was an interesting finding in this work. The combined application of SMM, CIP, and TC greatly promoted the dissipation rates of each antibiotic; this phenomenon is quite similar with cometabolism (Alexander 1981). It has been shown in several studies (Chait et al. 2007; Keith et al. 2005) that the coexistence of two or even more antibiotics would present a different effect from when they are acting alone. Thus, it is speculated that the combined effects of TC, SMM, and CIP differ from that of a single antibiotic being contributed to the dissipation of each antibiotic. Conversely, Fang et al. (2014) found contrary results that combined treatment with SDZ and CTC did not alter the respective dissipation rates significantly. It indicated that further investigations should be performed for the elucidation. Consistently, combined treatment with TC, CIP, and SMM also stimulated the occurrence and spread of antibiotic-resistant bacteria. Particularly, a sharp increase in the population of SMM-resistant bacteria was found in combined treatments compared with individual SMM treatments, which showed that sulphonamide resistance is stimulated in the combined treatment. Although molecular mechanisms for the higher presence of sulphonamide resistance still needs further investigations; this phenomenon appeared to be reasonable that class I integrons, linked closely with sulphonamide

resistance, could be associated with other resistance genes or genes assisting antibiotic degradation (Hall and Collis 1998). Similarly, the increase in resistant bacteria populations are expected to be associated with the elevated SMM degradation to some extent. The applications of SMM and AM resulted in a most robust stimulation in soil basal respirations even though each antibiotic showed low dissipations in SMM- and AM-spiked soils. The similar trend has also been reported by Vaclavik et al. (2004), who showed that the respiration of soils increased by 1.3–1.7 times after pretreated with antibiotics such as CTC, sulfonamide, and sulfachloropyridazine, at initial concentrations of 60 and 600 mg kg−1. Similarly, Fang et al. (2014) showed that soil samples after fifth treatment with SDZ or CTC exhibited higher CO2 fluxes than the control soils without antibiotics treatment. The results could be nonspecific and associated with uncertainty as soil processes responsible for respiration are complex and may just be stress responses to pressures. This is in agreement with the finding of antibiotics, which exerts an effect on respiration in this test without them functioning as a source of C (Vaclavik et al. 2004). Fang et al. (2014) also concluded in their work that the increased respiration after VAs application might not be attributed to the substantial degradation of antibiotics. Basal respirations in SMM- and AM-treated soils decreased after the addition of some CLPP substrates. It seemed that "CO2 burst" observed upon antibiotics application was alleviated by amending soil with a variety of CLPP substances, which resulted in reduced CO2 respiration. However, the mechanisms behind this are not clear; the dramatic inhibitions of SMM and AM on the diversity of soil microbial community may be one of the causes. SMM-induced changes of soil microbial community structures proceeded for a relative longer time as those found in some previous studies (Hammesfahr et al. 2008; Schmitt et al. 2004), in which we observed similar inhibition of CLPP substrates on basal respirations of soils (Campbell et al. 2008; Chen et al. 2013; Pietravalle and Aspray 2013). In the report of Pietravalle and Aspray (2013) on the determination catabolic diversity of different hydrocarbon-contaminated soils, the basal respiration of soil B significantly dropped after the addition of sodium fumarate and trisodium citrate. Some C sources (e.g., arginine, fructose, and trehalose) would exhibit negative effects on microbial species (Chen et al. 2013; Murray et al. 1996). Subsequently, gene abundance was used as a proxy of the potential rate of various N-cycling processes (Lindsay et al. 2010; Ollivier et al. 2010). Shifts in ecological function following antibiotics perturbation were assessed, although the effectiveness of this index might be influenced by several factors. Oxidation of ammonia is a key process in the global N cycle, during which ammonia-oxidizing archaea (AOA) and AOB are demonstrated to be the functional group. In this work, responses of amoA-AOB in soils to antibiotics were

Environ Sci Pollut Res

studied. The gene copy number of amoA in soils (sampled at day 7) decreased in a large extent after antibiotics perturbation; the inhibition of AM was most dramatic, followed by SMM, and CIP and TC were less dramatic. Similar results have been reported in investigations of bulk soil (Schauss et al. 2009) and rhizosphere of agricultural crops (Ollivier et al. 2010; Ollivier et al. 2013); these works demonstrated that AOB react stronger to sulfonamides than AOA (Ollivier et al. 2010; Ollivier et al. 2013; Schauss et al. 2009). Moreover, the perturbation treatment of SMZ penicillin, oxytetracycline, or streptomycin had a significant and rapid impact on the presence of amoA, which fell below detectable levels after the antibiotics application (Colloff et al. 2008). In contrast to the amoA gene, the abundance of nifH was not significantly affected by any types of antibiotics. It is possible that the active N-fixing bacteria are protected from the antibiotic and therefore are not affected (Ollivier et al. 2010). In addiiton, we noted that the soil used in this work contains a high amount of available N; thus, N provided by N fixers might not be needed in this soil system. Effects of TC, SMM, CIP, and AM on nitrite reducers in bulk soils were studied by using the three genes nirK, nirS, and narG involved in denitrification. Among the four VAs treatments, only the AM treatment having a reduced copy number of narG showed an extremely low amount of NO2–N. Under the pot conditions in this work, both nirK and nirS after 7 days of antibiotics application exhibited reduced pattern of abundance. Similar phenomena have been reported by Ollivier et al. (2010), showing that the antibiotic SDZ effects would lead to reduced abundances of both nirK and nirS in the root–rhizosphere complexes of agricultural crops. Different results also could be found that SDZ application resulted in an increase abundance of nirS and a decrease abundance of nirK in bulk soils after dark incubation for 32 days (Kleineidam et al. 2010a). All these results indicated that responses of nirK- and nirS-harboring microbes to sulfonamide drugs could differ among different studies, which might be due to different soil conditions. Generally, our results indicated that the abundances of microbes involved in soil N-cycling processes respond differentially to various antibiotics perturbations. These soils exposed to antibiotics for 7 days showed reduced abundances of N-functional groups than healthy soils, whereas the influences of SMM and AM on the abundance of N-functional genes remained for a longer time than that of TC and CIP. Thiele-Bruhn and Beck (2005) indicated that structural changes of the soil microbial community, once initiated, might proceed for several weeks. The decrease in gene abundances of the N-cycling genes may have contributed to not only the inhibitive effects of antibiotics on N-cycling microbes but also differences in plant quantity and quality caused by antibiotics application. For example, as plant (e.g., B. chinensis in this work) decreased, the amount of labile organic material supplied from plants to soil microbes would likely decrease

(Knops et al. 2002); this may cause the decrease in the gene abundances of the heterotrophic N-cycling microbes (Zhang et al. 2013). Metabolites as products of the antibiotics (bio-)degradation would exert deviant effects as well (Halling-Sorensen et al. 2002). Thus, the mechanisms responsible for the changes in N-functional genes likely reflect complex feedbacks within the soil–microbe–plant system under pot conditions. Whether the observed alterations in nutrient state of nitrate, nitrite, and ammonium were caused by a general shifts in abundance of N-cycle genes still needs further investigations (Kotzerke et al. 2008). On the first glance, one might speculate that changes in the abundance of N-cycling genes may reflect soil N in soils. As it is generally assumed that NO2 and NH4 are intermediates occurring during nitrification, the reduction in gene level of amoA would lead to a decrease amount of nitrite and an accumulation of ammonia. Similarly, inhibitory effects of SMM or AM on denitrify genes (e.g., nirK, nirS, and narG) may contribute to an accumulation of NO3–N and a loss in NO2–N. In this work, we found that the nirK abundance correlated negatively (Pearson correlation=−0.602, p= 0.066) with NH4–N concentration in soils, whereas the abundance of narG correlated positively (Pearson correlation= 0.511, p=0.131) with NO2–N concentration. However, significant correlation between the N content (i.e., TN, NO3–N, NO2–N, and NH4–N) and the abundance of the six N-cycling genes could hardly be observed according to correlation analysis results (data not shown). Actually, N input, loss, and form transformation could relate with many environmental factors. Soil N reflects not only N-cycling microbes but other organisms in soils. Furthermore, NO3–N accumulation in soils of 20 days after the AM application could be another important indicator reflecting the strong toxicity effects of antibiotics. Most of the soil samples showed a reduced or stable NO3–N content from the 7th to 20th days; the NO3–N content increased dramatically in AM-spiked soils on the contrary. NO3–N accumulation in soils could be hazardous to the human health through food chain eventually (Wang et al. 2002), and the greatly NO3–N runoff loss would lead to the potential risk for the water body (Camargo and Alonso 2006). Acknowledgments This study was supported by the Special Fund for Agro-scientific Research in the Public Interest (201303091), the Scientific and Technological Innovation Capacity Improvement Programmer of the Zhejiang Academy of Agricultural Sciences, and the National Key Technologies R&D Program of China (2012BAC17B04).

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