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Biodegradation of sulfonamide antibiotics in sludge
Chemosphere xxx (2016) 1e7
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Biodegradation of sulfonamide antibiotics in sludge Chu-Wen Yang, Wan-Chun Hsiao, Bea-Ven Chang* Department of Microbiology, Soochow University, Taipei, Taiwan
h i g h l i g h t s This study investigated sulfonamides biodegradation in sludge. The removal rates of sulfonamides of were enhanced by addition of spent mushroom compost at six times. Acinetobacter and Pseudomonas were major bacteria involved in sulfonamides degradation in sludge.
a r t i c l e i n f o
a b s t r a c t
Article history: Received 10 November 2015 Received in revised form 15 February 2016 Accepted 15 February 2016 Available online xxx
Sulfonamide antibiotics are widely used in human and veterinary medicine. This study assessed the degradation of three sulfonamides (100 mg kg1 each of sulfamethoxazole, sulfadimethoxine and sulfamethazine) and changes in the microbial communities of sewage sludge. Sulfamethoxazole degradation was enhanced by spent mushroom compost (SMC), SMC extract, and extract-containing microcapsules in the sludge. The degradation of sulfonamides in sludge and SMC mixtures occurred in the order of sulfamethoxazole > sulfadimethoxine > sulfamethazine. Bioreactor experiments revealed that the sulfonamides removal rates in sludge with SMC were greater than those in sludge alone. The sulfonamides removal rates were enhanced by the addition of SMC for six time additions. The sulfonamides concentrations were 200 and 500 mg kg1 for the first to third additions and the fourth to sixth additions, respectively. With the high correlations between TOC and the proportions of sulfonamides remaining in sludge, sulfonamides may be mineralized to a greater extent with SMC in sludge than in sludge alone. Four bacterial genera were identified from the different settings and stages of the bioreactor experiments. Acinetobacter and Pseudomonas were major bacterial communities that were responsible for sulfonamide degradation in sludge. © 2016 Published by Elsevier Ltd.
Handling Editor: Shane Snyder Keywords: sulfonamide antibiotics Degradation Spent mushroom compost Sludge
1. Introduction Antibiotics have been recently been investigated as a source of emerging environmental contaminants. These antibiotics may exert selective pressure that favours resistant bacteria (Schwartz et al., 2003), which are a major public health concern due to the increased occurrence of associated clinical infections. Sulfonamide antibiotics (SAs) exhibit a wide spectrum of action against most gram-positive and many gram-negative microorganisms. SAs inhibit the proliferation of bacteria by acting as competitive inhibitors of p-aminobenzoic acid in the folic acid metabolism cycle (Sukul and Spiteller, 2006). SAs that are excreted by humans or animals can enter wastewater treatment plants (WWTPs) through
* Corresponding author. E-mail address: [email protected] (B.-V. Chang).
the sewage system (Ingerslev and Halling-Sørensen, 2000). Conventional wastewater treatment facilities are unable to efficiently remove SA residues (Barber et al., 2009). SAs have been found in the sludges of most WWTPs (Gobel et al., 2005; Nieto et al., 2010). Three SAs, i.e., sulfamethoxazole (SMX), sulfadimethoxine (SDM), and sulfamethazine (SMZ), have been detected in the sludge of a WWTP in Taiwan (Yang et al., 2011). Microbial degradation has been proposed to serve as one major process for the removal of antibiotics from contaminated sludge. Pleurotus eryngii is one of the most widespread white rot fungi in the world (Lee et al., 2004). Spent mushroom compost (SMC) is a waste product of the mushroom industry (e.g., P. eryngii cultivation factories) and contains many residual enzymes that can be used to degrade many organic toxic chemicals (Lau et al., 2003; Li et al., 2010). SMC contains bacteria that can degrade many organic toxic chemicals, such as nonylphenol, tetrabromobisphenol-A and tetracycline (Hsu et al., 2013; Chang and Ren, 2015).
http://dx.doi.org/10.1016/j.chemosphere.2016.02.064 0045-6535/© 2016 Published by Elsevier Ltd.
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Metagenomic studies with next-generation sequencing allow for the study of microbial diversity in environmental samples with genus-level resolution (Xu et al., 2012; Yang et al., 2015a, b). However, little is known about the microbial communities that are involved in SAs degradation in sludge. The aim of this study was to compare the degradations of SMX according to various additives, various sludge-to-SMC ratios, and the individual or simultaneous presence of three SAs. The degradation rates of SAs and the bacterial community changes in the sludge in bioreactor were also evaluated. The target compounds were SMX, SDM, and SMZ. 2. Materials and methods 2.1. Chemicals SMX, SDM and SMZ of 99.0% purity were obtained Sigma Chemical Co. (St. Louis, MO). The solvents were from Mallinckrodt (Paris, KY), and all other chemicals were from Sigma Chemical Co. Individual stock solutions of SMX, SDM and SMZ were dissolved in 0.03 M NaOH to establish concentrations of 5000 mg L1 and then diluted with water before use. 2.2. Sampling and medium The sludge was a semi-solid slurry that was produced as sewage sludge from the Dihus wastewater treatment plant in Taipei, Taiwan. The SMX, SDM, and SMZ concentrations in sludge were 5.9, 9.4, and 5.2 mg kg1, respectively. The basal medium contained the following (in mg L1): K2HPO4, 65.3; KH2PO4, 25.5; Na2HPO4‧ 12H2O, 133.8; NH4Cl, 5.1; CaCl2, 82.5; MgSO4‧7H2O, 67.5; and FeCl3‧ 6H2O, 0.75. The pH of the basal medium was adjusted to 7.2 before autoclaving at 121 C for 20 min. 2.3. Preparation of the SMC, SMC extract and extract-containing microcapsule The SMC of P. eryngii was produced at a mushroom cultivation factory in Chiayi, Taiwan. The SMC extract (SE) was extracted from the 120 g SMC via the use of 600 mL sodium acetate buffer (pH 5.0) for 3 h at 4 C. The samples were centrifuged (10,000 g 10 min), and the supernatants were partially purified by precipitation with ammonium sulfate and dialysis. SMC is a potential source of ligninolytic enzymes (Li et al., 2010). Most ligninolytic fungal species produce constitutively at least one laccase isoenzyme and laccases are also dominant among ligninolytic enzymes in the environment (Baldrian, 2006). We measured the laccase activity in the sludge with SMC, MC, and SE. The laccase activity was measured by spectrophotometry at 405 nm. The reactive mixture contained 0.5 mL of enzyme supernatant, 0.25 mL of 100 mM glycine buffer (pH 3.0), and 0.25 mL of 4 mM ABTS. The enzyme reactive mixtures were incubated at 25 C for 5 min before measurement. For the evaluation of the laccase activity, one activity unit was defined as the amount of enzyme necessary to oxidize 1 mmol of substrate per min. Each value presented in this paper represents the mean of three replicates. Alginate solution was made by dissolving sodium alginate (4 wt.%) in 0.9 wt.% sodium chloride with stirring for 1 h at room temperature. SE was then added to the alginate solution. The final concentration of alginate was 2.0 wt.% in the mixture solution. An electrostatic droplet generator was used to fabricate the capsules (Hsu et al., 2013). The mixture solution was drawn into a 10-mL syringe fitted with a 23G needle and attached to a syringe pump that provided a steady solution flow rate. The solution was pumped through the needle at a fixed feed rate (25.2 mL h1) and fixed voltage (12 kV) into a gently agitated aqueous solution of calcium
chloride (1.5 wt.%) to form an extract-containing microcapsule (MC) of 250 mm in diameter for the experiments. 2.4. Experimental design The batch experiments involved 125-mL serum bottles containing 40 mL medium, 5 g sludge, 5 g SMC and the SAs. We first compared three additives (i.e., SMC, SE and MC) and various sludgeto-SMC ratios (i.e., 1:0.5, 1:1, 1:1.5 and 1:2) in terms of the degradation of SMX (200 mg kg1) in the sludge. We also compared the three SAs (200 mg kg1 each of SMX, SDM and SMZ) that were present individually or simultaneously in the sludge-SMC mixtures. Inoculated controls were treated with sludge as the inoculum and incubated with shaking at 30 C in the dark. Sterile controls were prepared by autoclaving the sludge with SMC, SE or MC at 121 C for 20 min on 3 consecutive d. Each treatment was performed in triplicate. Samples were periodically collected to measure the residual SAs. The bioreactor was designed as previously described (Chang and Ren, 2015) and was aerated via the use of an air diffuser. The materials were agitated with a stirrer. Bioreactor A was filled with 1800 mL medium and 200 g sludge. Bioreactor B was filled with 1600 mL medium, 200 g sludge, and 200 g SMC. The three SAs were added, and when the SMX decreased to below the detection limit (ND), the three SAs were added again. The SA concentrations were 200 and 500 mg kg1 for the first to third additions and the fourth to sixth additions, respectively. The entire experiments experiment lasted 82 d. Samples were taken periodically for analyses of the residual SAs, total organic contents (TOCs) and microbial communities. 2.5. Chemical analysis SA determination was performed with an Agilent 1260 HPLC system equipped with a 4.6 250-mm column (Zorbax Eclipse Plus C18, Agilent) with a photodiode array detector monitoring at 270 nm. The mobile phase was acetonitrile and water (with 0.1% formic acid) at 70%:30%. Flow rate was 1 mL/min. The 0.5 mL aqueous phase samples were collected, and equal volumes of extraction solution were added with water (containing 0.1% formic acid): acetonitrile: methanol ¼ 10:3:1, vortexed well and centrifuged at 10,000 rpm for 5 min. The supernatant was filtered with a 0.22-mm filter into a vial for HPLC. The recovery percentages for SMX, SDM and SMZ were 97.5%, 97.4%, and 96.4%, respectively. The detection limits for SMX, SDM, and SMZ were all 0.1 mg L1. The TOCs were measured using a TOC Analyzer (OI Analytical 1030W, USA). 2.6DNA extraction, PCR, and pyrosequencing Total DNA was extracted from each experimental sample using the PowerSoil DNA Isolation Kit (MO BIO Laboratories). Partial 16S rRNA genes containing variable V5eV8 regions were amplified from the extracted DNA. The sequence for the 50 primer included a 454 pyrosequencing adaptor, a unique 4-mer tag for each sample and 787F (50 -ATTAGATACCCNGGTAG-30 ) for the 16S rRNA genes. The sequence of the 30 primer included a 454 pyrosequencing adaptor and 1391R (50 -ACGGGCGGTGWGTRC-30 ) for the 16S rRNA genes. The PCR reactions were performed as we have previously described (Yang et al., 2015a). Pyrosequencing was performed at the Genome Center of the National Yang-Ming University, Taiwan, ROC with a GS Junior System (Roche Diagnostics Corp., CT, USA). The sequence data produced in this study are provided as Supplementary material.
Please cite this article in press as: Yang, C.-W., et al., Biodegradation of sulfonamide antibiotics in sludge, Chemosphere (2016), http://dx.doi.org/ 10.1016/j.chemosphere.2016.02.064
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2.7. Data analysis The calculations for the SAs and TOC remaining percentages were performed as follows: [%] ¼ (residue substrate concentration/ initial substrate concentration) 100. The degradation data for the SAs collected in this study were well fit with first-order kinetics; i.e., S ¼ S0exp (-k1t), t1/2 ¼ ln 2/k1, where S0 is the initial concentration, S is the substrate concentration, t is the time, and k1 is the degradation rate constant. The SA removal rate [%] ¼ [1e (residue substrate concentration/initial substrate concentration)] 100. Significant differences were accepted at p < 0.05. The 16S rRNA gene sequence data were first analysed using Chimera Check to remove the chimeric sequences. The sequences that passed the Chimera Check were analysed with the RPD classifier in the RDPipeline (http://pyro.cme.msu.edu/) for phylogenetic classification. We used the highest similarity (95%) provided by the classifier software with the Ribosomal Database Project Web site for the groups of bacteria. The differences in bacterial community compositions between the samples were analysed with the Detrended Correspondence Analysis (DCA) function of the vegan package of R. Spearman correlation coefficients were computed using the cor.test function of R. The differences in the distribution of the bacterial genera were identified with ManneWhitney U tests using the wilcox.test function in R. 3. Results and discussion 3.1. Effect of various factors on the degradation of SAs The three SAs in the sterile controls were first examined at the end of the 8-d incubation. The remaining percentages of the three SAs ranged from 91.9% to 98.8% in the soil-sludge mixtures. Therefore, the SA degradation that occurred in all of the following experiments was due to microbial action. We first compared the effects of the three additives (SMC, MC and SE) on SMX degradation after 8 d of incubation in the sludge. As illustrated in Fig. 1, the remaining percentage of SMX was 58.3% without the three additives (i.e., SMC, SE or MC). SMX was completely degraded following the addition of SMC, but the percentages of SMX remaining following the additions of MC and SE were 3.2% and 1.8%, respectively. The SMX degradation data were well fit with first-order kinetics. The degradation half-lives (t1/2) of SMX were 1.3, 2.1 and 2.9 d in the sludge for SMC, MC and SE, respectively. The SMX degradation rates in the sludge occurred in the order of SMC > MC > SE. Thus, SMX degradation was enhanced
3
by the three additives (i.e., SMC, MC and SE), and SMC yielded a greater SMX degradation rate than the other additives to the sludge. Thus, SMC was used in all of the following experiments. SMC contains ligninolytic enzymes that can enhance SMX degradation (Lau et al., 2003). The laccase activities in the sludge with SMC, SE and MC were initially 878.4 ± 18.1, 862.1 ± 13.8, and 850.6 ± 18.2 UL1, respectively, and 860.4 ± 19.6, 672.1 ± 15.9, and 826.7 ± 21.2 UL1, respectively, after 10 d of incubation. The effects of the three additives on laccase activity occurred in the order SMC > MC > SE. SMC contains enzymes, bacteria, and fungi that all might play roles in the degradation of SMX. Additionally, the SMX degradation rate was higher with MC than with SE in the sludge. Removal with MC was better than SE in terms of improving the tolerance to the environmental loadings. Similar trends were revealed in our previous study, which demonstrated more efficient removal of nonylphenol with immobilized SE than the suspended form (Hsu et al., 2013). The SMX degradation data for the various sludge-to-SMC ratios were well fit with first-order kinetics as presented in Table 1. When the sludge-to-SMC mixture ratio was 1:1, the SMX degradation rate was greater than the rates observed in the experiments with mixture ratios of 1:0.5. When excessive amounts of SMC were added, and the mixture ratio was further increased (i.e., 1:1.5 and 1:2), SMX degradation was inhibited. Therefore, the optimal mixture ratio was 1:1. This finding agrees with that of Namkoong et al. (2002), who reported that while the addition of organic supplements increases the rate of contaminant degradation, excessive supplementation inhibits degradation. When the added carbon source is preferentially degraded relative to the target compounds, the microbial degradation of the target contaminants may be inhibited (Chang et al., 2009). Therefore, we used a sludgeto-SMC ratio of 1:1 in the following experiments. The degradation data for the individually and simultaneously presence of the three SAs in the sludge-SMC mixtures are presented in Table 2. The order of the degradation of the three SAs in the sludge-SMC mixtures was SMX > SDM > SMZ. The chemical structures of SMX, SDM and SMZ are illustrated in Fig. 2A. Functional groups may contribute electronegativity effects that hinder degradation by affecting the interaction between the contaminant and the microbes. With increasing electronegativities of the substituents, SA degradation rates decrease (Pepper et al., 2015). Additionally, when the three SAs were simultaneously present in the sludge-SMC mixture, the degradation rates decreased (Table 2). The rates were higher when the three SAs were present individually than when they were present simultaneously. Higher SA concentrations may affect microbial activity and inhibit SA degradation. 3.2. Bioreactor experiments The removals of the three SAs from the sludge alone and the sludge with SMC in the bioreactors are illustrated in Fig. 2. The concentrations of three SAs were 200 mg kg1 for the first to third additions and 500 mg kg1 for the fourth to sixth additions. In the
Table 1 Sulfamethoxazole (SMX) degradation rate constants (k1) and half-lives (t1/2) for the various sludge-SMC ratios.
Fig. 1. Degradations of sulfamethoxazole (SMX) with spent mushroom compost (SMC), SMC extract (SE) and extract-containing microcapsule (MC) in the sludge. B, sterile control; C, inoculated control; , SE; ;, MC and △, SMC.
▪
Sluge-SMC ratio (W/w)
k1 (d1)
t1/2 (d)
ra
1:0.5 1:1 1:1.5 1:2
0.182 0.533 0.239 0.133
3.8 1.3 2.9 5.2
0.921 0.928 0.910 0.966
a
r ¼ correlation coefficient.
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Table 2 Degradation rate constants (k1) and half-lives (t1/2) of the three sulfonamide antibiotics present individually or simultaneously in the sludge-SMC mixtures. Treatments
k1 (d1)
t1/2 (d)
ra
SMXb SDMb SMZb SMXc SDMc SMZc
0.533 0.315 0.217 0.257 0.178 0.139
1.3 2.2 3.2 2.7 3.9 5.0
0.923 0.839 0.854 0.951 0.942 0.961
a b c
r ¼ correlation coefficient. Individually. Simultaneously. Fig. 3. Percentages of total organic content (TOC) remaining in the sludge after the six additions of the three SAs in the bioreactor experiments over 82 d of incubation. Two hundred milligrams per kilogram of the SAs were added at the first to third times, and 500 mg kg1 mixtures were added at the fourth to sixth times. C, with SMC; B, without SMC.
These results are similar to those from our previous study of the effects of acclimation on the degradations of three tetracyclines in river sediment (Chang and Ren, 2015). The addition of SMC affected the microbial populations and thus enhanced the SA removal from the sludge. The studies were further extended with TOC measurements to demonstrate the apparent mineralization of these chemicals in the bioreactors. The percentages remaining TOC after the six additions of the three SA mixtures are illustrated in Fig. 3. In the sludge alone, the remaining TOC percentages were 35.3%, 29.6%, 23.50%, 22.2%, 19.1%, and 12.2% on d 12, 26, 45, 57, 68 and 82, respectively. However, in the sludge with SMC, the remaining TOC percentages were 10.2%, 8.1%, 7.0%, 6.5%, 6.0%, and 2.0% on d 12, 26, 45, 57, 68 and 82, respectively. The correlations of the SAs with the remaining TOC proportions were examined for the six additions of the three-SA mixture. In the sludge alone, the Spearman correlation coefficients for the remaining TOC proportions with the SMX, SMZ and SDM were 0.73 (p ¼ 1.8E-4), 0.75 (p ¼ 9.5E-5) and 0.74 (p ¼ 1.3E-4), respectively. In the sludge with SMC, the Fig. 2. Percentages of remaining SAs in the bioreactors. The chemical structures of the three SAs used in this study (A). The remaining percentages after six additions in the bioreactors during the 82 d experiments with sludge alone (B) and with sludge with SMC (C). Two hundred milligrams per kilogram SA mixtures were added at the first to third times, and 500 mg kg1 SA mixtures were added at the fourth to sixth times. C, SMX; B, SMZ; ;, SDM.
sludge alone, the remaining proportions of SMX, SDM and SMZ were 0.2%, 3.4% and 2.2% on d 82, respectively (Fig. 2B). However, in the sludge with SMC, SMX and SDM were completely removed on d 12, 26, 45, 57, 68 and 82, but the remaining proportions of SMZ were 10.1%, 8.1%, 3.7%, 2.0%, ND and ND on d 12, 26, 45, 57, 68 and 82, respectively (Fig. 2C). The removal rates were higher with than without SMC in the sludge. The order of the removal rates in the sludge was also SMX > SDM > SMZ. Fig. 2C also revealed that the SA removal rates were enhanced with SMC when the SAs were added at six times. Specifically, with the fourth to sixth additions of 500 mg kg1 of the SAs, the removal rates of the three SAs were not significantly different. The three SAs may have similar molecular structures and physicochemical properties (García-Gal an et al., 2012). The SMC may contain SAdegrading bacteria that might enhance the removal of SAs, and the second to sixth additions of the SAs might have increase the SAdegrading activities of the microorganisms. The solutions in the bioreactor experiments were aerated with an air diffuser and agitated with a stirrer, which enhanced the removal rate of the SAs.
Fig. 4. Detrended correspondence analysis. Data from the bioreactor experiments. S and M indicate sludge alone and sludge with SMC, respectively. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, and 12 represent d 0, 6, 12, 15, 26, 31, 45, 48, 57, 61, 68, and 82, respectively.
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Fig. 5. Major bacterial genera in the bioreactor experiments. (A) G1 bacterial genera present in higher proportions in the experimental samples S1-S3. (B) G2 bacterial genera present in higher proportions in the experimental samples S4-S12. (C) G3 bacterial genera present in higher proportions in the experimental samples M1-M3. (B) G4 bacterial genera present in higher proportions in the experimental samples M4-M12. S and M indicate sludge alone and sludge with SMC, respectively. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, and 12 represent d 0, 6, 12, 15, 26, 31, 45, 48, 57, 61, 68, and 82, respectively. The red stars indicate bacterial genera that have been reported to be involved in SA degradation. The red “@”s indicate bacterial genera that have been reported to be involved in the degradation of other aromatic hydrocarbons.
corresponding correlation coefficients for SMX, SMZ and SDM were 0.76 (p ¼ 5.6E-5), 0.95 (p ¼ 4.7E-11) and 0.80 (p ¼ 1.2E-5), respectively. These results revealed strong correlations between the remaining TOC and SAs in the sludge. These chemicals were better mineralized in the sludge with SMC than in the sludge alone. 3.3. Bacteria associated with SA degradation in the bioreactor experiments A total of 115,772 16S rRNA gene sequences were produced from the bioreactor experiments. The differences in the bacterial community compositions in the bioreactor samples were analysed with detrended correspondence analysis (DCA; Fig. 4). Four large clusters were identified. The first cluster represents the sludge alone
experiments S1eS3 (0e12 d), the second cluster represents the sludge alone experiments S4eS12 (26e82 d), the third cluster represents the sludge with SMC experiments M1-M3 (0e12 d), and the fourth cluster represents the sludge with SMC experiments M4M12 (26e82 d). These results suggest that the bacterial community compositions were substantially different between the sludge alone and sludge with SMC experiments. Moreover, the compositions in terms of major bacterial genera differed greatly between the early and late stages of the experiments. Two major factors may have influenced the bacterial compositions during the experimental periods. First, the nutrient or carbon sources differed between the sludge alone and sludge with SMC experiments. The second factor was the addition of the SAs. The bacterial genera that were identified in the late stage of the experiments might represent
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bacteria that are resistant to antibiotics. Group G1 included 26 bacterial genera that were present in greater proportions in the group experimental samples S1eS3 than the other sample groups (Fig. 5A). Ten genera (i.e., Bacteroides, Bdellovibrio, Cellulomonas, Chryseobacterium, Diaphorobacter, Dokdonella, Flavobacterium, Hydrogenophaga, Phenylobacterium, and Stenotrophomonas) have been reported to be associated with the degradation of aromatic hydrocarbons (Kasai et al., 2002; AlonsoGutierrez et al., 2009; Urszula et al., 2009; Zhang et al., 2010; Gan et al., 2011; Shrestha et al., 2013; Tian et al., 2014; Wang et al., 2014). Group G2 included 16 bacterial genera that were present in greater proportions in the group experimental samples S4eS12 than the other sample groups (Fig. 5B). Acinetobacter isolated from marine environments has been reported to be involved in sulfapyridine and sulfathiazole degradation (Zhang et al., 2012). Pseudomonas isolated from activated sludge has been reported to be involved in SMX degradation (Herzog et al., 2013). Another five genera (i.e., Arthrobacter, Clostridium, Mycobacterium, Rhodanobacter, and Sphingomonas) have been reported to be associated with the degradation of other aromatic hydrocarbons (Kasai et al., 2002; Alonso-Gutierrez et al., 2009; Uhlik et al., 2012; Ren et al., 2015; Xiong et al., 2015). Group G3 included ten bacterial genera that were present in greater proportions in the group experimental samples M1-M3 than the other sample groups (Fig. 5C). Four genera (i.e., Paenibacillus, Planctomyces, Methanobacterium, and Methanosaeta) have been reported to be associated with the degradation of aromatic hydrocarbons (Berdugo-Clavijo et al., 2012; Al-Bader et al., 2013; Hou et al., 2015; Zhuang et al., 2015). Group G4 included seven bacterial genera that were present in greater proportions in the group experimental samples M4-M12 than the other sample groups (Fig. 5D). Two genera (i.e., Nitrosomonas and Steroidobacter) have been reported to be associated with the degradation of aromatic hydrocarbons (Chang et al., 2002; Cebron et al., 2015). In total, 23 of the 59 identified bacterial genera have previously been reported to be associated with aromatic hydrocarbon degradation. Two of the 23 bacterial genera have been reported to be SAdegrading bacteria. Acinetobacter and Pseudomonas represent major bacterial communities that are involved in SAs degradation in sludge. Although there is currently no relevant literature, the other 21 bacterial genera might degrade SAs. These strains have persistently existed in sludge contaminated with SAs and thus may have gradually been domesticated and evolved into species that can degrade contaminants in sludge.
4. Conclusion This study assessed the degradation of three SAs (SMX, SDM and SMZ) and changes in microbial communities. The major findings are as follows: (1) Microbial degradation is a major process of the removal of SAs from sludge. (2) SMC contains enzymes, bacteria, and fungi that all play roles in the degradation of SAs. (3) The SA removal rates were enhanced by the addition of SMC at six times. (4) Four bacterial genera were identified in the different settings and stages of the bioreactor experiments. (5) Acinetobacter and Pseudomonas represent major bacterial communities that are involved in SAs degradation in sludge. (6) The combination of SMC with sludge provides a good solution for SA removal.
Acknowledgments This research was supported by the Ministry of Science and Technology, Republic of China, Taiwan (grant no. MOST 104-2313B-031-001-MY3 and MOST 104-2632- B-031- 001). Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.chemosphere.2016.02.064. References Al-Bader, D., Eliyas, M., Rayan, R., Radwan, S., 2013. Subsurface associations of Acaryochloris-related picocyanobacteria with oil-utilizing bacteria in the Arabian Gulf water body: promising consortia in oil sediment bioremediation. Microb. Ecol. 65, 555e565. Alonso-Gutierrez, J., Figueras, A., Albaiges, J., Jimenez, N., Vinas, M., Solanas, A.M., Novoa, B., 2009. Bacterial communities from shoreline environments (Costa Da Morte, northwestern Spain) affected by the prestige oil spill. Appl. Environ. Microbiol. 75, 3407e3418. Baldrian, P., 2006. Fungal laccases occurrence and properties. FEMS Microbiol. Rev. 30, 215e242. 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bacterial consortia under four different redox and temperature conditions. Appl. Microbiol. Biotechnol. 99, 1451e1461. Xu, M., Chen, X., Qiu, M., Zeng, X., Xu, J., Deng, D., Sun, G., Li, X., Guo, J., 2012. Barcoded pyrosequencing reveals the responses of PBDE-degrading microbial communities to electron donor amendments. PLoS One 7, e30439. Yang, S.F., Lin, C.F., Lin, A.Y.C., Hong, P.K.A., 2011. Sorption and biodegradation of sulfonamide antibiotics by activated sludge: experimental assessment using batch data obtained under aerobic conditions. Water Res. 45, 3389e3397. Yang, C.W., Huang, H.W., Chao, W.L., Chang, B.V., 2015a. Bacterial communities associated with aerobic degradation of polybrominated diphenyl ethers from river sediment. Environ. Sci. Pollut. Res. 22, 3810e3819. Yang, Y., Wang, Z., He, T., Dai, Y., Xie, S., 2015b. Sediment bacterial communities associated with anaerobic biodegradation of bisphenol-A. Microb. Ecol. 70, 97e104. Zhang, T., Ren, H.F., Liu, Y., Zhu, B.L., Liu, Z.P., 2010. A novel degradation pathway of chloroaniline in Diaphorobacter sp. PCA039 entails initial hydroxylation. World J. Microbiol. Biotechnol. 26, 665e673. Zhang, W.W., Wen, Y.Y., Niu, Z.L., Yin, K., Xu, D.X., Chen, L.X., 2012. Isolation and characterization of sulfonamide-degrading bacteria Escherichia sp. HS21 and Acinetobacter sp. HS51. World J. Microbiol. Biotechnol. 28, 447e452. Zhuang, L., Tang, J., Wang, Y., Hu, M., Zhou, S., 2015. Conductive iron oxide minerals accelerate syntrophic cooperation in methanogenic benzoate degradation. J. Hazard. Mater. 293, 37e45.
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Biodegradation of sulfonamide antibiotics in sludge Chu-Wen Yang, Wan-Chun Hsiao, Bea-Ven Chang* Department of Microbiology, Soochow University, Taipei, Taiwan
h i g h l i g h t s This study investigated sulfonamides biodegradation in sludge. The removal rates of sulfonamides of were enhanced by addition of spent mushroom compost at six times. Acinetobacter and Pseudomonas were major bacteria involved in sulfonamides degradation in sludge.
a r t i c l e i n f o
a b s t r a c t
Article history: Received 10 November 2015 Received in revised form 15 February 2016 Accepted 15 February 2016 Available online xxx
Sulfonamide antibiotics are widely used in human and veterinary medicine. This study assessed the degradation of three sulfonamides (100 mg kg1 each of sulfamethoxazole, sulfadimethoxine and sulfamethazine) and changes in the microbial communities of sewage sludge. Sulfamethoxazole degradation was enhanced by spent mushroom compost (SMC), SMC extract, and extract-containing microcapsules in the sludge. The degradation of sulfonamides in sludge and SMC mixtures occurred in the order of sulfamethoxazole > sulfadimethoxine > sulfamethazine. Bioreactor experiments revealed that the sulfonamides removal rates in sludge with SMC were greater than those in sludge alone. The sulfonamides removal rates were enhanced by the addition of SMC for six time additions. The sulfonamides concentrations were 200 and 500 mg kg1 for the first to third additions and the fourth to sixth additions, respectively. With the high correlations between TOC and the proportions of sulfonamides remaining in sludge, sulfonamides may be mineralized to a greater extent with SMC in sludge than in sludge alone. Four bacterial genera were identified from the different settings and stages of the bioreactor experiments. Acinetobacter and Pseudomonas were major bacterial communities that were responsible for sulfonamide degradation in sludge. © 2016 Published by Elsevier Ltd.
Handling Editor: Shane Snyder Keywords: sulfonamide antibiotics Degradation Spent mushroom compost Sludge
1. Introduction Antibiotics have been recently been investigated as a source of emerging environmental contaminants. These antibiotics may exert selective pressure that favours resistant bacteria (Schwartz et al., 2003), which are a major public health concern due to the increased occurrence of associated clinical infections. Sulfonamide antibiotics (SAs) exhibit a wide spectrum of action against most gram-positive and many gram-negative microorganisms. SAs inhibit the proliferation of bacteria by acting as competitive inhibitors of p-aminobenzoic acid in the folic acid metabolism cycle (Sukul and Spiteller, 2006). SAs that are excreted by humans or animals can enter wastewater treatment plants (WWTPs) through
* Corresponding author. E-mail address: [email protected] (B.-V. Chang).
the sewage system (Ingerslev and Halling-Sørensen, 2000). Conventional wastewater treatment facilities are unable to efficiently remove SA residues (Barber et al., 2009). SAs have been found in the sludges of most WWTPs (Gobel et al., 2005; Nieto et al., 2010). Three SAs, i.e., sulfamethoxazole (SMX), sulfadimethoxine (SDM), and sulfamethazine (SMZ), have been detected in the sludge of a WWTP in Taiwan (Yang et al., 2011). Microbial degradation has been proposed to serve as one major process for the removal of antibiotics from contaminated sludge. Pleurotus eryngii is one of the most widespread white rot fungi in the world (Lee et al., 2004). Spent mushroom compost (SMC) is a waste product of the mushroom industry (e.g., P. eryngii cultivation factories) and contains many residual enzymes that can be used to degrade many organic toxic chemicals (Lau et al., 2003; Li et al., 2010). SMC contains bacteria that can degrade many organic toxic chemicals, such as nonylphenol, tetrabromobisphenol-A and tetracycline (Hsu et al., 2013; Chang and Ren, 2015).
http://dx.doi.org/10.1016/j.chemosphere.2016.02.064 0045-6535/© 2016 Published by Elsevier Ltd.
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Metagenomic studies with next-generation sequencing allow for the study of microbial diversity in environmental samples with genus-level resolution (Xu et al., 2012; Yang et al., 2015a, b). However, little is known about the microbial communities that are involved in SAs degradation in sludge. The aim of this study was to compare the degradations of SMX according to various additives, various sludge-to-SMC ratios, and the individual or simultaneous presence of three SAs. The degradation rates of SAs and the bacterial community changes in the sludge in bioreactor were also evaluated. The target compounds were SMX, SDM, and SMZ. 2. Materials and methods 2.1. Chemicals SMX, SDM and SMZ of 99.0% purity were obtained Sigma Chemical Co. (St. Louis, MO). The solvents were from Mallinckrodt (Paris, KY), and all other chemicals were from Sigma Chemical Co. Individual stock solutions of SMX, SDM and SMZ were dissolved in 0.03 M NaOH to establish concentrations of 5000 mg L1 and then diluted with water before use. 2.2. Sampling and medium The sludge was a semi-solid slurry that was produced as sewage sludge from the Dihus wastewater treatment plant in Taipei, Taiwan. The SMX, SDM, and SMZ concentrations in sludge were 5.9, 9.4, and 5.2 mg kg1, respectively. The basal medium contained the following (in mg L1): K2HPO4, 65.3; KH2PO4, 25.5; Na2HPO4‧ 12H2O, 133.8; NH4Cl, 5.1; CaCl2, 82.5; MgSO4‧7H2O, 67.5; and FeCl3‧ 6H2O, 0.75. The pH of the basal medium was adjusted to 7.2 before autoclaving at 121 C for 20 min. 2.3. Preparation of the SMC, SMC extract and extract-containing microcapsule The SMC of P. eryngii was produced at a mushroom cultivation factory in Chiayi, Taiwan. The SMC extract (SE) was extracted from the 120 g SMC via the use of 600 mL sodium acetate buffer (pH 5.0) for 3 h at 4 C. The samples were centrifuged (10,000 g 10 min), and the supernatants were partially purified by precipitation with ammonium sulfate and dialysis. SMC is a potential source of ligninolytic enzymes (Li et al., 2010). Most ligninolytic fungal species produce constitutively at least one laccase isoenzyme and laccases are also dominant among ligninolytic enzymes in the environment (Baldrian, 2006). We measured the laccase activity in the sludge with SMC, MC, and SE. The laccase activity was measured by spectrophotometry at 405 nm. The reactive mixture contained 0.5 mL of enzyme supernatant, 0.25 mL of 100 mM glycine buffer (pH 3.0), and 0.25 mL of 4 mM ABTS. The enzyme reactive mixtures were incubated at 25 C for 5 min before measurement. For the evaluation of the laccase activity, one activity unit was defined as the amount of enzyme necessary to oxidize 1 mmol of substrate per min. Each value presented in this paper represents the mean of three replicates. Alginate solution was made by dissolving sodium alginate (4 wt.%) in 0.9 wt.% sodium chloride with stirring for 1 h at room temperature. SE was then added to the alginate solution. The final concentration of alginate was 2.0 wt.% in the mixture solution. An electrostatic droplet generator was used to fabricate the capsules (Hsu et al., 2013). The mixture solution was drawn into a 10-mL syringe fitted with a 23G needle and attached to a syringe pump that provided a steady solution flow rate. The solution was pumped through the needle at a fixed feed rate (25.2 mL h1) and fixed voltage (12 kV) into a gently agitated aqueous solution of calcium
chloride (1.5 wt.%) to form an extract-containing microcapsule (MC) of 250 mm in diameter for the experiments. 2.4. Experimental design The batch experiments involved 125-mL serum bottles containing 40 mL medium, 5 g sludge, 5 g SMC and the SAs. We first compared three additives (i.e., SMC, SE and MC) and various sludgeto-SMC ratios (i.e., 1:0.5, 1:1, 1:1.5 and 1:2) in terms of the degradation of SMX (200 mg kg1) in the sludge. We also compared the three SAs (200 mg kg1 each of SMX, SDM and SMZ) that were present individually or simultaneously in the sludge-SMC mixtures. Inoculated controls were treated with sludge as the inoculum and incubated with shaking at 30 C in the dark. Sterile controls were prepared by autoclaving the sludge with SMC, SE or MC at 121 C for 20 min on 3 consecutive d. Each treatment was performed in triplicate. Samples were periodically collected to measure the residual SAs. The bioreactor was designed as previously described (Chang and Ren, 2015) and was aerated via the use of an air diffuser. The materials were agitated with a stirrer. Bioreactor A was filled with 1800 mL medium and 200 g sludge. Bioreactor B was filled with 1600 mL medium, 200 g sludge, and 200 g SMC. The three SAs were added, and when the SMX decreased to below the detection limit (ND), the three SAs were added again. The SA concentrations were 200 and 500 mg kg1 for the first to third additions and the fourth to sixth additions, respectively. The entire experiments experiment lasted 82 d. Samples were taken periodically for analyses of the residual SAs, total organic contents (TOCs) and microbial communities. 2.5. Chemical analysis SA determination was performed with an Agilent 1260 HPLC system equipped with a 4.6 250-mm column (Zorbax Eclipse Plus C18, Agilent) with a photodiode array detector monitoring at 270 nm. The mobile phase was acetonitrile and water (with 0.1% formic acid) at 70%:30%. Flow rate was 1 mL/min. The 0.5 mL aqueous phase samples were collected, and equal volumes of extraction solution were added with water (containing 0.1% formic acid): acetonitrile: methanol ¼ 10:3:1, vortexed well and centrifuged at 10,000 rpm for 5 min. The supernatant was filtered with a 0.22-mm filter into a vial for HPLC. The recovery percentages for SMX, SDM and SMZ were 97.5%, 97.4%, and 96.4%, respectively. The detection limits for SMX, SDM, and SMZ were all 0.1 mg L1. The TOCs were measured using a TOC Analyzer (OI Analytical 1030W, USA). 2.6DNA extraction, PCR, and pyrosequencing Total DNA was extracted from each experimental sample using the PowerSoil DNA Isolation Kit (MO BIO Laboratories). Partial 16S rRNA genes containing variable V5eV8 regions were amplified from the extracted DNA. The sequence for the 50 primer included a 454 pyrosequencing adaptor, a unique 4-mer tag for each sample and 787F (50 -ATTAGATACCCNGGTAG-30 ) for the 16S rRNA genes. The sequence of the 30 primer included a 454 pyrosequencing adaptor and 1391R (50 -ACGGGCGGTGWGTRC-30 ) for the 16S rRNA genes. The PCR reactions were performed as we have previously described (Yang et al., 2015a). Pyrosequencing was performed at the Genome Center of the National Yang-Ming University, Taiwan, ROC with a GS Junior System (Roche Diagnostics Corp., CT, USA). The sequence data produced in this study are provided as Supplementary material.
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2.7. Data analysis The calculations for the SAs and TOC remaining percentages were performed as follows: [%] ¼ (residue substrate concentration/ initial substrate concentration) 100. The degradation data for the SAs collected in this study were well fit with first-order kinetics; i.e., S ¼ S0exp (-k1t), t1/2 ¼ ln 2/k1, where S0 is the initial concentration, S is the substrate concentration, t is the time, and k1 is the degradation rate constant. The SA removal rate [%] ¼ [1e (residue substrate concentration/initial substrate concentration)] 100. Significant differences were accepted at p < 0.05. The 16S rRNA gene sequence data were first analysed using Chimera Check to remove the chimeric sequences. The sequences that passed the Chimera Check were analysed with the RPD classifier in the RDPipeline (http://pyro.cme.msu.edu/) for phylogenetic classification. We used the highest similarity (95%) provided by the classifier software with the Ribosomal Database Project Web site for the groups of bacteria. The differences in bacterial community compositions between the samples were analysed with the Detrended Correspondence Analysis (DCA) function of the vegan package of R. Spearman correlation coefficients were computed using the cor.test function of R. The differences in the distribution of the bacterial genera were identified with ManneWhitney U tests using the wilcox.test function in R. 3. Results and discussion 3.1. Effect of various factors on the degradation of SAs The three SAs in the sterile controls were first examined at the end of the 8-d incubation. The remaining percentages of the three SAs ranged from 91.9% to 98.8% in the soil-sludge mixtures. Therefore, the SA degradation that occurred in all of the following experiments was due to microbial action. We first compared the effects of the three additives (SMC, MC and SE) on SMX degradation after 8 d of incubation in the sludge. As illustrated in Fig. 1, the remaining percentage of SMX was 58.3% without the three additives (i.e., SMC, SE or MC). SMX was completely degraded following the addition of SMC, but the percentages of SMX remaining following the additions of MC and SE were 3.2% and 1.8%, respectively. The SMX degradation data were well fit with first-order kinetics. The degradation half-lives (t1/2) of SMX were 1.3, 2.1 and 2.9 d in the sludge for SMC, MC and SE, respectively. The SMX degradation rates in the sludge occurred in the order of SMC > MC > SE. Thus, SMX degradation was enhanced
3
by the three additives (i.e., SMC, MC and SE), and SMC yielded a greater SMX degradation rate than the other additives to the sludge. Thus, SMC was used in all of the following experiments. SMC contains ligninolytic enzymes that can enhance SMX degradation (Lau et al., 2003). The laccase activities in the sludge with SMC, SE and MC were initially 878.4 ± 18.1, 862.1 ± 13.8, and 850.6 ± 18.2 UL1, respectively, and 860.4 ± 19.6, 672.1 ± 15.9, and 826.7 ± 21.2 UL1, respectively, after 10 d of incubation. The effects of the three additives on laccase activity occurred in the order SMC > MC > SE. SMC contains enzymes, bacteria, and fungi that all might play roles in the degradation of SMX. Additionally, the SMX degradation rate was higher with MC than with SE in the sludge. Removal with MC was better than SE in terms of improving the tolerance to the environmental loadings. Similar trends were revealed in our previous study, which demonstrated more efficient removal of nonylphenol with immobilized SE than the suspended form (Hsu et al., 2013). The SMX degradation data for the various sludge-to-SMC ratios were well fit with first-order kinetics as presented in Table 1. When the sludge-to-SMC mixture ratio was 1:1, the SMX degradation rate was greater than the rates observed in the experiments with mixture ratios of 1:0.5. When excessive amounts of SMC were added, and the mixture ratio was further increased (i.e., 1:1.5 and 1:2), SMX degradation was inhibited. Therefore, the optimal mixture ratio was 1:1. This finding agrees with that of Namkoong et al. (2002), who reported that while the addition of organic supplements increases the rate of contaminant degradation, excessive supplementation inhibits degradation. When the added carbon source is preferentially degraded relative to the target compounds, the microbial degradation of the target contaminants may be inhibited (Chang et al., 2009). Therefore, we used a sludgeto-SMC ratio of 1:1 in the following experiments. The degradation data for the individually and simultaneously presence of the three SAs in the sludge-SMC mixtures are presented in Table 2. The order of the degradation of the three SAs in the sludge-SMC mixtures was SMX > SDM > SMZ. The chemical structures of SMX, SDM and SMZ are illustrated in Fig. 2A. Functional groups may contribute electronegativity effects that hinder degradation by affecting the interaction between the contaminant and the microbes. With increasing electronegativities of the substituents, SA degradation rates decrease (Pepper et al., 2015). Additionally, when the three SAs were simultaneously present in the sludge-SMC mixture, the degradation rates decreased (Table 2). The rates were higher when the three SAs were present individually than when they were present simultaneously. Higher SA concentrations may affect microbial activity and inhibit SA degradation. 3.2. Bioreactor experiments The removals of the three SAs from the sludge alone and the sludge with SMC in the bioreactors are illustrated in Fig. 2. The concentrations of three SAs were 200 mg kg1 for the first to third additions and 500 mg kg1 for the fourth to sixth additions. In the
Table 1 Sulfamethoxazole (SMX) degradation rate constants (k1) and half-lives (t1/2) for the various sludge-SMC ratios.
Fig. 1. Degradations of sulfamethoxazole (SMX) with spent mushroom compost (SMC), SMC extract (SE) and extract-containing microcapsule (MC) in the sludge. B, sterile control; C, inoculated control; , SE; ;, MC and △, SMC.
▪
Sluge-SMC ratio (W/w)
k1 (d1)
t1/2 (d)
ra
1:0.5 1:1 1:1.5 1:2
0.182 0.533 0.239 0.133
3.8 1.3 2.9 5.2
0.921 0.928 0.910 0.966
a
r ¼ correlation coefficient.
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Table 2 Degradation rate constants (k1) and half-lives (t1/2) of the three sulfonamide antibiotics present individually or simultaneously in the sludge-SMC mixtures. Treatments
k1 (d1)
t1/2 (d)
ra
SMXb SDMb SMZb SMXc SDMc SMZc
0.533 0.315 0.217 0.257 0.178 0.139
1.3 2.2 3.2 2.7 3.9 5.0
0.923 0.839 0.854 0.951 0.942 0.961
a b c
r ¼ correlation coefficient. Individually. Simultaneously. Fig. 3. Percentages of total organic content (TOC) remaining in the sludge after the six additions of the three SAs in the bioreactor experiments over 82 d of incubation. Two hundred milligrams per kilogram of the SAs were added at the first to third times, and 500 mg kg1 mixtures were added at the fourth to sixth times. C, with SMC; B, without SMC.
These results are similar to those from our previous study of the effects of acclimation on the degradations of three tetracyclines in river sediment (Chang and Ren, 2015). The addition of SMC affected the microbial populations and thus enhanced the SA removal from the sludge. The studies were further extended with TOC measurements to demonstrate the apparent mineralization of these chemicals in the bioreactors. The percentages remaining TOC after the six additions of the three SA mixtures are illustrated in Fig. 3. In the sludge alone, the remaining TOC percentages were 35.3%, 29.6%, 23.50%, 22.2%, 19.1%, and 12.2% on d 12, 26, 45, 57, 68 and 82, respectively. However, in the sludge with SMC, the remaining TOC percentages were 10.2%, 8.1%, 7.0%, 6.5%, 6.0%, and 2.0% on d 12, 26, 45, 57, 68 and 82, respectively. The correlations of the SAs with the remaining TOC proportions were examined for the six additions of the three-SA mixture. In the sludge alone, the Spearman correlation coefficients for the remaining TOC proportions with the SMX, SMZ and SDM were 0.73 (p ¼ 1.8E-4), 0.75 (p ¼ 9.5E-5) and 0.74 (p ¼ 1.3E-4), respectively. In the sludge with SMC, the Fig. 2. Percentages of remaining SAs in the bioreactors. The chemical structures of the three SAs used in this study (A). The remaining percentages after six additions in the bioreactors during the 82 d experiments with sludge alone (B) and with sludge with SMC (C). Two hundred milligrams per kilogram SA mixtures were added at the first to third times, and 500 mg kg1 SA mixtures were added at the fourth to sixth times. C, SMX; B, SMZ; ;, SDM.
sludge alone, the remaining proportions of SMX, SDM and SMZ were 0.2%, 3.4% and 2.2% on d 82, respectively (Fig. 2B). However, in the sludge with SMC, SMX and SDM were completely removed on d 12, 26, 45, 57, 68 and 82, but the remaining proportions of SMZ were 10.1%, 8.1%, 3.7%, 2.0%, ND and ND on d 12, 26, 45, 57, 68 and 82, respectively (Fig. 2C). The removal rates were higher with than without SMC in the sludge. The order of the removal rates in the sludge was also SMX > SDM > SMZ. Fig. 2C also revealed that the SA removal rates were enhanced with SMC when the SAs were added at six times. Specifically, with the fourth to sixth additions of 500 mg kg1 of the SAs, the removal rates of the three SAs were not significantly different. The three SAs may have similar molecular structures and physicochemical properties (García-Gal an et al., 2012). The SMC may contain SAdegrading bacteria that might enhance the removal of SAs, and the second to sixth additions of the SAs might have increase the SAdegrading activities of the microorganisms. The solutions in the bioreactor experiments were aerated with an air diffuser and agitated with a stirrer, which enhanced the removal rate of the SAs.
Fig. 4. Detrended correspondence analysis. Data from the bioreactor experiments. S and M indicate sludge alone and sludge with SMC, respectively. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, and 12 represent d 0, 6, 12, 15, 26, 31, 45, 48, 57, 61, 68, and 82, respectively.
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Fig. 5. Major bacterial genera in the bioreactor experiments. (A) G1 bacterial genera present in higher proportions in the experimental samples S1-S3. (B) G2 bacterial genera present in higher proportions in the experimental samples S4-S12. (C) G3 bacterial genera present in higher proportions in the experimental samples M1-M3. (B) G4 bacterial genera present in higher proportions in the experimental samples M4-M12. S and M indicate sludge alone and sludge with SMC, respectively. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, and 12 represent d 0, 6, 12, 15, 26, 31, 45, 48, 57, 61, 68, and 82, respectively. The red stars indicate bacterial genera that have been reported to be involved in SA degradation. The red “@”s indicate bacterial genera that have been reported to be involved in the degradation of other aromatic hydrocarbons.
corresponding correlation coefficients for SMX, SMZ and SDM were 0.76 (p ¼ 5.6E-5), 0.95 (p ¼ 4.7E-11) and 0.80 (p ¼ 1.2E-5), respectively. These results revealed strong correlations between the remaining TOC and SAs in the sludge. These chemicals were better mineralized in the sludge with SMC than in the sludge alone. 3.3. Bacteria associated with SA degradation in the bioreactor experiments A total of 115,772 16S rRNA gene sequences were produced from the bioreactor experiments. The differences in the bacterial community compositions in the bioreactor samples were analysed with detrended correspondence analysis (DCA; Fig. 4). Four large clusters were identified. The first cluster represents the sludge alone
experiments S1eS3 (0e12 d), the second cluster represents the sludge alone experiments S4eS12 (26e82 d), the third cluster represents the sludge with SMC experiments M1-M3 (0e12 d), and the fourth cluster represents the sludge with SMC experiments M4M12 (26e82 d). These results suggest that the bacterial community compositions were substantially different between the sludge alone and sludge with SMC experiments. Moreover, the compositions in terms of major bacterial genera differed greatly between the early and late stages of the experiments. Two major factors may have influenced the bacterial compositions during the experimental periods. First, the nutrient or carbon sources differed between the sludge alone and sludge with SMC experiments. The second factor was the addition of the SAs. The bacterial genera that were identified in the late stage of the experiments might represent
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bacteria that are resistant to antibiotics. Group G1 included 26 bacterial genera that were present in greater proportions in the group experimental samples S1eS3 than the other sample groups (Fig. 5A). Ten genera (i.e., Bacteroides, Bdellovibrio, Cellulomonas, Chryseobacterium, Diaphorobacter, Dokdonella, Flavobacterium, Hydrogenophaga, Phenylobacterium, and Stenotrophomonas) have been reported to be associated with the degradation of aromatic hydrocarbons (Kasai et al., 2002; AlonsoGutierrez et al., 2009; Urszula et al., 2009; Zhang et al., 2010; Gan et al., 2011; Shrestha et al., 2013; Tian et al., 2014; Wang et al., 2014). Group G2 included 16 bacterial genera that were present in greater proportions in the group experimental samples S4eS12 than the other sample groups (Fig. 5B). Acinetobacter isolated from marine environments has been reported to be involved in sulfapyridine and sulfathiazole degradation (Zhang et al., 2012). Pseudomonas isolated from activated sludge has been reported to be involved in SMX degradation (Herzog et al., 2013). Another five genera (i.e., Arthrobacter, Clostridium, Mycobacterium, Rhodanobacter, and Sphingomonas) have been reported to be associated with the degradation of other aromatic hydrocarbons (Kasai et al., 2002; Alonso-Gutierrez et al., 2009; Uhlik et al., 2012; Ren et al., 2015; Xiong et al., 2015). Group G3 included ten bacterial genera that were present in greater proportions in the group experimental samples M1-M3 than the other sample groups (Fig. 5C). Four genera (i.e., Paenibacillus, Planctomyces, Methanobacterium, and Methanosaeta) have been reported to be associated with the degradation of aromatic hydrocarbons (Berdugo-Clavijo et al., 2012; Al-Bader et al., 2013; Hou et al., 2015; Zhuang et al., 2015). Group G4 included seven bacterial genera that were present in greater proportions in the group experimental samples M4-M12 than the other sample groups (Fig. 5D). Two genera (i.e., Nitrosomonas and Steroidobacter) have been reported to be associated with the degradation of aromatic hydrocarbons (Chang et al., 2002; Cebron et al., 2015). In total, 23 of the 59 identified bacterial genera have previously been reported to be associated with aromatic hydrocarbon degradation. Two of the 23 bacterial genera have been reported to be SAdegrading bacteria. Acinetobacter and Pseudomonas represent major bacterial communities that are involved in SAs degradation in sludge. Although there is currently no relevant literature, the other 21 bacterial genera might degrade SAs. These strains have persistently existed in sludge contaminated with SAs and thus may have gradually been domesticated and evolved into species that can degrade contaminants in sludge.
4. Conclusion This study assessed the degradation of three SAs (SMX, SDM and SMZ) and changes in microbial communities. The major findings are as follows: (1) Microbial degradation is a major process of the removal of SAs from sludge. (2) SMC contains enzymes, bacteria, and fungi that all play roles in the degradation of SAs. (3) The SA removal rates were enhanced by the addition of SMC at six times. (4) Four bacterial genera were identified in the different settings and stages of the bioreactor experiments. (5) Acinetobacter and Pseudomonas represent major bacterial communities that are involved in SAs degradation in sludge. (6) The combination of SMC with sludge provides a good solution for SA removal.
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Please cite this article in press as: Yang, C.-W., et al., Biodegradation of sulfonamide antibiotics in sludge, Chemosphere (2016), http://dx.doi.org/ 10.1016/j.chemosphere.2016.02.064