Exploring abundance, diversity and variation of a

Environment International 117 (2018) 186–195

Contents lists available at ScienceDirect

Environment International journal homepage: www.elsevier.com/locate/envint

Exploring abundance, diversity and variation of a widespread antibiotic resistance gene in wastewater treatment plants

T

Ziyan Weia,c, Kai Fenga,c, Shuzhen Lia,d, Yu Zhangb,c, Hongrui Chenb,c, Huaqun Yine, Meiying Xuf, ⁎ Ye Denga,c, a

Key Laboratory of Environmental Biotechnology of CAS, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China State Key Laboratory of Environmental Aquatic Chemistry, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China c College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China d State Key Laboratory of Industrial Ecology and Environmental Engineering, Ministry of Education, School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China e School of Minerals Processing and Bioengineering, Central South University, Changsha 410083, China f State Key Laboratory of Applied Microbiology Southern China, Guangdong Institute of Microbiology, Guangzhou 510070, China b

A R T I C LE I N FO

A B S T R A C T

Keywords: 16S rRNA sul1 Degenerate primer design Quantitative PCR MiSeq sequencing

An updated sul1 gene sequence database was constructed and new degenerate primers were designed to better investigate the abundance, diversity, and variation of a ubiquitous antibiotic resistance gene, sul1, with PCRbased methods in activated sludge from wastewater treatment plants (WWTPs). The newly designed degenerate primers showed high specificity and higher coverage in both in-silico evaluations and activated sludge samples compared to previous sul1 primers. Using the new primers, the abundance and diversity of sul1 gene, together with 16S rRNA gene, in activated sludge from five WWTPs in summer and winter were determined by quantitative PCR and MiSeq sequencing. The sul1 gene was found to be prevalent and displayed a comparable abundance (0.081 copies per bacterial cell in average) to the total bacteria across all samples. However, compared to the significant seasonal and geographical divergences in the quantity and diversity of bacterial communities in WWTPs, there were no significant seasonal or geographical variations of representative clusters of sul1 gene in most cases. Additionally, the representative sul1 clusters showed fairly close phylogeny and there was no obvious correlation between sul1 gene and the dominant bacterial genera, as well as the int1 gene, suggesting that bacterial hosts of sul1 gene is not stable, the sul1 gene may be carried by mobile genetic elements, sometimes integrated with class 1 integrons and sometimes not. Thus mobile genetic elements likely play a greater role than specific microbial taxa in determining the composition of sul1 gene in WWTPs.

1. Introduction Antibiotic resistance is one of the greatest threats to public health (Oberlé et al., 2012), with wastewater treatment plants (WWTPs) acting as both important recipients and reservoirs of antibiotic resistant bacteria (ARB) and antibiotic resistance genes (ARGs) in urban environments. The activated sludge within WWTPs, containing diverse microbial species at high quantities and playing important roles in the biological treatment of sewage, is believed as ideal conditions for horizontal gene transfer (HGT) of ARGs amidst those highly active species (Munck et al., 2015; Yang et al., 2013). As antibiotics have been introduced into WWTPs via influent streams (Zhang and Zhang, 2011), the microbial communities present in these systems have responded to

the high selection pressure by becoming enriched in ARB with ARGs (Zhang et al., 2016). HGT is generally mediated by mobile genetic elements (MGEs), such as plasmids, transposons, and phages (Leplae et al., 2010). These MGEs could play an important role in the acquisition, expression, and dissemination of ARGs (Bennett, 2009). The sulfonamide resistance gene sul1, is one of the most widely spread ARGs with high abundance in various environments (Colomer-Lluch et al., 2014; Miller et al., 2016; Muziasari et al., 2017). As it is almost exclusively found on a wide range of conjugative plasmids (Heuer et al., 2012; Popowska and Krawczyk-Balska, 2013; Wu et al., 2010), the sul1 gene should be one of the representative ARGs that closely related to MGEs. Additionally, sul1 gene was regarded as naturally occurring in bacterial fractions of raw

⁎ Corresponding author at: CAS Key Laboratory for Environmental Biotechnology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, 18 Shuangqing Road, Haidian District, Beijing 100085, China. E-mail address: [email protected] (Y. Deng).

https://doi.org/10.1016/j.envint.2018.05.009 Received 16 February 2018; Received in revised form 24 April 2018; Accepted 3 May 2018 0160-4120/ © 2018 Published by Elsevier Ltd.


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2. Methods

wastewater, hence it is also proper to assess the persistence of ARGs under different conditions (Calero-Caceres and Muniesa, 2016). Previous investigations indicated the associations of sul1 gene with other ARGs (Hu et al., 2016), like aminoglycoside (Rosengren et al., 2009), beta-lactamase (Devanga Ragupathi et al., 2016), chloramphenicol (Gow et al., 2008), tetracycline (Boerlin et al., 2005; Sunde and Norström, 2006), and trimethoprim resistance genes (Hu et al., 2011). Therefore, sul1 gene could be a good candidate to estimate the occurrence of antibiotic resistance. A number of molecular methods have been used to detect and quantify the occurrence of ARGs in environmental samples with traditional PCR and quantitative PCR (qPCR) among the most commonly used approaches (Mu et al., 2015), as exemplified by studies on the abundance of quinolone resistance genes in wetlands (Cummings et al., 2011), chloramphenicol resistance genes in wastewater (Chen et al., 2016), sulfonamide resistance genes and tetracycline resistance genes in soils (Tang et al., 2015), sediments (Yang et al., 2016), and fresh waters (Xiong et al., 2015). In the meantime, high-throughput microarray (Sun et al., 2014; Zhang et al., 2013), high-throughput quantitative PCR (Su et al., 2015; Wang et al., 2014) and metagenomics (Yang et al., 2013) have also been gradually adopted to detect the ARGs in environmental samples, and are promising methods that may provide more comprehensive information for multiple ARGs. However, with the exception of metagenomic sequencing, the accuracy of all other PCRbased approaches largely depends on the adopted primers. Many primers used for amplification of ARGs were designed ten years earlier and have not undergone a rigorous re-assessment even though the number of available sequences has expanded tremendously in the intervening time. Minor biases in primers could excessively alter the pool of amplicons from environmental samples, thereby result in imprecise conclusions (Thomas et al., 2011). For instance, Antibiotic Resistance Genes Database (ARDB) (Liu and Pop, 2009), the most widely used sequence database for ARGs including sul1, was last updated in 2009 but is still used for the design and validation of many primers. Therefore, a more comprehensive database containing all known sul1 gene sequences is urgently needed. Microbial communities are known to exhibit seasonal and geographical variations in different environments (Durrer et al., 2017; Turki et al., 2017; Wang et al., 2016). For example, bacterial communities in biofilms and wastewater of WWTPs obtained in summer were found to be more abundant, complex, and variable than in winter, indicating the fluctuation of bacterial community structure existed between seasons in municipal wastewater treatment process (Turki et al., 2017). Geographical distance also generally plays a significant role in shifting the bacterial communities in WWTPs (Wang et al., 2016). For instance, the bacterial communities showed significant alterations across 26 biological WWTPs in China, and those alterations had close correlations with geographical distance (Wang et al., 2016). Accordingly, the quantities and diversities of ARGs could show dynamics and regional specificity similar to those seen in the larger bacterial community. In the present study, we chose the representative marker of antibiotic resistance, sul1 gene, employing qPCR and MiSeq amplicon sequencing to explore the distribution of both sul1 and 16S rRNA genes in activated sludge from WWTPs. We sought to address the two following questions. (1) Do the quantities and diversities of sul1 gene show the geographical and seasonal changes in WWTPs like microbial communities? (2) Is the change of sul1 gene associated with any specific bacterial taxa that could be its potential host? To answer these questions, we collected all currently available sul1 gene sequences from public databases and designed a new pair of universal degenerate primers in order to achieve higher specificity and coverage of sul1 gene compared to other primers in environmental samples. Based on the new primers, we further explored the abundance, diversity, existing status and phylogenetic structure of the representative ARG, sul1, to help illuminate the level of antibiotic resistance and dissemination mechanism of ARGs in WWTPs.

2.1. Sample collection Five urban sewage WWTPs from three Chinese cities including one from Beijing (BJ), two from Qingdao (QDN, QDS) and two from Wuxi (WXN, WXS), were sampled for this study. Detailed operational parameters of the WWTPs are summarized in Table S1. Activated sludge was sampled in triplicate from aerobic tank in secondary treatment in both December 2014 and June 2015. The samples in those two seasons were designated as BJ6 (June samples), BJ12 (December samples), QDN6, QDN12, QDS6, QDS12, WXN6, WXN12, WXS6 and WXS12. Wastewater parameters, including dissolved oxygen (DO), pH, temperature, and operational parameters including inflow and effluent of chemical oxygen demand (COD), total nitrogen (TN), total phosphorus (TP) were measured according to the Standard Method of Water and Wastewater Monitoring (Bureau, 2002) in triplicate and average values were listed in Table S2. 2.2. In silico design and assessment of sul1-targeted primers For the purpose of selecting preferable primer pairs for the amplification of sul1 gene, commonly used sul1-targeted primer pairs were collected and evaluated. At the same time, 323 non-redundant nucleotide sequences of the sul1 gene were downloaded as sul1-ARDB according to the protein sequences in ARDB (Liu and Pop, 2009), and their corresponding protein sequences were aligned by ClustalW with default parameters in MEGA 6.0 (Thompson et al., 1994). HMMER 3.0 program was used to build the hidden Markov model (HMM) and search the GenBank protein database (as available on Dec 30, 2016). The obtained sequences were screened according to the sequence annotations and HMMER e-values, such that only protein sequences clearly annotated as “sul1” or “sulfonamide resistance” with e-values < 10−2 were kept. The corresponding nucleotide sequences were downloaded and integrated into an updated sul1 gene sequence database. Thereafter, in order to design a new pair of sul1 primers with higher coverage, the DegePrime program (Hugerth et al., 2014) with the superiority of finding a degenerate oligomer of as high coverage as possible, was adopted to design degenerate primers with the aligned sul1 nucleotide sequences. Primer coverage was determined by BLASTn against both sul1-ARDB and the updated sul1 gene sequence database (Table S3). After comparison of the primers' coverage, the new primer pairs 51F (5′-AAATGCTGCGAGTYGGMKCA-3′) and 280R (5′-AACMACCAKCCTRCAGTCCG-3′) were reserved. In order to test the primer specificity, 21,176 nucleotide sequences of all ARGs were downloaded from ARDB. Based on the updated sequence database of sul1 gene and ARDB, the specificity of 51F and 280R was assessed locally by searching the primer sequences against these databases with MFEprimer program (Qu et al., 2012) to exclude the false positive to other ARGs. Moreover, the oligonucleotide properties of 51F and 280R were further calculated by OligoCalc (Kibbe, 2007) to exclude potential hairpin formation and self-annealing. 2.3. DNA extraction and quantification of sul1, int1 and 16S rRNA genes by qPCR Six DNA samples were extracted from 0.25 gram sediment (wet weight) of each WWTP in each sampling season (3 biological replicates × 2 experimental replicates) with a FastDNA SPIN kit for soil (Qbiogene, Solon OH). Quantitative PCR was performed to determine the abundance of sul1 gene, int1 gene, and bacterial 16S rRNA gene in total DNA samples with SYBR Green method. The commonly used primer sets sul1_1, sul1_2, sul1_3 and sul1_4 with relatively higher coverage (Table S3), as well as 51F and 280R were tested in activated sludge samples from Beijing WWTP. After this parallel comparison, only 51F and 280R were chosen for the quantification of sul1 gene in all 187


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MEGA 6.0, thereafter a phylogenetic tree was constructed by maximum likelihood method in MEGA 6.0. Correlations between abundance of sul1 gene and int1 gene, as well as between relative abundance of sul1clusters and dominant bacterial genera were assessed by using Pearson's correlation coefficient. These calculation tests were obtained by SPSS 17.0 software and P < 0.05 was considered to be significant.

samples. The int1 gene, encoding the integrase of type 1 integrons, was also quantified to explore the correlation between sul1 gene and class 1 integrons (Zhao et al., 2001), and 16S rRNA gene (Koike et al., 2007) was quantified to estimate the bacterial abundance. The qPCR reactions, total volume of 20 μL, contained 1× SYBR Green I, 2 μL of DNA template, and 0.3 μM of each primer. All qPCR assays were carried out in technical triplicates on each sample with parallel negative controls, so as to validate the specificity and sensitivity of the primers. The copy numbers of sul1 gene, int1 gene, and 16S rRNA gene were calculated immediately by relating cycle threshold values to standard curves (Table S4). Relative abundance of sul1 and int1 genes to 16S rRNA gene was calculated as copies per 16S rRNA gene copy. Detailed protocols are available in Text S1.

3. Results 3.1. Diversity of bacterial community in WWTPs To determine the composition of bacterial communities in all 60 samples from five WWTPs, 16S rRNA gene was amplified and sequenced by MiSeq sequencing. In total, 5756 OTUs were generated at 97% similarity and classified to 58 phyla, 131 classes, 220 orders, 269 families and 416 genera of microbial taxa, indicating a high diversity of bacteria present in activated sludge microbial communities. Proteobacteria was the most abundant phylum in all samples with the largest class of Gammaproteobacteria in activated sludge from BJ6 (15.97%), and the highest abundance of Betaproteobacteria in QDN6 (13.37%), QDN12 (16.11%), WXN6 (21.30%), WXN12 (16.47%), WXS6 (16.97%) and WXS12 (19.34%). Meanwhile, BJ12, QDS6 and QDS12 were dominated by Saprospirae (belonging to phylum Bacteroidetes) (17.89–19.81%) at the class level (Fig. S1). Microbial communities exhibited geographical and seasonal separations, with different sampling locations having a greater impact as seen in the BrayCurtis distances showing on the NMDS (Fig. 1a). However, three dissimilarity tests (MRPP, ANOSIM, and PERMANOVA) demonstrated there were significant differences among all activated sludge samples from different WWTPs and between their two seasons (Table S5), suggesting microbial communities may have been intensively altered among different sites and between different seasons in all WWTPs. Moreover, Pearson correlation analysis revealed the significant correlation between COD_inflow and COD_effluent, TN_inflow, TP_inflow, TP_effluent (P < 0.05, Table S6), in this case, only COD_inflow and TN_effluent were kept for following analysis within the six operational parameters. Mantel test results indicated such shifts of microbial communities were significantly correlated with geographical distance, DO, temperature, COD_inflow, and TP_effluent (P < 0.05, Table S7). The number of total bacteria in both summer and winter from all five WWTPs was generated by quantifying 16S rRNA gene (Fig. 2a). The copy numbers of 16S rRNA gene reached 1.34 × 1010 (QDN12) – 6.45 × 1010 (BJ12) per gram wet weight activated sludge (Fig. 2a). The quantified bacterial abundance was higher in winter than summer in all WWTPs except QDN, which may be related to the seasonal switching and physiological adaptations of bacteria communities. Significant seasonal fluctuations were found between summer and winter samples from WWTPs in Beijing (BJ6 and BJ12), and Wuxi (WXN6 and WXN12, WXS6 and WXS12) (P < 0.05) (Fig. 2a). Nonetheless, microbial abundance estimated by 16S rRNA gene was usually overestimated due to multiple copies existing in genome (Kembel et al., 2012). After the correcting for the presence of the extra copies of 16S rRNA gene by the PICRUSt algorithm, the total abundance of bacteria decreased to an average of 44.46% of the original 16S rRNA gene copies, ranging from 42.33% (QDN6) to 47.15% (WXN6), among different samples (Table S8). Although the orders of bacterial quantities changed after correction, significant seasonal variations were still found in BJ and WXS WWTPs (Fig. 2b). In addition, the highest and lowest bacterial quantities still occurred in BJ12 and QDN12 (Fig. 2b). The corrected bacterial quantities may better reflect the real bacterial abundance in WWTPs and was therefore used for further evaluation of relative abundance of sul1 gene.

2.4. 16S rRNA, sul1 genes sequencing and data processing For MiSeq sequencing, 16S rRNA gene was amplified using the common primer set 515F and 806R (Caporaso et al., 2012), with barcode sequences at both ends, targeting on the V4 hypervariable region. The 51F and 280R with barcode sequences at both ends were used for sul1 gene (The protocols are presented in Text S1). PCR products were purified and pooled for construction of DNA libraries and sequenced on MiSeq sequencing machine (Illumina) by PE250 kit (2 ∗ 250 bp). Raw sequencing data was preprocessed and analyzed by an in-house Galaxy Pipeline (http://mem.rcees.ac.cn:8080/) integrated with FLASH program (Magoc and Salzberg, 2011), Btrim (Kong, 2011), Uparse (Edgar, 2013), Uclust (Edgar, 2010), and taxonomy assignment. Cutoff of 0.97 was used for 16S rRNA gene sequences by Uparse (Edgar, 2013), and sul1 gene was analyzed at four cut-off levels of 99%, 95%, 90%, and 85% by Uclust (Edgar, 2010). To avoid frameshifts resulting from sequencing errors, all representative sequences of sul1 gene were verified by BLASTx with GenBank protein database. Only both query cover and identity of a sul1 sequence were at least 75%, and the highest identity of corresponding putative conserved domains hits annotated as sul1 gene, were reserved for following analysis. These sequences were deposited to the Sequence Read Archive (SRA) database and are available to the public (SRP101823 for 16S rRNA gene sequences, and SRP103520 for sul1 gene sequences). 2.5. Ecological and statistical analysis On the basis of the resample OTU table of 16S rRNA, non-metric multidimensional scaling (NMDS), Mantel test, and dissimilarity analysis including Multi Response Permutation Procedure (MRPP), Analysis of Similarities (ANOSIM), and Permutational Multivariate Analysis of Variance (PERMANOVA), were all carried out in R project. The multiple copies in each OTU of 16S rRNA gene were corrected by PICRUSt algorithm (Langille et al., 2013) to calculate the total multiple copy numbers of 16S rRNA gene in each sample. Then the corresponding proportion of multicopies in 16S rRNA gene in each sample in qPCR results could be inferred (Wei et al., 2018). Afterwards, more accurate relative abundance of sul1 gene could be evaluated with the corrected 16S rRNA gene abundance. Grouped box plots were used for displaying the quantities of sul1, int1, and 16S rRNA genes, and heatmap was applied for exhibiting the abundance of representative clusters of sul1 gene (scaled by samples). The difference of total sul1clusters between sites and seasons was assessed by dissimilarity test (MRPP, ANOSIM, and PERMANOVA). While the seasonal and geographical differences in quantities of sul1, int1, and 16S rRNA genes were compared by Student's t-test. In order to examine whether the dominant genera, as determined by the 16S rRNA gene sequences, were potential hosts of sul1 gene, only bacterial genera previously confirmed as containing sul1 gene in the updated sul1 gene sequence database, Ensembl database (Yates et al., 2016), and BLASTx verification were collected. Then these collected sequences and sul1-clusters at 0.95 cutoff (Konstantinidis and Tiedje, 2005) were merged and aligned by

3.2. Design degenerate primers for sul1 gene To design and evaluate the universal primers of sul1 gene, an 188


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(86.47% and 83.01%) and sul1-ARDB (99.07% and 93.81%) (Table S3). The new primers were proved no potential for hairpin formation or selfannealing by OligoCalc. Furthermore, the specificity of primers was tested with the updated sequence database of sul1 gene and ARDB. The new primers showed 3536 potential PCR amplicons of sul1 gene, and no homologs with other ARGs in ARDB by MFEprimer program, thus there were no false positive amplicons with other ARGs. In order to validate the efficiency of these primers, we simultaneously amplified sul1 gene by qPCR from 12 activated sludge DNA from Beijing (BJ) by both the newly designed primers and previously utilized sul1_1, sul1_2, sul1_3 and sul1_4 primers due to the latter's relatively higher coverage (Table S3). Table 1 indicated that the degenerate primers obtained higher sul1 gene abundance in all activated sludge samples in both summer (BJ6) and winter (BJ12) than the primers of sul1_1 (1.80–9.63 folds), sul1_2 (1.16–3.29 folds), sul1_3 (3.73–6.97 folds) and sul1_4 (3.44–7.19 folds), suggesting the degenerate primers could observe higher abundance in qPCR in environmental samples. Therefore, the degenerate primers may provide more accurate results for the detection of sul1 gene in natural and engineered environments. 3.3. Prevalence of sul1 gene in WWTPs The sul1 gene was quantified using the degenerate primers across all five WWTPs in both summer and winter. The copy numbers of sul1 gene in the five sites ranged from 5.14 × 108 to 1.11 × 109 per gram wet weight activated sludge, while the corrected bacterial quantity varied between 5.91 × 109 and 2.93 × 1010 copies per gram wet weight activated sludge (Fig. 2b and c). Surprisingly, relative abundance of the sul1 gene, were detected at the range of 0.084–0.141 copies per 16S rRNA gene copy in both WWTPs at Qingdao (QDN, QDS), 0.055–0.072 copies per 16S rRNA gene copy at Wuxi (WXN, WXS), and 0.037–0.073 copies per 16S rRNA gene copy at Beijing. The relative abundance of sul1 gene was 0.081 copies per bacterial cell in average, indicating the high abundance and wide distribution of sul1 gene in WWTPs. The abundance of sul1 gene across all locations in both seasons were displayed in grouped box plots (Fig. 2c and d). The quantified abundance of sul1 gene showed both seasonal and geographical fluctuations, yet, no significant fluctuations were found between seasons in the five WWTPs (Fig. 2c). There were no significant geographical differences of the relative abundance of sul1 genes between WWTPs located in the same city (QDN and QDS, WXN and WXS), while significant differences were more frequently found between WWTPs in different cities, like BJ and QDN, BJ and QDS, QDN and WXN, QDS and WXN, QDS and WXS (P < 0.05). Meanwhile, significant seasonal difference of sul1 relative abundance was only observed in the samples collected at BJ WWTPs (Fig. 2d, P < 0.05).

Fig. 1. Non-metric multidimensional scaling (NMDS) plots based on bray-curtis index of (a) 16S rRNA gene and (b) sul1 gene (BJ6: June samples in Beijing city, BJ12: December samples in Beijing city; QDN, QDS: the northern and southern WWTPs in Qingdao city; WXN, WXS: the northern and southern WWTPs in Wuxi city).

updated reference database was first constructed. Initially, 9796 protein sequences from GenBank exhibited some homology with the conserved sul1 HMM. After applying strict criteria to both annotation and similarities, only 1082 high reliable protein sequences were reserved, and the corresponding 861 non-redundant nucleotide sequences were integrated as an updated sul1 gene sequence database, it is available on http://mem.rcees.ac.cn/download/. Meanwhile, 323 non-redundant nucleotide sequences of sul1 gene according to ARDB (Liu and Pop, 2009) were also retrieved out as sul1-ARDB. The sequence number of the updated sul1 gene sequence database is 2.68 folds more than that of sul1 gene deposited in ARDB. Based on the updated sul1 gene sequence database and sul1-ARDB, in-silico coverage of commonly used sul1 gene primers was evaluated to compare their coverage against the two different databases (Table S3). Primers with higher coverage of the updated sul1 gene sequence database also hit more sequences in sul1ARDB, nevertheless, much higher coverage (9.09–12.93%) was obtained for the same primer evaluated by sul1-ARDB than the updated sul1 gene sequence database. This result indicated the primer coverage evaluated by sul1-ARDB could be over-estimated, suggesting the update of ARGs sequence database is necessary. Using DegePrime program (Hugerth et al., 2014), a new set of sul1 degenerate primers, 51F and 280R, was designed for highly conserved regions of the sul1 gene. The forward and reverse primers showed the highest coverages of both the updated sul1 gene sequence database

3.4. Close phylogeny of sul1 gene in WWTPs To discover the diversity of sul1 gene in the samples from all WWTPs, we performed MiSeq sequencing on sul1 gene amplicons using the newly designed degenerate primers. A total of 3,011,785 sul1 sequences obtained from the 60 samples taken at all five sites, in both seasons. After quality control and BLASTx verification with GenBank protein database on raw reads, various sequence similarity thresholds (85%, 90%, 95% and 99%) were utilized in clustering of the 1,564,958 remaining sequences into sul1-clusters. A total of 17 sul1-clusters were further confirmed as sul1 gene at 95% similarity threshold by BLASTx against the GenBank protein database. Three of these sul1-clusters, Cluster_109 (91.94%), Cluster_130 (5.16%), and Cluster_108 (2.45%), prevalent in all samples, displayed higher abundance compared to other clusters and accounted for 99.55% of the community (Fig. 3). In total, 120 sul1-clusters at 0.99 similarity, 11 sul1-clusters at 0.90 similarity, and 6 sul1-clusters at 0.85 similarity levels could be obtained, with the top abundant sul1-cluster accounting for 54.79% (0.99), 99.31% (0.90) 189


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Fig. 2. Grouped box plots of distribution of 16S RNA, sul1, and int1 genes in activated sludge between different seasons from different WWTPs. (a) the quantified abundance of bacteria; (b) the corrected abundance of bacteria; (c) the quantified abundance of sul1 gene; (d) the relative abundance of sul1 gene (sul1/corrected 16S rRNA); (e) the quantified abundance of int1 gene; (f) the relative abundance of int1 gene (int1/corrected 16S rRNA).

The abundance of those sul1-clusters showed very little variation across samples between seasons or among sites (Fig. 4). The relative abundance of top three dominant sul1-clusters was highly similar, with insignificant differences found among most sites and between the two seasons, except between seasonal samples QDS6 and QDS12, and between WXN and QDS sampling sites (P < 0.05). For total sul1-clusters, the NMDS plot (Fig. 1b) and dissimilarity test results (Table S9) of the distribution of sul1-clusters depicted no obvious distinction among different WWTPs, and there was also no significant difference between different seasons except the significant difference between the two seasons in QDS, as revealed by MRPP and ANOSIM. These results revealed that the composition of sul1 gene among WWTPs from different

and 99.91% (0.85) of the total abundance respectively. Considering sul1 gene is present in all WWTPs at relatively high abundance, the limited numbers of detected sul1-clusters indicates that the phylogenetic diversity of this gene was fairly low. We further assessed the phylogenetic relationship of the representative sul1-clusters by generating phylogenetic tree at the 0.95 cluster classification (Fig. 3). The dendrogram of sul1-clusters indicated there was quite close phylogeny between the detected sul1 sequences in all WWTPs and some bacteria including Escherichia coli., Salmonella enterica, Laribacter hongkongensis, Klebsiella pneumoniae, as well as some class 1 integrons from Escherichia coli., Providencia vermicola, Pseudomonas aeruginosa, Riemerella anatipestifer and Vibrio vulnificus. 190


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Table 1 Average copy numbers of sul1 gene quantified by different primers in activated sludge samples from WWTP in Beijing (BJ) (copies per gram wet weight activated sludge). Standard deviations are listed in parenthesis. Site

Sample

Degenerate primer (×108)

Primer sul1_1 (×108)




BJ6

1 2 3 4 5 6 Average abundance 7 8 9 10 11 12 Average abundance

10.42 (1.01) a 8.99 (0.90) a 11.00 (0.16) a 11.39 (0.63) a 9.80 (0.14) a 10.26 (0.69) a 10.31 10.27 (0.05) a 14.02 (0.27) a 8.78 (0.54) a 7.27 (0.14) a 7.93 (0.73) a 13.67 (0.66) a 10.32

1.08 1.66 1.99 1.39 1.34 2.52 1.66 3.41 5.82 2.65 2.71 4.40 5.95 4.16

3.37 (0.27) b 4.68 (0.60) b 5.93 (0.14) b 5.22 (0.09) b 2.98 (0.18) b 4.77 (0.57) b 4.49 3.74 (0.31) b 12.09(1.55) a 6.42 (0.39) b 3.81 (0.47) b 4.96 (0.09) b 6.71 (0.40) b 6.29

1.94 1.81 1.58 2.21 1.75 1.54 1.81 1.75 3.76 1.88 1.55 1.32 3.34 2.27

1.94 (0.03) 2.61 (0.38) 2.61 (0.52) 3.01 (0.29) 1.36 (0.03) 2.47 (0.20) 2.34 c 2.25 (0.34) 3.94 (0.46) 2.07 (0.22) 1.42 (0.21) 1.68 (0.20) 3.14 (1.32) 2.41c

BJ12

(0.04) (0.20) (0.04) (0.27) (0.27) (0.21)

c c c d c c

(0.27) (0.79) (0.09) (1.10) (0.82) (0.32)

b b c bc b b

(0.20) (0.40) (0.37) (0.38) (0.35) (0.32)

c c c cd c c

(0.38) (2.70) (0.16) (0.33) (0.07) (0.57)

c b c c c c

c c c c c c c b c c c c

a, b, c and d represent the significance of the difference between different primers.

sites and seasons were highly similar at the level of 0.95, even across large geographical distances.

3.5. Correlations between major sul1-clusters with dominated microbial species In the 16S MiSeq sequencing results, 19 bacterial genera with relative abundance greater than 1% were identified as dominant groups

Fig. 3. Phylogenetic tree of sul1-clusters at 0.95 identity by using maximum likelihood method, the scale length was 0.05 (Data in arrows indicated the relative abundance of sul1-clusters). 191


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Fig. 4. Heatmap of sul1 gene compositions in activated sludge samples based on the representative sul1-clusters, reds correspond to clusters with higher abundance and blues indicate lower abundance.

indicate that sul1 gene may be commonly located in the 3′-conserved segment of some class 1 integrons, but sometimes it may also locate outside the structure of integrons.

in the microbial community, including Caldilinea, Nitrospira, Hyphomicrobium, SMB53, Candidatus Microthrix, Dechloromonas, Mycobacterium, Rubrivivax, Turicibacter, Planctomyces, Rhodobacter, Dokdonella, Gemmata, Allochromatium, Thermomonas, Paracoccus, Phycicoccus, Clostridium, and Dok59 (Table S10). Among them, only Mycobacterium (2.75%) and Rhodobacter (1.57%) were thought to contain the sul1 gene in nature according to the taxonomy information from the updated sul1 gene sequence database and the Ensembl database. However, from the phylogenetic tree (Fig. 3), all representative sul1clusters separated into a single clade with several branches that had evolved away from the dominant bacterial species which may be their potential bacterial hosts, Mycobacterium and Rhodobacter. Pearson correlation analysis displayed no significant correlation between relative abundance of major sul1-clusters and all dominant bacterial genera, including Mycobacterium and Rhodobacter (Table S10). Such phylogenetic relationship and correlation suggested sul1-clusters observed in WWTPs may have no stable bacterial hosts, and transfer frequently among different bacteria. Hence instead of specific microbial taxa, MGEs likely play a greater role in the environmental presence of sul1 gene.

4. Discussion Activated sludge in WWTPs often contains highly diverse microbial communities, with favorable conditions, including moderate temperature and good mobility, for the exchange and dissemination of ARGs among microbial species. Previous studies have investigated the seasonal fluctuations of bacterial communities and abundance of ARGs in different environments (Bevivino et al., 2014; Hell et al., 2013; Knapp et al., 2012; Turki et al., 2017; Yang et al., 2013), and inconsistent conclusions were drawn as the types of environments and ARGs studied varied. Quantitative PCR has been the most widely used approach for the detection and quantification of ARGs (Chen et al., 2014; ColomerLluch et al., 2014; Mu et al., 2015), while high-throughput sequencing (HTS) technology, has been widely used to explore the bacterial communities through the use of marker genes (e.g. 16S rRNA gene) and recently begun to be used in the study of specific functional groups (e.g. diazotrophic communities (Tu et al., 2016) and ammonia-oxidizing microorganisms (Hu et al., 2014)). Both qPCR and HTS are powerful tools to examine the composition and diversity of functional microbiota, but they heavily rely on the best available primers. In this study, we designed new degenerate primers for a representative marker of antibiotic resistance, the sul1 gene. The seasonal and geographical variations of abundance and diversity of sul1 gene were explored by combining qPCR and HTS methods. The newly designed degenerate primers and application of HTS in exploring the abundance, diversity and sequence variations of antibiotic resistance genes in WWTP samples could lead to higher amplification efficiency, and more comprehensive information with high fidelity. In the bacterial community, Proteobacteria was the most dominant bacterial phylum across all samples, accounting for 24.33–44.36% of the total sequences (Fig. S1), consistent with previous surveys in sewage treatment plants (Zhang et al., 2012), municipal WWTPs (Tang et al., 2016) and other environments like bioreactors (Xia et al., 2010), soil (Liu et al., 2008), sediments and waters (Bai et al., 2009). Within

3.6. Correlation between sul1 gene and int1 gene The abundance of int1 gene in activated sludge was also quantified by qPCR, its abundance varied from 5.04 × 108 (WXS6) – 9.12 × 109 (QDS12) copies per gram wet weight activated sludge among different samples (Fig. 2e), which exhibited a similar amount to the quantified abundance of sul1 gene (5.14 × 108 to 1.11 × 109 copies). Compared to the corrected bacterial quantity, the relative abundance of int1 gene was ranged from 0.030–0.187 copies per 16S rRNA gene copy among all the samples (Fig. 2f). Significant seasonal difference was only detected for the quantified abundance of int1 gene between WXS6 and WXS12, as well as the relative abundance of int1 gene between BJ6 and BJ12. Compared to the sul1 gene, the relative abundance of int1 gene was in the range of 0.620 (QDN12)–1.583 (WXN6). However, no significant correlation was found between the abundance of sul1 and int1 genes, except in BJ WWTP (BJ12) (P < 0.05, Table S11). Such results 192


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sul1 gene in activated sludge samples across five Chinese WWTPs. The high abundance of sul1 gene confirmed it is one of the most prevalent ARGs in the environment (Pei et al., 2007; Pruden et al., 2006). In previous reports, the copy numbers of sul1 gene could reach 1.2–2.7 × 104 per mL in water samples, 2.5–7.4 × 106 per gram in sediment (Koczura et al., 2016), and (1.39 ± 0.92) × 1010 per gram dry weight in feces (Mu et al., 2015). Similar high level abundance of sul1 gene relative to 16S rRNA gene have been observed in different environments such as water (1.06–1.56%), sediment (1.05–2.56%) (Koczura et al., 2016), soil (0.00049–0.23%) (Chen et al., 2014), and sewage treatment plants (6.03–98.86%) (Xu et al., 2015). Generally, the quantity of sul1 gene in this study was comparable to the previous studies. The common spread and high abundance of sul1 gene in microbial communities in WWTPs may be due to the frequent HGT of ARGs in activated sludge and its close relationship with MGEs. The relative abundance of sul1 gene varied between both sites and seasons, but most of the differences were insignificant, except the seasonal difference in BJ WWTP, and geographical variations between WWTPs in different cities. Some studies have found significant differences of abundance of ARGs between sampling sites (Zhu et al., 2013) or with seasonal changes (Yang et al., 2013), while insignificant geographical (Koczura et al., 2016) and seasonal (Borjesson et al., 2010; Borjesson et al., 2009; Koczura et al., 2016) changes were observed in other studies. All these evidences suggested sul1 gene is present at consistently high levels in all WWTPs across different locations and seasons, posing an increased risk to public health. Although sul1 gene was prevalent in WWTPs, a low phylogenetic diversity of sul1 gene was observed as compared to the high diversity of 16S rRNA gene. At 99% similarity threshold, there were only 120 sul1clusters detected in more than 1.5 million of sequences, while at 95% identity, only 17 sul1-clusters remained. Among these 17 sul1-clusters, the greatest genetic distance was 0.223 (Fig. 3), which was much smaller than the genetic distance (0.833) of all available sul1 sequences in the updated database, further confirming the close phylogeny of sul1 clusters in WWTPs. Convergent evolution may also play a part in shaping the close phylogenetic structure of sul1 gene if convergent traits are phylogenetically conserved (Agrawal, 2017). Additionally, the major sul1-clusters at 0.95 identity exhibited insignificant differences among different sampling sites or between distinct seasons (Fig. 1b). Especially, no significant correlations were found between the relative abundance of dominant sul1-clusters and their potential bacterial hosts in WWTPs (Table S10). If sul1 gene was integrated in host genome and involved and passed along via vertical transmission, the abundance of sul1 gene should alter through time and space in accordance with their hosts and additionally should exhibit high genetic variance. Since there is also no significant correlation between the abundance of sul1 and int1 genes, we deduce that sul1 gene detected in China WWTPs may originate from Salmonella enterica, Laribacter hongkongensis, and/or Klebsiella pneumoniae, but most of them may transferred among the conjugative plasmids or transposons (Galimand et al., 2005; Wu et al., 2010), some integrated with class 1 integrons (Antunes et al., 2005; Heuer et al., 2012), and some in free status outside the structure of integrons (Bean et al., 2009; Koczura et al., 2016). In this case, HGT may play a central role in shaping the structure of sul1 gene, with the potential contribution of vertical transmission and convergent evolution. If we desire to better explore the real status of sul1 gene or other ARGs in various environments, and excavate the correlation between ARGs and their potential bacterial hosts, the connection between a marker gene and its host at a single-cell level will make it possible by using the recently developed epicPCR method (Spencer et al., 2016).

Proteobacteria, the class Betaproteobacteria showed the highest abundance in activated sludge from WWTPs in QDN, WXN and WXS in both seasons. This result is similar to previous reports on the dominance of the Betaproteobacteria in activated sludge (Kwon et al., 2010; Thomsen et al., 2007). In addition, Gammaproteobacteria was the largest group in BJ6, which is similar to the distribution of Gammaproteobacteria in bioreactors (Xia et al., 2010). In previous studies, Proteobacteria, the major phylum of Gram-negative bacteria that contains a number of organisms pathogenic to humans and animals including Escherichia coli, Pseudomonas aeruginosa, Salmonella enterica, Vibrio cholera, and many other notable genera (Kersters et al., 2006), have been found to be important hosts of both ARGs and integrons (Hu et al., 2016; Sun et al., 2016), confirming their active and vital roles in HGT of mobile ARGs and dissemination of antibiotic resistance. Compared with the seasonal fluctuation, location exhibited stronger effect on shaping the bacterial communities in activated sludge from WWTPs. The bacterial communities in adjacent geographical locations from the same cities, were much more similar than WWTPs located farther away (Fig. 1a). Geographical distance was also proved as the major factor influencing microbial communities in bioreactors from WWTPs (Wang et al., 2016), though some bacterial taxa/species could be surprisingly consistent even across different countries (Xia et al., 2010). Significant differences were also found between bacterial composition from summer and winter in the same WWTP, which is consistent with the seasonal shifts observed in municipal WWTPs in Tunisia (Turki et al., 2017) and Hong Kong (Ju et al., 2014). All these results indicated the bacterial communities in WWTPs are having a marked tendency to be altered across both space and time. In order to improve the accuracy of PCR-based approaches, qPCR and HTS, an updated sul1 gene sequence database and an improved pair of degenerate primers were constructed. Although the results of primer coverage demonstrated similar trends based on the two databases, the coverage evaluated by sul1-ARDB was still significantly higher than our collected sul1 sequences, suggesting the updated sul1 gene sequence database was more comprehensive and diverse. Such results verified the crux of continued improvement of ARG databases (Crofts et al., 2017) and the importance of assessing primers with the most comprehensive database before commencing microbial researches based on PCR approaches (Thomas et al., 2011). Additionally, the newly designed degenerate primers, as well as commonly used primers were applied to quantify the copy numbers of sul1 gene in real environmental samples to compare the efficiency of different primers. The new primers exhibited significant higher coverage in all tested samples, and at least 90% sequences were confirmed as real sul1 gene amplicons by HTS method. Due to the inevitable mismatches between template and primers (Thomas et al., 2011), the specificity of the new degenerate primers was acceptable in the tested activated sludge samples. Our findings are consistent with earlier studies that the addition of appropriate degeneracies could generally improve primers (Thomas et al., 2011; Hugerth et al., 2014). Nevertheless, there is also a potential problem in using degenerate primers, since too many degenerate sites may bring specificity biases (Gaby and Buckley, 2017). The efficiency and specificity of PCR amplification may be highly related to templates, annealing temperatures, the concentration of primers, and PCR cycle numbers (Gaby and Buckley, 2017; Sipos et al., 2007). For example, templates containing GC complements at the degenerate sites tend to be more effective than AT-rich sites (Polz and Cavanaugh, 1998), and a relatively low annealing temperature may just reduce the unintended target amplifications and maintain the specificity (Sipos et al., 2007). Therefore, in order to apply PCR-based tools to probe other ARGs in various environments, updated reference sequence databases, comprehensive evaluation of primers, and optimal PCR programs are highly recommended. By using new primers, we found high copy numbers (5.14 × 108 to 1.11 × 109 copies per gram wet weight activated sludge) and high relative abundance (0.037–0.141 copies per 16S rRNA gene copy) of the

5. Conclusions In the present study, we sought to improve the coverage of PCRbased approaches for the detection of a widespread ARG, sul1, in environmental samples. Firstly, the new pair of degenerate primers 193


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designed based on the updated sul1 gene sequence database showed high specificity and efficiency in both in-silico evaluation and activated sludge samples. By using qPCR and HTS, we explored the distribution of sul1 gene across five WWTPs in both summer and winter. The results revealed there was fairly high relative abundance of sul1 gene to bacteria in all activated sludge microbial communities, indicating the high level of antibiotic resistance in WWTPs. In addition, the microbial taxonomic diversity was significantly altered across different locations and through two seasons, however, the phylogeny of sul1 sequences were highly conserved and both the abundance and diversity of total sul1-clusters were almost unchanged over different WWTPs and seasons. Also, there was no apparent association among sul1-clusters and dominant bacterial genera, as well as the abundance of sul1 gene and int1 gene. All these results indicated although the different WWTPs possessed unique microbial communities, the core representative sul1clusters remained relatively constant among different WWTPs. These results suggest that sul1 gene is highly abundant and mobile and exists as MGEs, located on class 1 integrons or not, transferring among different bacterial species rather than linked to any specific microbial taxa in WWTPs, posing a serious risk to public health in this urban environment.

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