- Email: [email protected]
Fate of antibiotic resistance genes in sewage treatment plant revealed by metagenomic approach
Accepted Manuscript Fate of Antibiotic Resistance Genes in Sewage Treatment Plant Revealed by Metagenomic Approach Ying Yang , Bing Li , Shichun Zou , Herbert H.P. Fang , Tong Zhang PII:
S0043-1354(14)00372-8
DOI:
10.1016/j.watres.2014.05.019
Reference:
WR 10673
To appear in:
Water Research
Received Date: 7 February 2014 Revised Date:
12 May 2014
Accepted Date: 13 May 2014
Please cite this article as: Yang, Y., Li, B., Zou, S., Fang, H.H.P., Zhang, T., Fate of Antibiotic Resistance Genes in Sewage Treatment Plant Revealed by Metagenomic Approach, Water Research (2014), doi: 10.1016/j.watres.2014.05.019. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Fate of Antibiotic Resistance Genes in Sewage Treatment Plant
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Revealed by Metagenomic Approach
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Ying Yanga, Bing Lia, Shichun Zoub, Herbert H.P. Fanga, Tong Zhanga,*
4
a
5
University of Hong Kong, Hong Kong SAR, China
6
b
7
Yat-Sen University, Guangzhou 510000, China
8
*
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Address: Environmental Biotechnology Laboratory, Department of Civil Engineering,
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Environmental Biotechnology Laboratory, Department of Civil Engineering, The
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Corresponding author
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Institute of Marine Science and Technology, School of Marine Sciences, Sun
The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China
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Email: [email protected]
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Tel: +852-2857 8551
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Fax: +852-2859 8987
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ACCEPTED MANUSCRIPT ABSTRACT
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Antibiotic resistance has become a serious threat to human health. Sewage treatment
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plant (STP) is one of the major sources of antibiotic resistance genes (ARGs) in
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natural environment. High-throughput sequencing-based metagenomic approach was
19
applied to investigate the broad-spectrum profiles and fate of ARGs in a full scale STP.
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Totally, 271 ARGs subtypes belonging to 18 ARGs types were identified by the broad
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scanning of metagenomic analysis. Influent had the highest ARGs abundance,
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followed by effluent, anaerobic digestion sludge and activated sludge. 78 ARGs
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subtypes persisted through the biological wastewater and sludge treatment process.
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The high removal efficiency of 99.82% for total ARGs in wastewater suggested that
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sewage treatment process is effective in reducing ARGs. But the removal efficiency of
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ARGs in sludge treatment was not as good as that in sewage treatment. Furthermore,
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the composition of microbial communities was examined and the correlation between
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microbial community and ARGs was investigated using redundancy analysis.
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Significant correlation between 6 genera and the distribution of ARGs were found and
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5 of the 6 genera included potential pathogens. This is the first study on the fate of
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ARGs in STP using metagenomic analysis with high-throughput sequencing and
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hopefully would enhance our knowledge on fate of ARGs in STP.
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Keywords: Antibiotic resistance genes; Sewage treatment plant; Metagenomic
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analysis; Bacterial community
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ACCEPTED MANUSCRIPT 1. Introduction
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The intensive use of antibiotics for medical treatment, veterinary or agriculture
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proposes resulted in the continuous release of antibiotics into the environment and
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subsequently the widespread of antibiotic resistance. The escalating of antibiotic
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resistance has become a serious threat to human health because of the reduced
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susceptibility of disease-causing microorganisms to antibiotics in medical treatment
42
(Abuzaid et al., 2012; Noguchi et al., 2007). Reports on the global spread of antibiotic
43
resistance genes (ARGs) are still on the rise (Allen et al., 2010; Zhang et al., 2006;
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Zhang et al., 2009b; Laxminarayan et al., 2013). ARGs have been detected in natural
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water bodies and drinking water (Jiang et al., 2013; Xu et al., 2014), soil (Su et al.,
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2014) and even deep ocean sediment (Chen et al., 2013), nit nd 0
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ACCEPTED MANUSCRIPT DNA extraction. The pellet of influent and the membrane of effluent samples were
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used for DNA extraction directly. DNA extraction was conducted using FastDNA®
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Spin kit for Soil (MP Biomedicals, CA, USA). DNA of influent samples collected
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from July, August, September of 2011 were pooled as one influent DNA sample
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while DNA of influent samples collected from November, December of 2011 and
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January of 2012 were pooled as another DNA sample to avoid monthly fluctuation of
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ARGs in the influent. Similar to influent, DNA of effluent collected from July,
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August, September of 2011 were pooled as one DNA sample and DNA of effluent
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collected from November, December of 2011 and January in 2012 were pooled as
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another effluent DNA sample. Since the microbial composition in AS or ADS is
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relatively stable, DNA samples from sludge were not composited. AS samples
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collected from January 2011 and March 2012, and ADS samples collected from
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September 2011 and March 2012 were used for DNA extraction, separately. DNA
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concentration and purity were determined by microspectrophotometry (NanoDrop
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ND-1000, NanoDrop Technologies, Willmington, DE). In summary, two DNA
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samples were used to study ARGs in each kind of sample, including influent, effluent,
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AS and ADS.
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2.3 Illumina sequencing and bioinformatics analysis
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High-throughput sequencing was conducted using Illumian Hiseq 2000 at Beijing
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Genomics Institute (BGI, Shenzhen, China). Approximately 6 µg of each DNA
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sample was used for library construction. The sequencing strategy was index
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PE101+8+101 cycle (Paired-End sequencing, 101-bp reads and 8-bp index sequence).
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more ambiguous nucleotides, or with quality score below 30, or with sequence length
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less than 100 bp. A customized python script was used to remove artificial replicates
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generated by the platforms of high-throughput sequencing, which may lead to
inot 1525(r)2.80439(e-2.16436(o)-74295-0.29558517i
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incorrect conclusions in the subsequent datoit
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(Fig. S3). At genus level, the differences of bacterial communities became more divergent
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(Table S8). For influent samples, the most abundant genus was Streptococcus
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belonging to the phylum of Firmicutes (12.7%). For effluent samples, the most
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abundant genus shift to Mycobacterium of the phylum of Actinobacteria with a much
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higher percentage (30.7%). The most abundant genus in AS was Nitrospira (8.76%), a
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nitrite-oxidizing bacteria. However, the most abundant genus in anaerobic digestion
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sludge was Kosmotoga (36.1%), which was a genus mainly found in anaerobic marine
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environments (Swithers et al., 2011).
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The composition of microbial communities would affect the occurrence and
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abundance of ARGs, but the information on the impact of microbial community
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composition on ARGs profiles in environmental samples was still very limited.
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Although broad profile of ARGs and composition of microbial communities can be
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revealed by metagenomic analysis, the correlation between them was very
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complicated. Hence multi-parametric analysis was applied to assess the influence of
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the microbial communities on the distribution of ARGs in wastewater treatment
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process in this study, which might shed some light on these complex relations and
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provide some information to the control of ARGs in environment through wastewater
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treatment process.
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The correlation was examined through RDA based on percentages of the 283
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genera and abundances of the 78 identified persistent ARGs in the samples collected
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from ST STP. Genera were analyzed as the environmental factors for ARGs. As 19
ACCEPTED MANUSCRIPT shown in Fig. 4, among the 283 identified genera, 6 genera including Flavobacterium
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(G1), Poriferibacter (G2), Bacteroides (G3), Acinetobacter (G4), Actinobaculum (G5)
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and Streptococcus (G6), were significantly correlated with the distribution of ARGs in
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ST STP samples (P=0.002), indicating that these genera possibly played important
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roles in shaping the ARGs profiles in ST STP.
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Except Poriferibacter, all the other 5 genera belonged to human or aquaculture
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pathogens, indicating that the existence of pathogens is one important factor on ARGs
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distribution in STP environment. ARGs have been frequently detected in the
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pathogens of those genera. In detail, the multidrug resistance gene mexB were
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detected in the aquaculture pathogenic Flavobacterium spp. (Clark et al., 2009).
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Multiple ARGs, including cepA, cfxA, cfiA, ermB, ermF, ermG, linA, mefA, msrSA,
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tetQ, tetX and bexA, were detected in the human opportunistic pathogen Bacteroides
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strains (Eitel et al., 2013). Resistance genes of RND efflux pump or OXA-type
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carbapenemases were detected in Acinetobacter strains (Farahani et al., 2013;
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Wieczorek et al., 2008). Quinolone resistance genes were detected in Actinobaculum
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strains (Cattoir et al., 2010) and resistance genes for macrolide and tetracycline were
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detected in Streptococcus strains (Pavlovic et al., 2010; Seppala, 2003).
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The persistent ARGs can be grouped as 3 clusters according to the RDA results
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(Fig. 4, ARGs subtypes in each cluster were listed in Table S9). Cluster 1 was
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positively affected by Flavobacterium (G1) and Poriferibacter (G2). Cluster 2 was
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positively affected by all of the 6 genera. Cluster 3 was significantly affected by
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Bacteroides (G3), Acinetobacter (G4), Actinobaculum (G5) and Streptococcus (G6) in 20
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2011. Quantification and characterization of β-lactam resistance genes in 15 sewage treatment plants from
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East Asia and North America. Applied Microbiology and Biotechnology 95(5), 1351-1358.
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revealed by 16S rDNA 454 pyrosequencing. Applied Microbiology and Biotechnology 97(6), 2381-2690.
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Ye, L., Zhang, T., 2012. Bacterial communities in different sections of a municipal wastewater treatment plant
Zhang, R., Eggleston, K., Rotimi, V., Zeckhauser, R.J., 2006. Antibiotic resistance as a global threat: evidence
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from
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http://dx.doi.org/10.1186/1744-8603-2-6.
China,
Kuwait
and
the
United
States.
Globalization
and
Health.
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Zhang, T., Zhang, M., Zhang, X.X., Fang, H.H.P,, 2009a. Tetracycline resistance genes and tetracycline resistant
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lactose-fermenting Enterobacteriaceae in activated sludge of sewage treatment plants. Environmental
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Science & Technology 43(10), 3455-3460.
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mobile genetic elements in activated sludge. PLoS ONE. http://dx.doi.org/10.1371/journal.pone.0026041
Zhang, X. X., Zhang, T. and P.Fang, H.H.P., 2009b. Antibiotic resistance genes in water environment. Applied
Microbiology and Biotechnology 82(3), 397-414.
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Zhang, T., Zhang, X.X., Ye, L., 2011. Plasmid metagenome reveals high levels of antibiotic resistance genes and
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Table for
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Fate of Antibiotic Resistance Genes in Sewage Treatment Plant
3
Revealed by Metagenomic Approach
4
Ying Yanga, Bing Lia, Shichun Zoub, Herbert H.P. Fanga, Tong Zhanga,*
5
a
6
University of Hong Kong, Hong Kong SAR, China
7
b
8
University, Guangzhou 510000, China
9
*
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1
Environmental Biotechnology Laboratory, Department of Civil Engineering, The
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Corresponding author
SC
Institute of Marine Science and Technology, School of Marine Sciences, Sun Yat-Sen
Address: Environmental Biotechnology Laboratory, Department of Civil Engineering,
11
The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China
12
Email: [email protected]
13
Tel: +852-2857 8551
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Fax: +852-2859 8987
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List of Table
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Table 1 - Removal efficiency from influent to effluent, and from AS to ADS
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ACCEPTED MANUSCRIPT Table 1 - Removal efficiency from influent to effluent, and from AS to ADS
ADS N/A 4.40 4.00 1.18 1.88 N/A 0.47 N/A N/A 11.1 3.98 0.42 0.89 3.41 13.6 0.04 0.23 1.89 47.4
Water 99.22 99.89 99.76 99.83 99.77 99.81 99.02 99.94 99.90 99.93 99.89 99.90 99.43 98.09 99.93 99.91 99.96 99.92 99.82
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AS N/A 3.15 3.30 3.03 2.59 N/A 0.79 N/A 0.03 2.04 4.58 0.03 0.63 3.50 4.86 0.02 0.01 1.38 29.9
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Effluent 0.03 3.19 15.4 2.41 6.36 0.04 5.50 0.02 0.07 3.34 10.5 0.14 8.72 15.9 7.56 0.08 0.04 3.28 82.6
Sludge N/A 30.16 39.39 80.53 63.71 N/A 70.25 N/A 100 -172.1 56.55 -600.0 29.37 51.29 -39.92 0 -1050 31.52 20.74
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Acridine Acriflavine Aminoglycoside Bacitracin Beta-lactam Bicyclomycin Chloramphenicol Fosfomycin Fosmidomycin MLS Multidrug Polymyxin Quinolone Sulfonamide Tetracycline Trimethoprim Vancomycin Others Total
Influent 0.05 39.1 84.5 18.4 36.0 0.27 7.30 0.41 0.88 58.4 122 1.78 19.9 10.8 138 1.12 1.37 55.0 595
Removal efficiency /%
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Average abundance/ppm
ARGs type
* The removal efficiency with negative value indicated that the ARGs were enriched after
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treatment.
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ACCEPTED MANUSCRIPT Fig. 3 - Average abundances of ARG subtypes in influent, effluent, AS and ADS. From influent to effluent, subtypes with removal rate larger than 99.99% were marked with “**”. Subtypes with removal rate larger than 99.90% were
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marked with “*”. From AS to ADS, subtypes with removal rate larger than 90% were marked with “◎”, subtypes that were enriched by more than 10 times
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(removal rate lower than -1000%) were marked with “◇”.
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ACCEPTED MANUSCRIPT Fig. 4 - Redundancy analysis (RDA) identified the correlation of genera and ARGs in samples collected in ST WWTP. Six genera were significantly correlated with the distribution of ARGs (P=0.002). They are Flavobacterium (G1),
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Poriferibacter (G2), Bacteroides (G3), Acinetobacter (G4), Actinobaculum (G5) and Streptococcus (G6). The persistent ARGs can be grouped into 3 clusters according to the RDA result. ARGs subtypes in each cluster are
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summarized in Table S9.
ACCEPTED MANUSCRIPT Highlights: Biological wastewater treatment removed 99.8% of antibiotic resistance genes.
Anaerobic digestion removed 20.7% of antibiotic resistance genes from sludge.
Persistence of 78 antibiotic resistance subtypes was identified.
Significant correlation between 6 genera and antibiotic resistance genes was
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found.
ACCEPTED MANUSCRIPT Supplementary Materials for
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Fate of Antibiotic Resistance Genes in Sewage Treatment Plant
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Revealed by Metagenomic Approach
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Ying Yanga, Bing Lia, Shichun Zoub, Herbert H.P. Fanga, Tong Zhanga,*
5
a
6
University of Hong Kong, Hong Kong SAR, China
7
b
8
Yat-Sen University, Guangzhou 510000, China
9
*
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Environmental Biotechnology Laboratory, Department of Civil Engineering, The
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Corresponding author
SC
Institute of Marine Science and Technology, School of Marine Sciences, Sun
Address: Environmental Biotechnology Laboratory, Department of Civil Engineering,
11
The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China
12
Email: [email protected]
13
Tel: +852-2857 8551
14
Fax: +852-2859 8987
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ACCEPTED MANUSCRIPT List of Tables
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Table S1 - Detailed information of the samples
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Table S2 - Monitor parameters of influent and effluent samples from Shatin WWTP
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Table S3 - Abundances of ARG types in influent, effluent, AS and ADS
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Table S4 - Percentage of ARGs types in influent, effluent, AS and ADS.
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Table S5 - Abundances and percentages of the top 10 ARGs subtypes from non-persistent ARGs in influent
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Table S6 - Average abundances and removal rate of the persistent ARG subtypes
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Table S7 - Removal efficiency of ARGs in previous studies and this study.
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Table S8 - Top 5 genera in the influent, effluent, AS and ADS
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Table S9 - Persistent ARGs subtypes in each cluster in RDA
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Table S10 - Total abundances of ARGs in different clusters from RDA
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Fig. S1 Flowchart of the ST WWTP and the sampling site of the samples of influent
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(IN-1 and IN-2), effluent (EF-1 and EF-2), activated sludge (AS-1 and AS-2)
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and anaerobic digestion sludge (ADS-1 and ADS-2).
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Fig. S2 Percentage of the persistent ARGs types in influent, effluent, AS and ADS.
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Fig. S3 Microbial compositions at phylum level in influent, effluent, AS and ADS.
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S3
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AS-2
March 2012
30,634,077
ADS-1
September 2011
11,825,380
ADS-2
March 2012
32,744,699
IN-2 EF-1 Effluent EF-2 Activated sludge Anaerobic digestion sludge
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Influent
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IN-1
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Number of clean metagenomic sequences
AS-1
Sampling time Composite sample collected from July, August and September 2011Composite sample collected from November, December 2011 and January 2012 Composite sample collected from July, August and September 2011 Composite sample collected from November, December 2011and January 2012 January 2011
31,035,681 34,003,203 33,083,035 27,948,384 49,149,488
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Name
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Type
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Table S1 - Detailed information of the samples
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Table S2 - Water quality parameters of influent and effluent samples from Shatin WWTP
pH
CBOD (mg/L)
SCOD (mg/L)
COD (mg/L)
TSS (mg/L)
NH3-N (mg/L)
TKN-N (mg/L)
NOX-N (mg/L)
Ortho-PO4 (mg/L)
TP (mg/L)
July 2011
7.2
310
63
740
430
21
43
<2.8
1.5
5.1
August 2011
7.2
>360
110
1040
430
27
54
<2.9
1.9
5.8
September 2011
7.3
>360
120
860
450
32
61
<1.6
<2.5
7.8
November 2011
7.1
360
110
920
490
27
41
4.6
1.8
6.2
December 2011
7.4
350
120
880
470
35
67
<2.9
3.0
8.7
January 2012
7.6
370
120
920
510
33
73
<1.7
3.5
8.5
TSS (mg/L)
NH3-N (mg/L)
TKN-N (mg/L)
NOX-N (mg/L)
Ortho-PO4 (mg/L)
TP (mg/L)
Cl(mg/L)
E. coli (/100mL)
430
21
43
<2.8
1.5
5.1
<108
8
430
27
54
<2.9
1.9
5.8
<110
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Effluent
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Sampling month
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Influent
COD (mg/L)
pH
July 2011
7.2
310
63
740
August 2011
7.2
>360
110
1040
September 2011
7.3
>360
120
860
450
32
61
<1.6
<2.5
7.8
<114
12
November 2011
7.1
360
110
920
490
27
41
4.6
1.8
6.2
<105
5
December 2011
7.4
350
120
880
470
35
67
<2.9
3.0
8.7
204
3
January 2012
7.6
370
120
920
510
33
73
<1.7
3.5
8.5
240
4
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SCOD (mg/L)
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CBOD (mg/L)
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MLS : Macrolide-lincosamide-streptogramin. N/A : not available.
0.05 39.1 84.5 18.4 36.0 0.27 7.30 0.41 0.88 58.4 122 1.78 19.9 10.8 138 1.12 1.37 55.0 595
0.06 3.60 21.6 1.66 5.56 N/A 8.43 0.03 0.06 4.53 12.1 0.21 6.77 6.89 9.25 0.09 N/A 3.23 84.0
N/A 2.79 9.27 3.15 7.16 0.07 2.58 N/A 0.07 2.15 8.95 0.07 10.7 24.9 5.87 0.07 0.04 3.33 81.2
0.03 3.19 15.4 2.41 6.36 0.04 5.50 0.02 0.07 3.34 10.5 0.14 8.72 15.9 7.56 0.08 0.04 3.28 82.6
N/A 2.36 3.93 2.40 3.52 N/A 0.94 N/A 0.02 2.64 3.36 0.02 0.47 3.01 4.94 0.04 0.02 1.16 28.8
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0.06 45.1 62.9 21.5 40.7 0.38 7.29 0.59 1.15 49.7 156 2.56 32.9 12.9 158 1.26 1.71 69.4 664
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0.03 33.0 106 15.3 31.4 0.16 7.31 0.23 0.61 67.1 88.6 1.00 6.93 8.64 117 0.97 1.03 40.6 526
EF_1
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Acridine Acriflavine Aminoglycoside Bacitracin Beta-lactam Bicyclomycin Chloramphenicol Fosfomycin Fosmidomycin MLS Multidrug Polymyxin Quinolone Sulfonamide Tetracycline Trimethoprim Vancomycin Others Total
Influent IN_2 Average
Abundance/ppm Effluent Activated sludge EF_2 Average AS_1 AS_2 Average
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IN_1
AC C
ARG type
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Table S3 - Abundances of ARG types in influent, effluent, AS and ADS
N/A 3.95 2.68 3.66 1.66 N/A 0.65 N/A 0.03 1.44 5.81 0.03 0.78 3.98 4.77 N/A N/A 1.60 31.0
N/A 3.15 3.30 3.03 2.59 N/A 0.79 N/A 0.03 2.04 4.58 0.03 0.63 3.50 4.86 0.02 0.01 1.38 29.9
Anaerobic digestion sludge ADS_1 ADS_2 Average N/A 3.72 5.92 1.27 2.54 N/A 0.51 N/A N/A 14.5 3.72 0.59 1.27 2.79 18.5 0.08 0.25 2.54 58.2
N/A 5.07 2.08 1.10 1.22 N/A 0.43 N/A N/A 7.67 4.24 0.24 0.52 4.03 8.58 N/A 0.21 1.25 36.6
N/A 4.40 4.00 1.18 1.88 N/A 0.47 N/A N/A 11.1 3.98 0.42 0.89 3.41 13.6 0.04 0.23 1.89 47.4
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Table S4 - Percentage of ARGs types in influent, effluent, AS and ADS.
MLS : Macrolide-lincosamide-streptogramin. N/A : not available.
0.01 6.54 14.8 3.08 6.04 0.04 1.24 0.07 0.14 10.1 20.2 0.29 3.13 1.79 23.1 0.19 0.23 9.08
0.07 4.28 25.7 1.98 6.62 N/A 10.0 0.04 0.07 5.40 14.4 0.25 8.06 8.20 11.0 0.11 N/A 3.85
N/A 3.44 11.4 3.88 8.82 0.09 3.17 N/A 0.09 2.65 11.0 0.09 13.1 30.7 7.23 0.09 0.04 4.10
0.04 3.86 18.5 2.93 7.72 0.04 6.61 0.02 0.08 4.02 12.7 0.17 10.6 19.5 9.12 0.10 0.02 3.97
AS_1
AS AS_2
Average
N/A 8.19 13.6 8.33 12.2 N/A 3.25 N/A 0.07 9.17 11.6 0.07 1.62 10.4 17.2 0.14 0.07 4.02
N/A 12.7 8.62 11.8 5.36 N/A 2.10 N/A 0.11 4.63 18.7 0.11 2.52 12.8 15.4 N/A N/A 5.15
N/A 10.5 11.1 10.1 8.79 N/A 2.67 N/A 0.09 6.90 15.2 0.09 2.07 11.6 16.3 0.07 0.04 4.59
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0.01 6.80 9.47 3.24 6.12 0.06 1.10 0.09 0.17 7.49 23.5 0.39 4.95 1.94 23.8 0.19 0.26 10.4
Effluent EF_2 Average
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0.01 6.28 20.2 2.91 5.96 0.03 1.39 0.04 0.12 12.8 16.8 0.19 1.32 1.64 22.3 0.18 0.20 7.71
EF_1
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Acridine Acriflavine Aminoglycoside Bacitracin Beta-lactam Bicyclomycin Chloramphenicol Fosfomycin Fosmidomycin MLS Multidrug Polymyxin Quinolone Sulfonamide Tetracycline Trimethoprim Vancomycin Others
Influent IN_2 Average
EP
IN_1
ADS ADS_1 ADS_2 Average N/A 6.40 10.2 2.18 4.36 N/A 0.87 N/A N/A 24.9 6.40 1.02 2.18 4.80 31.8 0.15 0.44 4.36
N/A 13.8 5.67 3.00 3.33 N/A 1.17 N/A N/A 20.9 11.6 0.67 1.42 11.0 23.4 N/A 0.58 3.42
N/A 10.1 7.92 2.59 3.85 N/A 1.02 N/A N/A 22.9 8.99 0.84 1.80 7.90 27.6 0.07 0.51 3.89
AC C
ARG type
RI PT
Percentage/%
S7
ACCEPTED MANUSCRIPT
Abundance/ppm
Others Multidrug Multidrug MLS Multidrug Multidrug Multidrug Tetracycline
0.35
8.50
N/D
0.19
7.77
0.38
0.02
7.47
0.52
0.04
7.20 6.70
0.28 N/D
6.20 5.88 4.02 4.05 66.8
0.18
Percentage/% 0.42
Activate Sludge N/D
Anaerobic Digestion Sludge 0.07
1.50
N/D
0.67
0.33
N/D
1.28
0.46
0.07
N/D
N/D
1.22
0.63
0.12
N/D
0.02 0.16
N/D 0.74
1.18 1.20
0.34 N/D
0.07 0.56
N/D 1.50
0.22
0.01
N/D
1.02
0.26
0.04
N/D
0.23 0.25 0.13 2.36
N/D N/D 0.03 0.47
0.04 N/D N/D 1.01
0.95 0.67 0.70 11.2
0.28 0.30 0.16 2.85
N/D N/D 0.11 1.64
0.07 N/D N/D 1.97
SC
Aminoglycoside
9.01
Anaerobic Digestion Sludge 0.04
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acrA bifunctional enzyme aacA_aphD dimethyladenosine transferase outer membrane protein TolC mdtO mefE (bicyclomycin) multidrug efflux system mdtL mdfA tetA(39) Total
Effluent
TE D
Acriflavine
Influent
Activate Sludge N/D
Influent
Effluent
1.50
EP
ARGs subtype
AC C
ARGs type
RI PT
Table S5 - Abundances and percentages of the top 10 ARGs subtypes from non-persistent ARGs in influent
S8
ACCEPTED MANUSCRIPT
Table S6 - Average abundances and removal rate of the persistent ARG subtypes
acrB aadA aminoglycoside 2-N-acetyltransferase aminoglycoside 3-phosphotransferase * aminoglycoside 6-phosphotransferase * aminoglycoside acetyltransferase aminoglycoside resistance protein * aminoglycoside resistance protein A * aminoglycoside resistance protein B * streptomycin phosphotransferase_strB undecaprenol kinase undecaprenyl-diphosphatase beta-lactamase class A beta-lactamase NPS-1 OXA gene OXA-10 OXA-46 penicillin binding protein * penicillin-binding protein 2 *
Aminoglycoside
Beta-lactam
AC C
EP
Bacitracin
Effluent
AS
ADS
30.1 7.34 0.02 1.36 25.8 5.42 1.65 27.0 1.55 1.25 3.92 10.1 4.80 3.94 2.21 0.31 1.92 0.11 2.36 1.05
2.84 6.16 0.04 0.05 1.14 4.99 0.10 1.81 0.09 0.36 1.19 0.80 0.39 1.59 0.70 0.05 1.86 0.08 0.04 0.02
3.15 0.81 0.01 0.02 0.86 0.38 0.04 0.68 0.06 0.24 1.70 1.07 0.16 0.21 0.95 0.02 0.17 0.05 0.05 0.01
4.35 0.81 0.05 0.08 0.99 0.60 0.02 0.81 0.13 0.06 0.74 0.40 0.19 0.15 0.30 0.04 0.23 0.03 0.07 0.03
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Acriflavine
Influent
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ARG Subtype
TE D
ARG Type
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Average Abundance/ppm
Removal Rate/% From From AS Influent to to ADS Effluent 99.88 31.02 98.91 49.76 97.12 -125.2 99.96 -107.8 99.94 42.29 98.81 21.29 99.92 79.18 99.91 40.60 99.92 -0.41 99.63 88.03 99.60 78.22 99.90 81.14 99.89 40.89 99.48 65.64 99.59 84.06 99.81 -29.53 98.74 30.91 99.10 68.82 99.98 31.29 99.98 -50.10
VEB-1
1.01
0.32
0.56
0.34
99.59
69.89
VEB-3
0.09
0.05
0.10
0.05
99.37
77.95 S9
ACCEPTED MANUSCRIPT
AC C
Multidrug
EP
TE D
MLS
0.05 0.05 0.21 0.22 0.15 0.08 0.12 0.04 0.44 0.05 0.04 0.19 0.75 0.08 0.04 0.02
0.08 0.05 0.02 0.03 0.33 0.02 0.58 0.34 1.41 0.34 0.16 1.86 0.69 2.59 1.41 0.26
99.41 99.90 99.74 99.88 99.92 99.94 99.99 99.87 94.39 99.61 99.99 99.98 99.81 99.99 99.98 99.98
20.21 51.10 96.40 93.18 -12.51 90.39 -139.1 -359.7 -62.18 -221.4 -83.79 -382.2 54.38 -1493 -1629 -532.1
15.9
2.03
0.85
1.23
99.83
27.32
9.79 3.98 2.85 2.84 2.16 0.34 1.60 14.8 4.10
0.33 0.21 0.19 0.17 0.63 0.03 0.95 2.04 0.18
0.18 0.02 0.02 0.01 1.45 0.12 0.16 1.38 0.02
0.08 0.02 0.02 0.02 0.76 0.06 0.06 1.08 0.03
99.96 99.93 99.92 99.92 99.62 99.87 99.23 99.82 99.94
78.96 53.22 53.22 24.95 73.87 75.67 81.90 60.92 6.450
RI PT
0.16 0.10 0.20 0.14 0.09 0.26 0.19 0.17 1.72 0.14 0.02 0.17 0.50 0.03 0.03 0.02
SC
0.36 1.30 0.99 1.55 1.50 5.31 16.3 1.68 0.40 0.47 2.78 9.27 3.36 4.47 2.39 1.23
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Chloramphenicol
VIM-11 chloramphenicol acetyltransferase * chloramphenicol and florfenicol exporter cmlA9 florfenicol exporter * ABC-type efflux carrier * ermB * ermF erythromycin esterase erythromycin esterase type 1 erythromycin ribosome methylase ** macB ** macrolide transporter macrolide-efflux protein mef gene * mefA * hydrophobe_amphiphile efflux-1 (HAE1) family protein mdtF * mdtG * mdtH * mdtN * mexB mexD mexF multidrug efflux protein multidrug resistance protein D *
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0.05 0.02 0.01 0.07 0.13 0.03 0.63 0.86 2.64
0.28 0.13 0.05 0.05 0.07 0.42 0.89 0.35 3.06
99.96 99.66 99.93 99.73 99.88 99.90 99.42 99.83 97.55
-163.8 -211.7 -125.2 65.91 72.95 -689.1 28.58 79.72 41.93
0.92
0.05
0.09
0.19
99.93
-4.060
31.1 2.72 0.28 1.25 0.63 2.12 0.73 3.10 33.3 3.85 9.57 7.59 0.19 31.2 0.25 32.6 1.80 0.14
0.31 0.12 0.19 2.53 0.02 0.19 0.05 0.36 0.65 0.02 0.66 0.42 0.34 1.18 0.02 1.61 0.07 0.19
0.56 0.03 0.08 0.10 0.04 0.02 0.07 0.65 0.88 0.02 0.15 1.14 0.05 0.59 0.20 0.67 0.01 0.51
1.95 0.06 0.21 0.62 0.13 0.04 0.16 0.23 4.85 0.22 0.35 0.77 0.06 3.33 0.02 1.11 0.02 0.09
99.99 99.94 99.14 97.39 99.97 99.88 99.92 99.85 99.97 99.99 99.91 99.93 97.67 99.95 99.92 99.94 99.95 98.29
-73.64 -0.07 -28.69 -203.6 -60.19 -29.53 -10.51 82.35 -175.3 -436.9 -14.57 66.20 38.57 -180.4 96.10 17.54 24.95 91.32
RI PT
0.27 0.52 0.02 0.12 0.27 0.14 8.72 0.33 15.6
AC C
EP
Tetracycline
TE D
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Sulfonamide
8.14 1.98 0.33 0.56 3.04 1.78 19.4 2.54 8.24
SC
Polymyxin Quinolone
multidrug transporter * norM RND multidrug efflux transporter * sdeY efflux transporter bifunctional polymyxin resistance protein arnA quinolone resistance protein sul2 sul1 ribosomal protection tetracycline resistance protein * tet32 * tetA * tetA(33) tetA(C) tetA(M) * tetA(Q) tetB(P) * tetG tetM * tetO ** tetQ * tetracycline resistance protein * tetV tetW * tetX * hypothetical protein * proton antiport protein * RND protein
Others
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ACCEPTED MANUSCRIPT
6.87
1. MLS : Macrolide-lincosamide-streptogramin. 2. **: subtypes with removal rate larger than 99.99% from influent to effluent.
0.21
0.02
RI PT
2.00
0.40
0.03
0.02
99.86
53.22
0.43
99.93
-551.3
SC
transporter, monovalent cationproton antiporter-2 (CPA2) family UDP-glucuronic acid decarboxylase_UDP-4-amino-4-deoxy-L-arabinose formyltransferase *
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3. *: subtypes with removal rate between 99.90% and 99.99% from influent to effluent. 4.
: subtypes with removal rate between 90.00% and 99.90% from activated sludge to anaeorbic digestion sludge.
5.
: subtypes that were enriched by more than 10 times (removal rate lower than -1000%) from activated sludge to anaerobic digestion
AC C
EP
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sludge.
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ACCEPTED MANUSCRIPT
Table S7 - Removal efficiency of ARGs in previous studies and this study.
tetB
---
---
---
60 ~ 92
tetC
---
---
---
---
tetG
---
---
---
---
tetL
---
---
---
tetM
> 99
68 ~ 99
tetO
> 99
tetQ
SC
tetA
Water (Chen and Zhang, 2013a) ---
ARGs
This study/%
RI PT
Removal efficiency in previous studies/% Water Water Water Sludge (Chen and (Liu et al., (Nõlvak et al., (Diehl and LaPara, Zhang, 2013b) 2013) 2013) 2010) ----49 ~ 98 90~99
Sludge (Ma et al., 2011) ---
Persistent ARGs
Water
Sludge
99.94
-0.07
---
100
100
---
-525 ~ 75
97.39
-203.6
---
-66 ~ 92
99.85
82.35
---
98
---
100
N. A
-77 ~ 82
68
---
---
99.97
-175.3
68 ~ 99
-46 ~ 57
---
90~99
-233 ~ 83
99.9
-436.9
> 99
68 ~ 99
---
---
---
---
99.99
-14.57
tetW
> 99
68 ~ 99
-50 ~ 67
---
99
-525 ~ 75
99.95
-180.4
tetX
---
---
---
---
99
-122 ~ 94
99.92
96.10
sulI
90 ~ 99
50 ~ 97
---
-298 ~ 75
---
84 ~ 99
97.55
41.93
sulII
---
---
---
---
---
45 ~ 90
99.83
79.72
ermB
---
---
---
21 ~ 60
---
-233 ~ 73
99.99
-139.1
ermF
---
---
---
---
-1900 ~ 86
99.87
-359.7
AC C
EP
TE D
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---
---
ampC ------90 ~ 92 ----99.95 N.A qnrS ------87 ~ 97 ----100 N.A The abundance of ARGs used in the calculating removal efficiency was estimated from the bar chart in the previous studies if the abundance of ARGs were not clearly reported in numbers. S13
ACCEPTED MANUSCRIPT Table S8 - Top 5 genera in the influent, effluent, AS and ADS
Digestion
Firmicutes
Blautia
12.1
Proteobacteria
Acinetobacter
9.56
Actinobacteria
Bifidobacterium
5.56
Proteobacteria
Moraxella
4.74
Actinobacteria
Mycobacterium
30.7
Proteobacteria
Francisella
6.51
Proteobacteria
Vibrio
3.88
Bacteroidetes
Flavobacterium
3.41
Spirochaetes
Leptospira
2.49
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12.7
Nitrospira
8.76
Actinobacteria
Mycobacterium
7.04
Bacteroidetes
Lewinella
5.08
Planctomycetes
Planctomyces
4.94
Proteobacteria
Denitromonas
4.65
Thermotogae
Kosmotoga
36.1
Synergistetes
Thermovirga
5.34
Actinobacteria
Mycobacterium
5.31
Chloroflexi
Longilinea
3.64
Syntrophomonas
3.23
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Sludge
Streptococcus
TE D
Anaerobic
Firmicutes
Nitrospirae Activated Sludge
Percentage/%
M AN U
Effluent
genus
SC
Influent
phylum
AC C
Firmicutes
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ACCEPTED MANUSCRIPT Table S9 - Persistent ARGs subtypes in each cluster in RDA
1
ARGs types Aminoglycoside MLS Others Aminoglycoside
2
RI PT
Beta-lactam Multidrug Quinolone Sulfonamide
SC
Tetracycline Acriflavine
Aminoglycoside
AC C
EP
3
TE D
Bacitracin
Beta-lactam
ARGs subtypes aminoglycoside 2-N-acetyltransferase erythromycin esterase RND protein aadA aminoglycoside acetyltransferase class A beta-lactamase OXA-10 mexF norM quinolone resistance protein sul1 tetA(C) tetV acrB aminoglycoside 3-phosphotransferase aminoglycoside 6-phosphotransferase aminoglycoside resistance protein aminoglycoside resistance protein A aminoglycoside resistance protein B streptomycin phosphotransferase_strB undecaprenol kinase undecaprenyl-diphosphatase beta-lactamase NPS-1 OXA gene OXA-46 penicillin binding protein penicillin-binding protein 2 VEB-1 VEB-3 VIM-11 chloramphenicol acetyltransferase chloramphenicol and florfenicol exporter cmlA9 florfenicol export er ABC-type efflux carrier ermB ermF erythromycin esterase type 1 erythromycin ribosome methylase macrolide efflux protein macB macrolide transporter mefA
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Cluster
Chloramphenicol
MLS
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ACCEPTED MANUSCRIPT
SC
RI PT
Multidrug
mef gene hydrophobe_amphiphile efflux-1 (HAE1) family protein mdtF mdtG mdtH mdtN mexB mexD multidrug efflux protein multidrug resistance protein D multidrug transporter multidrug efflux pump sdeY transporter hypothetical protein proton antiport protein transporter, monovalent cationproton antiporter-2 (CPA2) family UDP-glucuronic acid decarboxylase_UDP-4-amino-4-deoxy-L-arabinose formyltransferase bifunctional polymyxin resistance protein arnA dihydropteroate synthase_sul2 ribosomal protection tetracycline resistance protein tetracycline resistance protein tet32 tetA tetA(33) tetA(M) tetA(Q) tetB(P) tetG tetM tetO tetQ tetW tetX
M AN U
Others
AC C
EP
Tetracycline
TE D
Polymyxin Sulfonamide
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ACCEPTED MANUSCRIPT Table S10 - Total abundances of ARGs in different clusters from RDA Cluster
Total abundance/ppm Effluent
AS
ADS
1
0.55
1.94
0.95
1.55
2
51.3
43.3
5.16
6.61
3
412
25.7
22.3
36.0
AC C
EP
TE D
M AN U
SC
RI PT
Influent
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EP
TE D
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SC
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Fig. S1 - Flowchart of the ST WWTP and the sampling site of the samples of influent (IN-1 and IN-2), effluent (EF-1 and EF-2), activated sludge (AS-1 and AS-2) and anaerobic digestion sludge (ADS-1 and ADS-2).
S18
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
AC C
Fig. S2 - Percentage of the persistent ARGs types in influent, effluent, AS and ADS.
S19
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RI PT
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Fig. S3 - Microbial compositions at phylum level in influent, effluent, AS and ADS.
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ACCEPTED MANUSCRIPT REFERENCES Chen, H. and Zhang, M., 2013a. Effects of advanced treatment systems on the removal of antibiotic resistance genes in wastewater treatment plants from Hangzhou, China. Environmental Science & Technology http://dx.doi.org/10.1021/es401091y. Chen, H. and Zhang, M., 2013b. Occurrence and removal of antibiotic resistance genes in municipal wastewater
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and rural domestic sewage treatment systems in eastern China. Environment International 55, 9-14. Diehl, D.L. and LaPara, T.M., 2010. Effect of temperature on the fate of genes encoding tetracycline resistance and the integrase of class 1 integrons within anaerobic and aerobic digesters treating municipal wastewater solids. Environmental Science & Technology 44(23), 9128-9133.
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Liu, L., Liu, C., Zheng, J., Huang, X., Wang, Z., Liu, Y. and Zhu, G., 2013. Elimination of veterinary antibiotics and antibiotic resistance genes from swine wastewater in the vertical flow constructed wetlands.
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Chemosphere 91(8), 1088-1093.
Ma, Y., Wilson, C.A., Novak, J.T., Riffat, R., Aynur, S., Murthy, S. and Pruden, A., 2011. Effect of various sludge digestion conditions on sulfonamide, macrolide, and tetracycline resistance genes and class I integrons. Environmental Science & Technology 45(18), 7855-7861.
Nõlvak, H., Truu, M., Tiirik, K., Oopkaup, K., Sildvee, T., Kaasik, A., Mander, Ü. and Truu, J., 2013. Dynamics of antibiotic resistance genes and their relationships with system treatment efficiency in a horizontal
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subsurface flow constructed wetland. Science of The Total Environment 461-462, 636-644.
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S0043-1354(14)00372-8
DOI:
10.1016/j.watres.2014.05.019
Reference:
WR 10673
To appear in:
Water Research
Received Date: 7 February 2014 Revised Date:
12 May 2014
Accepted Date: 13 May 2014
Please cite this article as: Yang, Y., Li, B., Zou, S., Fang, H.H.P., Zhang, T., Fate of Antibiotic Resistance Genes in Sewage Treatment Plant Revealed by Metagenomic Approach, Water Research (2014), doi: 10.1016/j.watres.2014.05.019. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT 1
Fate of Antibiotic Resistance Genes in Sewage Treatment Plant
2
Revealed by Metagenomic Approach
3
Ying Yanga, Bing Lia, Shichun Zoub, Herbert H.P. Fanga, Tong Zhanga,*
4
a
5
University of Hong Kong, Hong Kong SAR, China
6
b
7
Yat-Sen University, Guangzhou 510000, China
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*
9
Address: Environmental Biotechnology Laboratory, Department of Civil Engineering,
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Environmental Biotechnology Laboratory, Department of Civil Engineering, The
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Corresponding author
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Institute of Marine Science and Technology, School of Marine Sciences, Sun
The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China
11
Email: [email protected]
12
Tel: +852-2857 8551
13
Fax: +852-2859 8987
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1
ACCEPTED MANUSCRIPT ABSTRACT
16
Antibiotic resistance has become a serious threat to human health. Sewage treatment
17
plant (STP) is one of the major sources of antibiotic resistance genes (ARGs) in
18
natural environment. High-throughput sequencing-based metagenomic approach was
19
applied to investigate the broad-spectrum profiles and fate of ARGs in a full scale STP.
20
Totally, 271 ARGs subtypes belonging to 18 ARGs types were identified by the broad
21
scanning of metagenomic analysis. Influent had the highest ARGs abundance,
22
followed by effluent, anaerobic digestion sludge and activated sludge. 78 ARGs
23
subtypes persisted through the biological wastewater and sludge treatment process.
24
The high removal efficiency of 99.82% for total ARGs in wastewater suggested that
25
sewage treatment process is effective in reducing ARGs. But the removal efficiency of
26
ARGs in sludge treatment was not as good as that in sewage treatment. Furthermore,
27
the composition of microbial communities was examined and the correlation between
28
microbial community and ARGs was investigated using redundancy analysis.
29
Significant correlation between 6 genera and the distribution of ARGs were found and
30
5 of the 6 genera included potential pathogens. This is the first study on the fate of
31
ARGs in STP using metagenomic analysis with high-throughput sequencing and
32
hopefully would enhance our knowledge on fate of ARGs in STP.
33
Keywords: Antibiotic resistance genes; Sewage treatment plant; Metagenomic
34
analysis; Bacterial community
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ACCEPTED MANUSCRIPT 1. Introduction
37
The intensive use of antibiotics for medical treatment, veterinary or agriculture
38
proposes resulted in the continuous release of antibiotics into the environment and
39
subsequently the widespread of antibiotic resistance. The escalating of antibiotic
40
resistance has become a serious threat to human health because of the reduced
41
susceptibility of disease-causing microorganisms to antibiotics in medical treatment
42
(Abuzaid et al., 2012; Noguchi et al., 2007). Reports on the global spread of antibiotic
43
resistance genes (ARGs) are still on the rise (Allen et al., 2010; Zhang et al., 2006;
44
Zhang et al., 2009b; Laxminarayan et al., 2013). ARGs have been detected in natural
45
water bodies and drinking water (Jiang et al., 2013; Xu et al., 2014), soil (Su et al.,
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36
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2014) and even deep ocean sediment (Chen et al., 2013), nit nd 0
3
ACCEPTED MANUSCRIPT DNA extraction. The pellet of influent and the membrane of effluent samples were
125
used for DNA extraction directly. DNA extraction was conducted using FastDNA®
126
Spin kit for Soil (MP Biomedicals, CA, USA). DNA of influent samples collected
127
from July, August, September of 2011 were pooled as one influent DNA sample
128
while DNA of influent samples collected from November, December of 2011 and
129
January of 2012 were pooled as another DNA sample to avoid monthly fluctuation of
130
ARGs in the influent. Similar to influent, DNA of effluent collected from July,
131
August, September of 2011 were pooled as one DNA sample and DNA of effluent
132
collected from November, December of 2011 and January in 2012 were pooled as
133
another effluent DNA sample. Since the microbial composition in AS or ADS is
134
relatively stable, DNA samples from sludge were not composited. AS samples
135
collected from January 2011 and March 2012, and ADS samples collected from
136
September 2011 and March 2012 were used for DNA extraction, separately. DNA
137
concentration and purity were determined by microspectrophotometry (NanoDrop
138
ND-1000, NanoDrop Technologies, Willmington, DE). In summary, two DNA
139
samples were used to study ARGs in each kind of sample, including influent, effluent,
140
AS and ADS.
141
2.3 Illumina sequencing and bioinformatics analysis
142
High-throughput sequencing was conducted using Illumian Hiseq 2000 at Beijing
143
Genomics Institute (BGI, Shenzhen, China). Approximately 6 µg of each DNA
144
sample was used for library construction. The sequencing strategy was index
145
PE101+8+101 cycle (Paired-End sequencing, 101-bp reads and 8-bp index sequence).
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7
ACCEPTED MANUSCRIPT The generated metagenomic data was first filtered to remove reads containing three or
147
more ambiguous nucleotides, or with quality score below 30, or with sequence length
148
less than 100 bp. A customized python script was used to remove artificial replicates
149
generated by the platforms of high-throughput sequencing, which may lead to
inot 1525(r)2.80439(e-2.16436(o)-74295-0.29558517i
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incorrect conclusions in the subsequent datoit
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8
ACCEPTED MANUSCRIPT 388
(Fig. S3). At genus level, the differences of bacterial communities became more divergent
390
(Table S8). For influent samples, the most abundant genus was Streptococcus
391
belonging to the phylum of Firmicutes (12.7%). For effluent samples, the most
392
abundant genus shift to Mycobacterium of the phylum of Actinobacteria with a much
393
higher percentage (30.7%). The most abundant genus in AS was Nitrospira (8.76%), a
394
nitrite-oxidizing bacteria. However, the most abundant genus in anaerobic digestion
395
sludge was Kosmotoga (36.1%), which was a genus mainly found in anaerobic marine
396
environments (Swithers et al., 2011).
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389
The composition of microbial communities would affect the occurrence and
398
abundance of ARGs, but the information on the impact of microbial community
399
composition on ARGs profiles in environmental samples was still very limited.
400
Although broad profile of ARGs and composition of microbial communities can be
401
revealed by metagenomic analysis, the correlation between them was very
402
complicated. Hence multi-parametric analysis was applied to assess the influence of
403
the microbial communities on the distribution of ARGs in wastewater treatment
404
process in this study, which might shed some light on these complex relations and
405
provide some information to the control of ARGs in environment through wastewater
406
treatment process.
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The correlation was examined through RDA based on percentages of the 283
408
genera and abundances of the 78 identified persistent ARGs in the samples collected
409
from ST STP. Genera were analyzed as the environmental factors for ARGs. As 19
ACCEPTED MANUSCRIPT shown in Fig. 4, among the 283 identified genera, 6 genera including Flavobacterium
411
(G1), Poriferibacter (G2), Bacteroides (G3), Acinetobacter (G4), Actinobaculum (G5)
412
and Streptococcus (G6), were significantly correlated with the distribution of ARGs in
413
ST STP samples (P=0.002), indicating that these genera possibly played important
414
roles in shaping the ARGs profiles in ST STP.
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410
Except Poriferibacter, all the other 5 genera belonged to human or aquaculture
416
pathogens, indicating that the existence of pathogens is one important factor on ARGs
417
distribution in STP environment. ARGs have been frequently detected in the
418
pathogens of those genera. In detail, the multidrug resistance gene mexB were
419
detected in the aquaculture pathogenic Flavobacterium spp. (Clark et al., 2009).
420
Multiple ARGs, including cepA, cfxA, cfiA, ermB, ermF, ermG, linA, mefA, msrSA,
421
tetQ, tetX and bexA, were detected in the human opportunistic pathogen Bacteroides
422
strains (Eitel et al., 2013). Resistance genes of RND efflux pump or OXA-type
423
carbapenemases were detected in Acinetobacter strains (Farahani et al., 2013;
424
Wieczorek et al., 2008). Quinolone resistance genes were detected in Actinobaculum
425
strains (Cattoir et al., 2010) and resistance genes for macrolide and tetracycline were
426
detected in Streptococcus strains (Pavlovic et al., 2010; Seppala, 2003).
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The persistent ARGs can be grouped as 3 clusters according to the RDA results
428
(Fig. 4, ARGs subtypes in each cluster were listed in Table S9). Cluster 1 was
429
positively affected by Flavobacterium (G1) and Poriferibacter (G2). Cluster 2 was
430
positively affected by all of the 6 genera. Cluster 3 was significantly affected by
431
Bacteroides (G3), Acinetobacter (G4), Actinobaculum (G5) and Streptococcus (G6) in 20
ACCEPTED MANUSCRIPT 644
2011. Quantification and characterization of β-lactam resistance genes in 15 sewage treatment plants from
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East Asia and North America. Applied Microbiology and Biotechnology 95(5), 1351-1358.
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revealed by 16S rDNA 454 pyrosequencing. Applied Microbiology and Biotechnology 97(6), 2381-2690.
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Ye, L., Zhang, T., 2012. Bacterial communities in different sections of a municipal wastewater treatment plant
Zhang, R., Eggleston, K., Rotimi, V., Zeckhauser, R.J., 2006. Antibiotic resistance as a global threat: evidence
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from
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http://dx.doi.org/10.1186/1744-8603-2-6.
China,
Kuwait
and
the
United
States.
Globalization
and
Health.
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Zhang, T., Zhang, M., Zhang, X.X., Fang, H.H.P,, 2009a. Tetracycline resistance genes and tetracycline resistant
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lactose-fermenting Enterobacteriaceae in activated sludge of sewage treatment plants. Environmental
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Science & Technology 43(10), 3455-3460.
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mobile genetic elements in activated sludge. PLoS ONE. http://dx.doi.org/10.1371/journal.pone.0026041
Zhang, X. X., Zhang, T. and P.Fang, H.H.P., 2009b. Antibiotic resistance genes in water environment. Applied
Microbiology and Biotechnology 82(3), 397-414.
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Zhang, T., Zhang, X.X., Ye, L., 2011. Plasmid metagenome reveals high levels of antibiotic resistance genes and
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Table for
2
Fate of Antibiotic Resistance Genes in Sewage Treatment Plant
3
Revealed by Metagenomic Approach
4
Ying Yanga, Bing Lia, Shichun Zoub, Herbert H.P. Fanga, Tong Zhanga,*
5
a
6
University of Hong Kong, Hong Kong SAR, China
7
b
8
University, Guangzhou 510000, China
9
*
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1
Environmental Biotechnology Laboratory, Department of Civil Engineering, The
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Corresponding author
SC
Institute of Marine Science and Technology, School of Marine Sciences, Sun Yat-Sen
Address: Environmental Biotechnology Laboratory, Department of Civil Engineering,
11
The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China
12
Email: [email protected]
13
Tel: +852-2857 8551
14
Fax: +852-2859 8987
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List of Table
17
Table 1 - Removal efficiency from influent to effluent, and from AS to ADS
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ACCEPTED MANUSCRIPT Table 1 - Removal efficiency from influent to effluent, and from AS to ADS
ADS N/A 4.40 4.00 1.18 1.88 N/A 0.47 N/A N/A 11.1 3.98 0.42 0.89 3.41 13.6 0.04 0.23 1.89 47.4
Water 99.22 99.89 99.76 99.83 99.77 99.81 99.02 99.94 99.90 99.93 99.89 99.90 99.43 98.09 99.93 99.91 99.96 99.92 99.82
SC
AS N/A 3.15 3.30 3.03 2.59 N/A 0.79 N/A 0.03 2.04 4.58 0.03 0.63 3.50 4.86 0.02 0.01 1.38 29.9
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Effluent 0.03 3.19 15.4 2.41 6.36 0.04 5.50 0.02 0.07 3.34 10.5 0.14 8.72 15.9 7.56 0.08 0.04 3.28 82.6
Sludge N/A 30.16 39.39 80.53 63.71 N/A 70.25 N/A 100 -172.1 56.55 -600.0 29.37 51.29 -39.92 0 -1050 31.52 20.74
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Acridine Acriflavine Aminoglycoside Bacitracin Beta-lactam Bicyclomycin Chloramphenicol Fosfomycin Fosmidomycin MLS Multidrug Polymyxin Quinolone Sulfonamide Tetracycline Trimethoprim Vancomycin Others Total
Influent 0.05 39.1 84.5 18.4 36.0 0.27 7.30 0.41 0.88 58.4 122 1.78 19.9 10.8 138 1.12 1.37 55.0 595
Removal efficiency /%
RI PT
Average abundance/ppm
ARGs type
* The removal efficiency with negative value indicated that the ARGs were enriched after
AC C
EP
treatment.
3
AC C
EP
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ACCEPTED MANUSCRIPT
ACCEPTED MANUSCRIPT Fig. 3 - Average abundances of ARG subtypes in influent, effluent, AS and ADS. From influent to effluent, subtypes with removal rate larger than 99.99% were marked with “**”. Subtypes with removal rate larger than 99.90% were
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marked with “*”. From AS to ADS, subtypes with removal rate larger than 90% were marked with “◎”, subtypes that were enriched by more than 10 times
AC C
EP
TE D
M AN U
SC
(removal rate lower than -1000%) were marked with “◇”.
AC C
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ACCEPTED MANUSCRIPT Fig. 4 - Redundancy analysis (RDA) identified the correlation of genera and ARGs in samples collected in ST WWTP. Six genera were significantly correlated with the distribution of ARGs (P=0.002). They are Flavobacterium (G1),
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Poriferibacter (G2), Bacteroides (G3), Acinetobacter (G4), Actinobaculum (G5) and Streptococcus (G6). The persistent ARGs can be grouped into 3 clusters according to the RDA result. ARGs subtypes in each cluster are
AC C
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summarized in Table S9.
ACCEPTED MANUSCRIPT Highlights: Biological wastewater treatment removed 99.8% of antibiotic resistance genes.
Anaerobic digestion removed 20.7% of antibiotic resistance genes from sludge.
Persistence of 78 antibiotic resistance subtypes was identified.
Significant correlation between 6 genera and antibiotic resistance genes was
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found.
ACCEPTED MANUSCRIPT Supplementary Materials for
2
Fate of Antibiotic Resistance Genes in Sewage Treatment Plant
3
Revealed by Metagenomic Approach
4
Ying Yanga, Bing Lia, Shichun Zoub, Herbert H.P. Fanga, Tong Zhanga,*
5
a
6
University of Hong Kong, Hong Kong SAR, China
7
b
8
Yat-Sen University, Guangzhou 510000, China
9
*
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1
Environmental Biotechnology Laboratory, Department of Civil Engineering, The
M AN U
Corresponding author
SC
Institute of Marine Science and Technology, School of Marine Sciences, Sun
Address: Environmental Biotechnology Laboratory, Department of Civil Engineering,
11
The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China
12
Email: [email protected]
13
Tel: +852-2857 8551
14
Fax: +852-2859 8987
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ACCEPTED MANUSCRIPT List of Tables
17
Table S1 - Detailed information of the samples
18
Table S2 - Monitor parameters of influent and effluent samples from Shatin WWTP
19
Table S3 - Abundances of ARG types in influent, effluent, AS and ADS
20
Table S4 - Percentage of ARGs types in influent, effluent, AS and ADS.
21
Table S5 - Abundances and percentages of the top 10 ARGs subtypes from non-persistent ARGs in influent
SC
22
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16
Table S6 - Average abundances and removal rate of the persistent ARG subtypes
24
Table S7 - Removal efficiency of ARGs in previous studies and this study.
25
Table S8 - Top 5 genera in the influent, effluent, AS and ADS
26
Table S9 - Persistent ARGs subtypes in each cluster in RDA
27
Table S10 - Total abundances of ARGs in different clusters from RDA
TE D EP AC C
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ACCEPTED MANUSCRIPT List of Figures
30
Fig. S1 Flowchart of the ST WWTP and the sampling site of the samples of influent
31
(IN-1 and IN-2), effluent (EF-1 and EF-2), activated sludge (AS-1 and AS-2)
32
and anaerobic digestion sludge (ADS-1 and ADS-2).
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29
Fig. S2 Percentage of the persistent ARGs types in influent, effluent, AS and ADS.
34
Fig. S3 Microbial compositions at phylum level in influent, effluent, AS and ADS.
SC
33
35
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S3
ACCEPTED MANUSCRIPT
AS-2
March 2012
30,634,077
ADS-1
September 2011
11,825,380
ADS-2
March 2012
32,744,699
IN-2 EF-1 Effluent EF-2 Activated sludge Anaerobic digestion sludge
M AN U
Influent
TE D
IN-1
SC
Number of clean metagenomic sequences
AS-1
Sampling time Composite sample collected from July, August and September 2011Composite sample collected from November, December 2011 and January 2012 Composite sample collected from July, August and September 2011 Composite sample collected from November, December 2011and January 2012 January 2011
31,035,681 34,003,203 33,083,035 27,948,384 49,149,488
EP
Name
AC C
Type
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Table S1 - Detailed information of the samples
S4
ACCEPTED MANUSCRIPT
Table S2 - Water quality parameters of influent and effluent samples from Shatin WWTP
pH
CBOD (mg/L)
SCOD (mg/L)
COD (mg/L)
TSS (mg/L)
NH3-N (mg/L)
TKN-N (mg/L)
NOX-N (mg/L)
Ortho-PO4 (mg/L)
TP (mg/L)
July 2011
7.2
310
63
740
430
21
43
<2.8
1.5
5.1
August 2011
7.2
>360
110
1040
430
27
54
<2.9
1.9
5.8
September 2011
7.3
>360
120
860
450
32
61
<1.6
<2.5
7.8
November 2011
7.1
360
110
920
490
27
41
4.6
1.8
6.2
December 2011
7.4
350
120
880
470
35
67
<2.9
3.0
8.7
January 2012
7.6
370
120
920
510
33
73
<1.7
3.5
8.5
TSS (mg/L)
NH3-N (mg/L)
TKN-N (mg/L)
NOX-N (mg/L)
Ortho-PO4 (mg/L)
TP (mg/L)
Cl(mg/L)
E. coli (/100mL)
430
21
43
<2.8
1.5
5.1
<108
8
430
27
54
<2.9
1.9
5.8
<110
36
EP
Effluent
M AN U
Sampling month
SC
RI PT
Influent
COD (mg/L)
pH
July 2011
7.2
310
63
740
August 2011
7.2
>360
110
1040
September 2011
7.3
>360
120
860
450
32
61
<1.6
<2.5
7.8
<114
12
November 2011
7.1
360
110
920
490
27
41
4.6
1.8
6.2
<105
5
December 2011
7.4
350
120
880
470
35
67
<2.9
3.0
8.7
204
3
January 2012
7.6
370
120
920
510
33
73
<1.7
3.5
8.5
240
4
AC C
SCOD (mg/L)
TE D
Date
CBOD (mg/L)
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ACCEPTED MANUSCRIPT
MLS : Macrolide-lincosamide-streptogramin. N/A : not available.
0.05 39.1 84.5 18.4 36.0 0.27 7.30 0.41 0.88 58.4 122 1.78 19.9 10.8 138 1.12 1.37 55.0 595
0.06 3.60 21.6 1.66 5.56 N/A 8.43 0.03 0.06 4.53 12.1 0.21 6.77 6.89 9.25 0.09 N/A 3.23 84.0
N/A 2.79 9.27 3.15 7.16 0.07 2.58 N/A 0.07 2.15 8.95 0.07 10.7 24.9 5.87 0.07 0.04 3.33 81.2
0.03 3.19 15.4 2.41 6.36 0.04 5.50 0.02 0.07 3.34 10.5 0.14 8.72 15.9 7.56 0.08 0.04 3.28 82.6
N/A 2.36 3.93 2.40 3.52 N/A 0.94 N/A 0.02 2.64 3.36 0.02 0.47 3.01 4.94 0.04 0.02 1.16 28.8
SC
0.06 45.1 62.9 21.5 40.7 0.38 7.29 0.59 1.15 49.7 156 2.56 32.9 12.9 158 1.26 1.71 69.4 664
M AN U
0.03 33.0 106 15.3 31.4 0.16 7.31 0.23 0.61 67.1 88.6 1.00 6.93 8.64 117 0.97 1.03 40.6 526
EF_1
TE D
Acridine Acriflavine Aminoglycoside Bacitracin Beta-lactam Bicyclomycin Chloramphenicol Fosfomycin Fosmidomycin MLS Multidrug Polymyxin Quinolone Sulfonamide Tetracycline Trimethoprim Vancomycin Others Total
Influent IN_2 Average
Abundance/ppm Effluent Activated sludge EF_2 Average AS_1 AS_2 Average
EP
IN_1
AC C
ARG type
RI PT
Table S3 - Abundances of ARG types in influent, effluent, AS and ADS
N/A 3.95 2.68 3.66 1.66 N/A 0.65 N/A 0.03 1.44 5.81 0.03 0.78 3.98 4.77 N/A N/A 1.60 31.0
N/A 3.15 3.30 3.03 2.59 N/A 0.79 N/A 0.03 2.04 4.58 0.03 0.63 3.50 4.86 0.02 0.01 1.38 29.9
Anaerobic digestion sludge ADS_1 ADS_2 Average N/A 3.72 5.92 1.27 2.54 N/A 0.51 N/A N/A 14.5 3.72 0.59 1.27 2.79 18.5 0.08 0.25 2.54 58.2
N/A 5.07 2.08 1.10 1.22 N/A 0.43 N/A N/A 7.67 4.24 0.24 0.52 4.03 8.58 N/A 0.21 1.25 36.6
N/A 4.40 4.00 1.18 1.88 N/A 0.47 N/A N/A 11.1 3.98 0.42 0.89 3.41 13.6 0.04 0.23 1.89 47.4
S6
ACCEPTED MANUSCRIPT
Table S4 - Percentage of ARGs types in influent, effluent, AS and ADS.
MLS : Macrolide-lincosamide-streptogramin. N/A : not available.
0.01 6.54 14.8 3.08 6.04 0.04 1.24 0.07 0.14 10.1 20.2 0.29 3.13 1.79 23.1 0.19 0.23 9.08
0.07 4.28 25.7 1.98 6.62 N/A 10.0 0.04 0.07 5.40 14.4 0.25 8.06 8.20 11.0 0.11 N/A 3.85
N/A 3.44 11.4 3.88 8.82 0.09 3.17 N/A 0.09 2.65 11.0 0.09 13.1 30.7 7.23 0.09 0.04 4.10
0.04 3.86 18.5 2.93 7.72 0.04 6.61 0.02 0.08 4.02 12.7 0.17 10.6 19.5 9.12 0.10 0.02 3.97
AS_1
AS AS_2
Average
N/A 8.19 13.6 8.33 12.2 N/A 3.25 N/A 0.07 9.17 11.6 0.07 1.62 10.4 17.2 0.14 0.07 4.02
N/A 12.7 8.62 11.8 5.36 N/A 2.10 N/A 0.11 4.63 18.7 0.11 2.52 12.8 15.4 N/A N/A 5.15
N/A 10.5 11.1 10.1 8.79 N/A 2.67 N/A 0.09 6.90 15.2 0.09 2.07 11.6 16.3 0.07 0.04 4.59
SC
0.01 6.80 9.47 3.24 6.12 0.06 1.10 0.09 0.17 7.49 23.5 0.39 4.95 1.94 23.8 0.19 0.26 10.4
Effluent EF_2 Average
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0.01 6.28 20.2 2.91 5.96 0.03 1.39 0.04 0.12 12.8 16.8 0.19 1.32 1.64 22.3 0.18 0.20 7.71
EF_1
TE D
Acridine Acriflavine Aminoglycoside Bacitracin Beta-lactam Bicyclomycin Chloramphenicol Fosfomycin Fosmidomycin MLS Multidrug Polymyxin Quinolone Sulfonamide Tetracycline Trimethoprim Vancomycin Others
Influent IN_2 Average
EP
IN_1
ADS ADS_1 ADS_2 Average N/A 6.40 10.2 2.18 4.36 N/A 0.87 N/A N/A 24.9 6.40 1.02 2.18 4.80 31.8 0.15 0.44 4.36
N/A 13.8 5.67 3.00 3.33 N/A 1.17 N/A N/A 20.9 11.6 0.67 1.42 11.0 23.4 N/A 0.58 3.42
N/A 10.1 7.92 2.59 3.85 N/A 1.02 N/A N/A 22.9 8.99 0.84 1.80 7.90 27.6 0.07 0.51 3.89
AC C
ARG type
RI PT
Percentage/%
S7
ACCEPTED MANUSCRIPT
Abundance/ppm
Others Multidrug Multidrug MLS Multidrug Multidrug Multidrug Tetracycline
0.35
8.50
N/D
0.19
7.77
0.38
0.02
7.47
0.52
0.04
7.20 6.70
0.28 N/D
6.20 5.88 4.02 4.05 66.8
0.18
Percentage/% 0.42
Activate Sludge N/D
Anaerobic Digestion Sludge 0.07
1.50
N/D
0.67
0.33
N/D
1.28
0.46
0.07
N/D
N/D
1.22
0.63
0.12
N/D
0.02 0.16
N/D 0.74
1.18 1.20
0.34 N/D
0.07 0.56
N/D 1.50
0.22
0.01
N/D
1.02
0.26
0.04
N/D
0.23 0.25 0.13 2.36
N/D N/D 0.03 0.47
0.04 N/D N/D 1.01
0.95 0.67 0.70 11.2
0.28 0.30 0.16 2.85
N/D N/D 0.11 1.64
0.07 N/D N/D 1.97
SC
Aminoglycoside
9.01
Anaerobic Digestion Sludge 0.04
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acrA bifunctional enzyme aacA_aphD dimethyladenosine transferase outer membrane protein TolC mdtO mefE (bicyclomycin) multidrug efflux system mdtL mdfA tetA(39) Total
Effluent
TE D
Acriflavine
Influent
Activate Sludge N/D
Influent
Effluent
1.50
EP
ARGs subtype
AC C
ARGs type
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Table S5 - Abundances and percentages of the top 10 ARGs subtypes from non-persistent ARGs in influent
S8
ACCEPTED MANUSCRIPT
Table S6 - Average abundances and removal rate of the persistent ARG subtypes
acrB aadA aminoglycoside 2-N-acetyltransferase aminoglycoside 3-phosphotransferase * aminoglycoside 6-phosphotransferase * aminoglycoside acetyltransferase aminoglycoside resistance protein * aminoglycoside resistance protein A * aminoglycoside resistance protein B * streptomycin phosphotransferase_strB undecaprenol kinase undecaprenyl-diphosphatase beta-lactamase class A beta-lactamase NPS-1 OXA gene OXA-10 OXA-46 penicillin binding protein * penicillin-binding protein 2 *
Aminoglycoside
Beta-lactam
AC C
EP
Bacitracin
Effluent
AS
ADS
30.1 7.34 0.02 1.36 25.8 5.42 1.65 27.0 1.55 1.25 3.92 10.1 4.80 3.94 2.21 0.31 1.92 0.11 2.36 1.05
2.84 6.16 0.04 0.05 1.14 4.99 0.10 1.81 0.09 0.36 1.19 0.80 0.39 1.59 0.70 0.05 1.86 0.08 0.04 0.02
3.15 0.81 0.01 0.02 0.86 0.38 0.04 0.68 0.06 0.24 1.70 1.07 0.16 0.21 0.95 0.02 0.17 0.05 0.05 0.01
4.35 0.81 0.05 0.08 0.99 0.60 0.02 0.81 0.13 0.06 0.74 0.40 0.19 0.15 0.30 0.04 0.23 0.03 0.07 0.03
SC
Acriflavine
Influent
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ARG Subtype
TE D
ARG Type
RI PT
Average Abundance/ppm
Removal Rate/% From From AS Influent to to ADS Effluent 99.88 31.02 98.91 49.76 97.12 -125.2 99.96 -107.8 99.94 42.29 98.81 21.29 99.92 79.18 99.91 40.60 99.92 -0.41 99.63 88.03 99.60 78.22 99.90 81.14 99.89 40.89 99.48 65.64 99.59 84.06 99.81 -29.53 98.74 30.91 99.10 68.82 99.98 31.29 99.98 -50.10
VEB-1
1.01
0.32
0.56
0.34
99.59
69.89
VEB-3
0.09
0.05
0.10
0.05
99.37
77.95 S9
ACCEPTED MANUSCRIPT
AC C
Multidrug
EP
TE D
MLS
0.05 0.05 0.21 0.22 0.15 0.08 0.12 0.04 0.44 0.05 0.04 0.19 0.75 0.08 0.04 0.02
0.08 0.05 0.02 0.03 0.33 0.02 0.58 0.34 1.41 0.34 0.16 1.86 0.69 2.59 1.41 0.26
99.41 99.90 99.74 99.88 99.92 99.94 99.99 99.87 94.39 99.61 99.99 99.98 99.81 99.99 99.98 99.98
20.21 51.10 96.40 93.18 -12.51 90.39 -139.1 -359.7 -62.18 -221.4 -83.79 -382.2 54.38 -1493 -1629 -532.1
15.9
2.03
0.85
1.23
99.83
27.32
9.79 3.98 2.85 2.84 2.16 0.34 1.60 14.8 4.10
0.33 0.21 0.19 0.17 0.63 0.03 0.95 2.04 0.18
0.18 0.02 0.02 0.01 1.45 0.12 0.16 1.38 0.02
0.08 0.02 0.02 0.02 0.76 0.06 0.06 1.08 0.03
99.96 99.93 99.92 99.92 99.62 99.87 99.23 99.82 99.94
78.96 53.22 53.22 24.95 73.87 75.67 81.90 60.92 6.450
RI PT
0.16 0.10 0.20 0.14 0.09 0.26 0.19 0.17 1.72 0.14 0.02 0.17 0.50 0.03 0.03 0.02
SC
0.36 1.30 0.99 1.55 1.50 5.31 16.3 1.68 0.40 0.47 2.78 9.27 3.36 4.47 2.39 1.23
M AN U
Chloramphenicol
VIM-11 chloramphenicol acetyltransferase * chloramphenicol and florfenicol exporter cmlA9 florfenicol exporter * ABC-type efflux carrier * ermB * ermF erythromycin esterase erythromycin esterase type 1 erythromycin ribosome methylase ** macB ** macrolide transporter macrolide-efflux protein mef gene * mefA * hydrophobe_amphiphile efflux-1 (HAE1) family protein mdtF * mdtG * mdtH * mdtN * mexB mexD mexF multidrug efflux protein multidrug resistance protein D *
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ACCEPTED MANUSCRIPT
0.05 0.02 0.01 0.07 0.13 0.03 0.63 0.86 2.64
0.28 0.13 0.05 0.05 0.07 0.42 0.89 0.35 3.06
99.96 99.66 99.93 99.73 99.88 99.90 99.42 99.83 97.55
-163.8 -211.7 -125.2 65.91 72.95 -689.1 28.58 79.72 41.93
0.92
0.05
0.09
0.19
99.93
-4.060
31.1 2.72 0.28 1.25 0.63 2.12 0.73 3.10 33.3 3.85 9.57 7.59 0.19 31.2 0.25 32.6 1.80 0.14
0.31 0.12 0.19 2.53 0.02 0.19 0.05 0.36 0.65 0.02 0.66 0.42 0.34 1.18 0.02 1.61 0.07 0.19
0.56 0.03 0.08 0.10 0.04 0.02 0.07 0.65 0.88 0.02 0.15 1.14 0.05 0.59 0.20 0.67 0.01 0.51
1.95 0.06 0.21 0.62 0.13 0.04 0.16 0.23 4.85 0.22 0.35 0.77 0.06 3.33 0.02 1.11 0.02 0.09
99.99 99.94 99.14 97.39 99.97 99.88 99.92 99.85 99.97 99.99 99.91 99.93 97.67 99.95 99.92 99.94 99.95 98.29
-73.64 -0.07 -28.69 -203.6 -60.19 -29.53 -10.51 82.35 -175.3 -436.9 -14.57 66.20 38.57 -180.4 96.10 17.54 24.95 91.32
RI PT
0.27 0.52 0.02 0.12 0.27 0.14 8.72 0.33 15.6
AC C
EP
Tetracycline
TE D
M AN U
Sulfonamide
8.14 1.98 0.33 0.56 3.04 1.78 19.4 2.54 8.24
SC
Polymyxin Quinolone
multidrug transporter * norM RND multidrug efflux transporter * sdeY efflux transporter bifunctional polymyxin resistance protein arnA quinolone resistance protein sul2 sul1 ribosomal protection tetracycline resistance protein * tet32 * tetA * tetA(33) tetA(C) tetA(M) * tetA(Q) tetB(P) * tetG tetM * tetO ** tetQ * tetracycline resistance protein * tetV tetW * tetX * hypothetical protein * proton antiport protein * RND protein
Others
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ACCEPTED MANUSCRIPT
6.87
1. MLS : Macrolide-lincosamide-streptogramin. 2. **: subtypes with removal rate larger than 99.99% from influent to effluent.
0.21
0.02
RI PT
2.00
0.40
0.03
0.02
99.86
53.22
0.43
99.93
-551.3
SC
transporter, monovalent cationproton antiporter-2 (CPA2) family UDP-glucuronic acid decarboxylase_UDP-4-amino-4-deoxy-L-arabinose formyltransferase *
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3. *: subtypes with removal rate between 99.90% and 99.99% from influent to effluent. 4.
: subtypes with removal rate between 90.00% and 99.90% from activated sludge to anaeorbic digestion sludge.
5.
: subtypes that were enriched by more than 10 times (removal rate lower than -1000%) from activated sludge to anaerobic digestion
AC C
EP
TE D
sludge.
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ACCEPTED MANUSCRIPT
Table S7 - Removal efficiency of ARGs in previous studies and this study.
tetB
---
---
---
60 ~ 92
tetC
---
---
---
---
tetG
---
---
---
---
tetL
---
---
---
tetM
> 99
68 ~ 99
tetO
> 99
tetQ
SC
tetA
Water (Chen and Zhang, 2013a) ---
ARGs
This study/%
RI PT
Removal efficiency in previous studies/% Water Water Water Sludge (Chen and (Liu et al., (Nõlvak et al., (Diehl and LaPara, Zhang, 2013b) 2013) 2013) 2010) ----49 ~ 98 90~99
Sludge (Ma et al., 2011) ---
Persistent ARGs
Water
Sludge
99.94
-0.07
---
100
100
---
-525 ~ 75
97.39
-203.6
---
-66 ~ 92
99.85
82.35
---
98
---
100
N. A
-77 ~ 82
68
---
---
99.97
-175.3
68 ~ 99
-46 ~ 57
---
90~99
-233 ~ 83
99.9
-436.9
> 99
68 ~ 99
---
---
---
---
99.99
-14.57
tetW
> 99
68 ~ 99
-50 ~ 67
---
99
-525 ~ 75
99.95
-180.4
tetX
---
---
---
---
99
-122 ~ 94
99.92
96.10
sulI
90 ~ 99
50 ~ 97
---
-298 ~ 75
---
84 ~ 99
97.55
41.93
sulII
---
---
---
---
---
45 ~ 90
99.83
79.72
ermB
---
---
---
21 ~ 60
---
-233 ~ 73
99.99
-139.1
ermF
---
---
---
---
-1900 ~ 86
99.87
-359.7
AC C
EP
TE D
M AN U
---
---
ampC ------90 ~ 92 ----99.95 N.A qnrS ------87 ~ 97 ----100 N.A The abundance of ARGs used in the calculating removal efficiency was estimated from the bar chart in the previous studies if the abundance of ARGs were not clearly reported in numbers. S13
ACCEPTED MANUSCRIPT Table S8 - Top 5 genera in the influent, effluent, AS and ADS
Digestion
Firmicutes
Blautia
12.1
Proteobacteria
Acinetobacter
9.56
Actinobacteria
Bifidobacterium
5.56
Proteobacteria
Moraxella
4.74
Actinobacteria
Mycobacterium
30.7
Proteobacteria
Francisella
6.51
Proteobacteria
Vibrio
3.88
Bacteroidetes
Flavobacterium
3.41
Spirochaetes
Leptospira
2.49
RI PT
12.7
Nitrospira
8.76
Actinobacteria
Mycobacterium
7.04
Bacteroidetes
Lewinella
5.08
Planctomycetes
Planctomyces
4.94
Proteobacteria
Denitromonas
4.65
Thermotogae
Kosmotoga
36.1
Synergistetes
Thermovirga
5.34
Actinobacteria
Mycobacterium
5.31
Chloroflexi
Longilinea
3.64
Syntrophomonas
3.23
EP
Sludge
Streptococcus
TE D
Anaerobic
Firmicutes
Nitrospirae Activated Sludge
Percentage/%
M AN U
Effluent
genus
SC
Influent
phylum
AC C
Firmicutes
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ACCEPTED MANUSCRIPT Table S9 - Persistent ARGs subtypes in each cluster in RDA
1
ARGs types Aminoglycoside MLS Others Aminoglycoside
2
RI PT
Beta-lactam Multidrug Quinolone Sulfonamide
SC
Tetracycline Acriflavine
Aminoglycoside
AC C
EP
3
TE D
Bacitracin
Beta-lactam
ARGs subtypes aminoglycoside 2-N-acetyltransferase erythromycin esterase RND protein aadA aminoglycoside acetyltransferase class A beta-lactamase OXA-10 mexF norM quinolone resistance protein sul1 tetA(C) tetV acrB aminoglycoside 3-phosphotransferase aminoglycoside 6-phosphotransferase aminoglycoside resistance protein aminoglycoside resistance protein A aminoglycoside resistance protein B streptomycin phosphotransferase_strB undecaprenol kinase undecaprenyl-diphosphatase beta-lactamase NPS-1 OXA gene OXA-46 penicillin binding protein penicillin-binding protein 2 VEB-1 VEB-3 VIM-11 chloramphenicol acetyltransferase chloramphenicol and florfenicol exporter cmlA9 florfenicol export er ABC-type efflux carrier ermB ermF erythromycin esterase type 1 erythromycin ribosome methylase macrolide efflux protein macB macrolide transporter mefA
M AN U
Cluster
Chloramphenicol
MLS
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ACCEPTED MANUSCRIPT
SC
RI PT
Multidrug
mef gene hydrophobe_amphiphile efflux-1 (HAE1) family protein mdtF mdtG mdtH mdtN mexB mexD multidrug efflux protein multidrug resistance protein D multidrug transporter multidrug efflux pump sdeY transporter hypothetical protein proton antiport protein transporter, monovalent cationproton antiporter-2 (CPA2) family UDP-glucuronic acid decarboxylase_UDP-4-amino-4-deoxy-L-arabinose formyltransferase bifunctional polymyxin resistance protein arnA dihydropteroate synthase_sul2 ribosomal protection tetracycline resistance protein tetracycline resistance protein tet32 tetA tetA(33) tetA(M) tetA(Q) tetB(P) tetG tetM tetO tetQ tetW tetX
M AN U
Others
AC C
EP
Tetracycline
TE D
Polymyxin Sulfonamide
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ACCEPTED MANUSCRIPT Table S10 - Total abundances of ARGs in different clusters from RDA Cluster
Total abundance/ppm Effluent
AS
ADS
1
0.55
1.94
0.95
1.55
2
51.3
43.3
5.16
6.61
3
412
25.7
22.3
36.0
AC C
EP
TE D
M AN U
SC
RI PT
Influent
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AC C
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
Fig. S1 - Flowchart of the ST WWTP and the sampling site of the samples of influent (IN-1 and IN-2), effluent (EF-1 and EF-2), activated sludge (AS-1 and AS-2) and anaerobic digestion sludge (ADS-1 and ADS-2).
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EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
AC C
Fig. S2 - Percentage of the persistent ARGs types in influent, effluent, AS and ADS.
S19
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
AC C
Fig. S3 - Microbial compositions at phylum level in influent, effluent, AS and ADS.
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ACCEPTED MANUSCRIPT REFERENCES Chen, H. and Zhang, M., 2013a. Effects of advanced treatment systems on the removal of antibiotic resistance genes in wastewater treatment plants from Hangzhou, China. Environmental Science & Technology http://dx.doi.org/10.1021/es401091y. Chen, H. and Zhang, M., 2013b. Occurrence and removal of antibiotic resistance genes in municipal wastewater
RI PT
and rural domestic sewage treatment systems in eastern China. Environment International 55, 9-14. Diehl, D.L. and LaPara, T.M., 2010. Effect of temperature on the fate of genes encoding tetracycline resistance and the integrase of class 1 integrons within anaerobic and aerobic digesters treating municipal wastewater solids. Environmental Science & Technology 44(23), 9128-9133.
SC
Liu, L., Liu, C., Zheng, J., Huang, X., Wang, Z., Liu, Y. and Zhu, G., 2013. Elimination of veterinary antibiotics and antibiotic resistance genes from swine wastewater in the vertical flow constructed wetlands.
M AN U
Chemosphere 91(8), 1088-1093.
Ma, Y., Wilson, C.A., Novak, J.T., Riffat, R., Aynur, S., Murthy, S. and Pruden, A., 2011. Effect of various sludge digestion conditions on sulfonamide, macrolide, and tetracycline resistance genes and class I integrons. Environmental Science & Technology 45(18), 7855-7861.
Nõlvak, H., Truu, M., Tiirik, K., Oopkaup, K., Sildvee, T., Kaasik, A., Mander, Ü. and Truu, J., 2013. Dynamics of antibiotic resistance genes and their relationships with system treatment efficiency in a horizontal
AC C
EP
TE D
subsurface flow constructed wetland. Science of The Total Environment 461-462, 636-644.
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