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Supporting Information

Exploring abundance, diversity and variation of a widespread antibiotic

resistance gene in wastewater treatment plants

Ziyan Wei a,c, Kai Feng a,c, Shuzhen Li a,d, Yu Zhang b,c, Hongrui Chen b,c, Huaqun Yin e, Meiying

Xu f, Ye Deng a,c*

a Key Laboratory of Environmental Biotechnology of CAS, Research Center for Eco-

Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China

b 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, China), 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

Correspondence

Ye Deng

E-mail: [email protected]

Number of pages: 17

Number of additional text: 1

11 Tables and 1 Figures

Table of contents

Text S1 Methods

SPAGE \* MERGEFORMAT17

Table S1 Operational parameters of five urban sewage WWTPs

Table S2 Wastewater characteristics in five WWTPs in two seasons

Table S3 Primer pairs commonly used for the amplification of sul1 gene including target length

and primer coverage

Table S4 Quantitative amplification conditions and quality control of qPCR methods

Table S5 Dissimilarity test of bacterial communities among different sites and seasons based on

Bray-curtis

Table S6 Pearson correlations among different wastewater and operational parameters

Table S7 Correlations between bacterial community and environmental properties analyzed by

Mantel test based on Bray-curtis and Jaccard

Table S8 Initial and corrected bacterial quantity in different WWTPs between summer and winter

(average total abundance of OTUs)

Table S9 Dissimilarity test of total sul1-clusters in activated sludge among different sites and

seasons based on Bray-curtis

Table S10 Pearson correlation between relative abundance of dominant bacterial genera and major

clusters of sul1 gene

Table S11 The correlation between absolute abundance of sul1 and int1 genes in activated sludge

Fig. S1 Bacterial composition of activated sludge from five WWTPs in summer and winter at the

class-level


Text S1 Methods

DNA Extraction. 1 mL of each sample was centrifuged at 12,000×g for 10 min at 4 °C. Then 0.25

gram sediment (wet weight) after centrifugation was collected for DNA extraction in duplicate

with a FastDNA SPIN kit for soil (Qbiogene, Solon OH) according to the manufacturer’s manual.

During each sampling season, 6 replicate DNA samples (3 biological replicates × 2 experimental

replicates) were obtained from each wastewater treatment plant. The environmental DNA was

detected by 1% agarose gel electrophoresis and quantified with NanoDrop 2000 (Nanodrop,

USA).

Specificity assessment of the degenerate primers for sul1 gene. Experimentally, the PCR

products amplified with 51F and 280R were ligated to pMD18-T vector (TAKARA) and inserted

into E-coli., then plasmids were extracted from positive clones by the TIANpure Mini Plasmid kit

(TianGen, China) and sequenced by Sangon Biotech (Shanghai) Co., Ltd. The positive clones

were selected by penbritin and blue-white spot screening, they were annotated as sul1 gene and

there were no sequences annotated as other antibiotic resistance genes by BLASTn with the

Genbank nucleotide database. In this way, the specificity of the new primers was satisfactory, and

the standard plasmid of sul1 gene was obtained for further qPCR experiment.

Protocols of quantification of sul1, int1, and 16S rRNA genes by qPCR. Quantitative PCR was

performed with the CFX96 Touch Real-Time PCR Detection System (BioRad). The standard

plasmids were extracted from the Escherichia coli containing sul1, int1, and 16S rRNA genes by

the TIANpure Mini Plasmid kit (TianGen, China). The standard curves prepared from serial 10-

fold dilutions of standard plasmids covering 5-8 orders of magnitude, with high specificity of

primers, R2 value higher than 0.99, and proper amplification efficiencies (90 - 110%), could ensure

reliable quantification (Table S4).The qPCR program consisted of initial denaturing for 15 min at

95 C, followed by 40 cycles of 10 sec at 95 C, 30 sec at annealing temperatures (60 C, 58 C,

and 50 C for sul1, int1 and 16S rRNA genes, respectively), and a final melt curve stage with

temperature ramping form 50 C to 95 C (0.5 per read, 5 sec hold).

Amplification of 16S rRNA, sul1 genes before sequencing. PCR amplification was performed

in a mixture of 50 μL, including 5 μL DNA template, 2.5 U of Taq DNA Polymerase (TaKaRa), 1

× Taq buffer, 75 μM dNTP and 0.3 μM of each primer. Total 30 cycles (94 C for 20 sec, annealing


temperature (57 C for 16S rRNA gene or 60 C for sul1 gene) for 25 sec, and 68 C for 45 sec)

were performed after 1 min of denaturation at 94 C, followed by final extension at 68 C for 10

min. PCR products were detected by agarose gel electrophoresis, and purified by recovering the

gel using Gel Extraction Kit (OMEGA BioTek).

Sequence data processing. Raw sequencing data were preprocessed and analyzed by the

following tools integrated in an in-house Galaxy Pipeline (http://mem.rcees.ac.cn:8080/),

including FLASH program (Magoc and Salzberg 2011) for combination of paired-end reads,

Btrim (Kong 2011) for sequence quality trimming, Uparse (Edgar 2013) and Uclust (Edgar 2010)

for chimeras detection and operational taxonomy units (OTUs) generation, and taxonomy

assignment of 16S rRNA sequences against the Greengenes database (DeSantis et al. 2006).


Table S1 Operational parameters of five urban sewage WWTPs

WWTPsSecondary

treatment

HRT

(h)

SRT

(d)

Actual

treatment

capacity (m3/d)

Population

equivalent

(104)

Sludge

yield

(kg/d)

Influent

composition

BJ MBR 16.5 20 150,000 22 88,000 domestic sewage

QDN A/A/O 21 19 170,000 80 126,000

mainly domestic

sewage and part of

industrial sewage

QDS A/A/O 21 13 100,000 26 11,800

mainly domestic

sewage and part of

industrial sewage

WXNC-orbal

OD20 15 150,000 50 120,000

mainly domestic

sewage and part of

industrial sewage

WXS A/A/O 18 19 100,000 82 89,000 domestic sewage

MBR: Membrane bio-reactor; A/A/O: Anaerobic-anoxic-oxic; C-orbal OD: Combined orbal oxidation ditch; HRT:

Hydraulic retention time; SRT: Sludge Retention Time


Table S2 Wastewater characteristics in five WWTPs in two seasons

SampleDO

(mg/L)pH

Temper

ature

(C)

COD_

inflow

(mg/L)

COD_

effluent

(mg/L)

TN_

inflow

(mg/L)

TN_

effluent

(mg/L)

TP_

inflow

(mg/L)

TP_

effluent

(mg/L)

BJ6 2.14 7.15 21.30 225.00 30.00 65.09 8.34 5.50 0.50

BJ12 2.02 7.19 11.57 225.00 30.00 65.09 8.34 5.50 0.50

QDN6 1.77 7.04 23.90 950.00 50.00 80.00 8.00 13.00 0.50

QDN12 2.20 7.13 11.17 950.00 50.00 80.00 8.00 13.00 0.50

QDS6 1.89 7.14 25.40 1000.00 50.00 100.00 15.00 15.00 0.50

QDS12 2.17 7.02 12.87 1000.00 50.00 100.00 15.00 15.00 0.50

WXN6 2.05 6.83 24.47 344.28 28.44 34.39 13.58 3.68 0.23

WXN12 1.72 7.33 14.93 344.28 28.44 34.39 13.58 3.68 0.23

WXS6 1.91 7.17 25.70 220.92 30.69 37.82 12.84 6.92 0.38

WXS12 1.81 7.17 14.80 220.92 30.69 37.82 12.84 6.92 0.38

DO: dissolved oxygen; COD: chemical oxygen demand; TN: total nitrogen; TP: total phosphorus.


Table S3 Primer pairs commonly used for the amplification of sul1 gene including target length

and primer coverage

Primer name

Targeted

length

(bp)

Primer sequences (5′-3′)

Primer coverage (%)

(0 Mismatch)

Updated sul1

gene sequence

database

Sul1-

ARDB

Degenerate Primer 23551F: AAATGCTGCGAGTYGGMKCA 86.41 99.07

280R: AACMACCAKCCTRCAGTCCG 83.39 93.81

sul1_1

(Van et al. 2008)822

F: TTCGGCATTCTGAATCTCAC 85.25 97.83

R: ATGATCTAACCCTCGGTCTC 70.62 79.57

sul1_2

(Ma et al. 2007)793

F: TTTCCTGACCCTGCGCTCTAT 85.02 97.21

R: GTGCGGACGTAGTCAGCGCCA 76.66 88.24

sul1_3

(Kerrn et al. 2002)433

F: CGGCGTGGGCTACCTGAACG 84.55 97.21

R: GCCGATCGCGTGAAGTTCCG 77.24 89.16

sul1_4

(Pyatov et al. 2014)160

F: GACGAGATTGTGCGGTTCTT 82.11 92.88

R: CCGACTTCAGCTTTTGAAGG 77.93 89.16

sul1_5

(Toleman et al. 2006)840

F: ATGGTGACGGTGTTCGGCATTCTGA 81.04 93.81

R: CTAGGCATGATCTAACCCTCGGTCT 70.17 79.26

sul1_6

(Sáenz et al. 2004)789

F: TGGTGACGGTGTTCGGCATTC 81.42 93.81

R: GCGAGGGTTTCCGAGAAGGTG 67.83 76.78

sul1_7

(Song et al. 2010)134

F: GATTTTTCTTGAGCCCCGC 79.67 90.71

R: TGGACCCAGATCCTTTACAGG 77.70 89.47

sul1_8

(Chang et al. 2007)201

F: GGATTTTTCTTGAGCCCCGC 79.67 90.71

R: CATTGCCGATCGCGTGAAGT 76.66 88.54

sul1_9

(Luo et al. 2010)158

F: CACCGGAAACATCGCTGCA 79.21 91.33

R: AAGTTCCGCCGCAAGGCT 77.47 89.16

sul1_10

(Czekalski et al. 2014)67

F: CCGTTGGCCTTCCTGTAAAG 77.58 89.16

R: TTGCCGATCGCGTGAAGT 77.12 89.16

Table S4 Quantitative amplification conditions and quality control of qPCR methods

Target Primers Amplicon Annealing R2 Efficiency Slope


gene size (bp) temperature (C) (%)

16S

rRNA

341F, 534R

(Koike et al. 2007)193 50 0.99 101 -3.307

int1int-F, int-R

(Zhao et al. 2001)280 58 0.99 102 -3.270

sul1

51F, 280R 235 60 0.99 99 -3.339

sul1_1 822 55 0.99 100 -3.313

sul1_2 793 55 0.99 91 -3.568

sul1_3 433 60 0.99 90 -3.602

sul1_4 160 60 0.99 109 -3.131


Table S5 Dissimilarity test of bacterial communities among different sites and seasons based on

Bray-curtis index

SampleMRPP ANOSIM PERMANOVA

Delta P R P F P

BJ-QDN 0.343 0.001 0.470 0.001 0.286 0.001

BJ-QDS 0.343 0.002 0.470 0.001 0.286 0.001

BJ-WXN 0.343 0.001 0.470 0.003 0.286 0.001

BJ-WXS 0.343 0.002 0.470 0.001 0.286 0.001

QDN-QDS 0.332 0.001 0.847 0.001 0.499 0.001

QDN-WXN 0.363 0.001 0.718 0.001 0.474 0.001

QDN-WXS 0.330 0.001 0.847 0.001 0.493 0.001

QDS-WXN 0.316 0.001 0.847 0.001 0.633 0.001

QDS-WXS 0.283 0.001 0.847 0.001 0.703 0.001

WXN-WXS 0.314 0.001 0.847 0.001 0.475 0.001

BJ6-BJ12 0.258 0.005 0.563 0.002 0.523 0.001

QDN6-QDN12 0.246 0.004 0.722 0.004 0.476 0.001

QDS6-QDS12 0.148 0.003 0.723 0.001 0.790 0.001

WXN6-WXN12 0.282 0.002 0.709 0.002 0.282 0.001

WXS6-WXS12 0.178 0.002 0.470 0.001 0.663 0.001

Values in bold indicate significant difference


Table S6 Pearson correlations among different wastewater and operational parameters

SampleCOD

_inflow

COD

_effluent

TN

_inflow

TN

_effluent

TP

_inflow

TP

_effluentDO pH

temperat

ure

COD_

inflow1 0.979* 0.842* 0.067 0.933* 0.503* 0.142 -0.153 -0.025

COD_

effluent1 0.876* -0.031 0.976* 0.638* 0.161 -0.123 -0.040

TN_inflow 1 -0.084 0.889* 0.821* 0.320 -0.077 -0.105

TN_effluent 1 0.069 -0.462* -0.184 -0.064 0.206

TP_inflow 1 0.689* 0.160 -0.098 -0.022

TP_effluent 1 0.337 0.033 -0.059

DO 1 0.069 -0.462*

pH 1 0.689*

temperature 1

COD: chemical oxygen demand; TN: total nitrogen; TP: total phosphorus; DO: dissolved oxygen.

Values in bold indicate significant correlation, * Correlation is significant at 0.05 level.


Table S7 Correlations between bacterial community and environmental properties analyzed by

Mantel test based on Bray-curtis and Jaccard

Environmental factorsBray-curtis Jaccard

rM P rM P

Geographical distance 0.499 0.001 0.482 0.001

DO 0.110 0.005 0.093 0.008

pH 0.041 0.164 0.028 0.196

Temperature 0.181 0.001 0.143 0.001

COD_inflow 0.579 0.001 0.651 0.001

TP_effluent 0.403 0.001 0.388 0.001



Table S8 Initial and corrected bacterial quantities in different WWTPs between summer and winter

(average total abundance of OTUs)

WWTPsInitial bacterial quantity Corrected bacterial quantity

Ratios of corrected bacterial

quantity / initial bacterial quantity

summer winter summer winter summer winter

BJ 108483.67 82898.83 50730.95 37730.19 46.66% 45.54%

QDN 36222.00 36564.33 14850.43 16081.74 42.33% 44.08%

QDS 59496.67 82652.33 25440.60 34639.84 42.62% 42.52%

WXN 36435.17 64572.17 17188.18 27375.40 47.15% 46.25%

WXS 75808.50 40543.83 34933.23 18318.36 45.90% 45.19%


Table S9 Dissimilarity test of total sul1-clusters in activated sludge among different sites and

seasons based on Bray-curtis

SampleMRPP ANOSIM PERMANOVA

Delta P R P F P

BJ-QDN 0.008 0.111 0.094 0.062 0.045 0.333

BJ-QDS 0.008 0.115 0.094 0.075 0.045 0.317

BJ-WXN 0.008 0.091 0.094 0.069 0.045 0.358

BJ-WXS 0.108 0.128 0.094 0.061 0.045 0.349

QDN-QDS 0.008 0.465 0.005 0.345 0.022 0.634

QDN-WXN 0.009 0.587 0.034 0.696 0.031 0.482

QDN-WXS 0.008 0.323 0.029 0.226 0.041 0.366

QDS-WXN 0.008 0.595 0.026 0.636 0.040 0.408

QDS-WXS 0.006 0.511 0.030 0.639 0.034 0.457

WXN-WXS 0.008 0.092 0.063 0.116 0.093 0.110

BJ6-BJ12 0.009 0.180 0.131 0.136 0.071 0.417

QDN6-QDN12 0.009 0.121 0.186 0.112 0.219 0.085

QDS6-QDS12 0.006 0.028 0.269 0.032 0.239 0.058

WXN6-WXN12 0.010 0.683 0.036 0.553 0.058 0.553

WXS6-WXS12 0.006 0.635 0.102 0.830 0.065 0.476



Table S10 Pearson correlation between relative abundance of dominant bacterial genera and major

clusters of sul1 gene

Genus Relative abundanceCluster_109 Cluster_130 Cluster_108

r P r P r P

Caldilinea 8.32% 0.347 0.326 0.374 0.287 0.356 0.312

Nitrospira 8.14% -0.393 0.261 -0.434 0.210 -0.461 0.180

Hyphomicrobium 5.91% -0.297 0.404 -0.301 0.398 -0.326 0.359

SMB53 5.70% -0.182 0.615 -0.167 0.645 -0.203 0.574

Candidatus

Microthrix4.89% 0.173 0.632 0.156 0.666 0.130 0.720

Dechloromonas 3.73% -0.040 0.913 -0.077 0.833 -0.099 0.785

Mycobacterium 2.75% 0.371 0.292 0.375 0.286 0.395 0.259

Rubrivivax 2.33% -0.375 0.285 -0.375 0.285 -0.377 0.283

Turicibacter 1.67% -0.101 0.782 -0.085 0.816 -0.117 0.747

Planctomyces 1.65% 0.317 0.372 0.342 0.333 0.356 0.313

Rhodobacter 1.57% 0.144 0.691 0.096 0.791 0.046 0.900

Dokdonella 1.56% 0.357 0.311 0.321 0.365 0.343 0.331

Gemmata 1.54% -0.280 0.433 -0.249 0.487 -0.198 0.584

Allochromatium 1.48% 0.313 0.379 0.336 0.342 0.395 0.258

Thermomonas 1.46% 0.422 0.224 0.375 0.285 0.358 0.310

Paracoccus 1.37% -0.006 0.987 -0.030 0.935 -0.098 0.788

Phycicoccus 1.27% 0.119 0.743 0.120 0.741 0.088 0.809

Clostridium 1.21% 0.174 0.630 0.179 0.622 0.204 0.573

Dok59 1.01% -0.183 0.612 -0.177 0.626 -0.216 0.550


Table S11 The correlation between absolute abundance of sul1 and int1 genes in activated sludge

CorrelationPearson Kendall Spearman

r P r P r P

All 0.200 0.126 0.165 0.063 0.248 0.056

BJ 0.832 0.001 0.595 0.007 0.729 0.007

QDN 0.409 0.186 0.121 0.583 0.210 0.513

QDS 0.282 0.375 0.212 0.337 0.413 0.183

WXN -0.185 0.564 0.121 0.583 0.196 0.542

WXS 0.290 0.360 0.273 0.217 0.357 0.255

BJ6 0.753 0.084 0.600 0.091 0.714 0.111

BJ12 0.873 0.023 0.600 0.091 0.771 0.072

QDN6 0.022 0.967 -0.333 0.348 -0.486 0.329

QDN12 0.584 0.224 0.333 0.348 0.486 0.329

QDS6 -0.264 0.613 -0.333 0.348 -0.371 0.468

QDS12 0.547 0.262 0.467 0.188 0.714 0.111

WXN6 0.446 0.375 0.200 0.573 0.257 0.623

WXN12 -0.658 0.156 -0.333 0.348 -0.374 0.468

WXS6 0.367 0.475 0.467 0.188 0.600 0.208

WXS12 -0.214 0.683 -0.067 0.851 -0.086 0.872

Values in bold indicate significant correlation


Fig. S1 Bacterial composition of activated sludge from five WWTPs in summer and winter at the

class-level


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