The Prevalence of Integrons as the Carrier of Antibiotic Resistance

Apr 20, 2017 - integrons could carry diverse antibiotic resistance genes (ARGs) and conduct ... The overuse and misuse of antibiotics not only for hum...
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The Prevalence of Integrons as the Carrier of Antibiotic Resistance Genes in Natural and Man-made Environments Liping Ma, An-dong Li, Xiao-Le Yin, and Tong Zhang Environ. Sci. Technol., Just Accepted Manuscript • Publication Date (Web): 20 Apr 2017 Downloaded from http://pubs.acs.org on April 20, 2017

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Environmental Science & Technology

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The Prevalence of Integrons as the Carrier of Antibiotic Resistance Genes in Natural and Man-made Environments

2 3 4 5 6

Authors: Liping Ma1, An-Dong Li1, Xiao-Le Yin1 and Tong Zhang1,2*

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Author affiliation:

8

1

Environmental Biotechnology Laboratory, The University of Hong Kong, Hong Kong

9

2

International Center for Antibiotic Resistance in the Environment, School of

10

Environmental Science and Engineering, Southern University of Science and Technology,

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Shenzhen, China

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Corresponding author:

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Prof. Tong Zhang

14

Email: [email protected] or [email protected]

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ABSTRACT

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Class 1 integrase intI1 has been considered as a good proxy for anthropogenic pollution

17

because of being linked to genes conferring resistance to antibiotics. The gene cassettes of

18

class 1 integrons could carry diverse antibiotic resistance genes (ARGs) and conduct

19

horizontal

20

high-throughput sequencing technique combined with an intI1 database and genome

21

assembly to quantify the abundance of intI1 in 64 environmental samples from 8

22

ecosystems, and to investigate the diverse arrangements of ARG-carrying gene cassettes

23

(ACGCs) carried by class 1 integrons. The abundance of detected intI1 ranged from 3.83

24

× 10-4 to 4.26 × 100 intI1/cell. High correlation (Pearson‟s r = 0.852) between intI1 and

25

ARG abundance indicated that intI1 could be considered as an important indicator of

26

ARGs in environments. Aminoglycoside resistance genes were most frequently observed

27

on gene cassettes, carried by 57% assembled ACGCs, followed by trimethoprim and

28

beta-lactam resistance genes. This study established the pipeline for broad monitoring of

29

intI1 in various environmental samples and scanning the ARGs carried by integrons.

30

These findings supplemented our knowledge on the distribution of class 1 integrons and

31

ARGs carried on mobile genetic elements, benefitting future studies on horizontal gene

32

transfer of ARGs.

gene

transfer

among

microorganisms.

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The

present

study

applied

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Environmental Science & Technology

TOC art Variable region

Class 1 integron

P intI1 5‟ -CS

Gene cassette

P

P sulI

ORF5 3‟ -CS

qacE△1

P AMI

10

Feces & Wastewater from livestock farm

BET

* TET – 31%

Feces & Wastewater from livestock farm

CHL ERY

*

STP Influent

1

copy of intI1/cell

SUL TET TRI

* AMI – 29%

STP Effluent

0.1

AMI – 39%

AMI-AMI AMI-BET

AMI-CHL

* AMI – 26%

STP AS

AMI-ERY AMI-TRI

* AMI – 27%

STP ADS

0.01

BET-CHL BET-ERY

*

Drinking water

BET-TRI

AMI-TRI – 73%

ERY-ERY ERY-TRI

1E-3

*

River water

AMI – 37%

AMI-BET-AMI

1E-4

AMI-BET-BET AMI-CHL-AMI

P Eff lu en t ST P In Fe flu en fro ces t m & liv Wa es s to tew ck fa ater rm

r

AS P

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

AMI-TRI-BET

ST

S

ate kin

rin D

ST

g

P

w

AD

ate w iv er R

ST

dim

en

t

r

AMI-TRI-AMI

Se

35

AMI-AMI-AMI

* SUL – 36%

Sediment

Levels of intI1

Diversity of ARGs carried by gene cassettes

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INTRODUCTION

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The overuse and misuse of antibiotics not only for human therapy but also for

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livestock breeding have led to the emergence and prosperity of antibiotic resistance genes

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(ARGs) and antibiotic resistant bacteria (ARB) and attracted great concerns worldwide.1

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The sludge, sewage, and animal waste are considered as important reservoirs for ARGs,

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since abundant and diverse ARGs have been frequently discovered in these ecosystems.2, 3

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Mobile genetic elements (MGEs), including integrons, transposons and plasmids, can play

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an important role in transferring ARGs among microorganisms in environments.4

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Integrons could capture ARGs from environments and then incorporate onto their gene

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cassettes by site-specific recombination.5 Thus, integrons play a major role in antibiotic

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resistance development and horizontal gene transfer (HGT) of ARGs among bacteria in

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different environments.6, 7 Among different classes of integrons, class 1 integrons were

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most frequently detected in diverse environments.7 Its 5‟ conserved segment harbors

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integrase intI1 gene for integration and excision of gene cassettes, and its 3‟ conserved

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segment at the downstream of gene cassettes consisted of sulI and qacEΔ1, encoding

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resistance to sulfonamide and quaternary ammonium compounds, respectively.8

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The class 1 integrase, intI1, has been proposed as a proxy for anthropogenic

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pollution.6 Current concerns mainly focus on the characterization and quantification of

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integrons and ARG-carrying gene cassettes (ACGCs) from clinical isolates or

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environmental bacterial species using traditional PCR and qPCR methods.6, 9 However,

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biases may exist during PCR amplifications and traditional methods cannot work well for

57

the quantification of ACGCs, as amplification is limited by the conserved domain and the

58

length of the target sequence. Recently, the rapid development of next-generation

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sequencing technique makes it possible for the detection of integrons using large

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metagenomic data sets. Although the current integron-related database INTEGRALL10

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and program IntegronFinder5 have been developed to identify the integrons carried by

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bacteria based on sequences, they are especially designed for targeted bacterial genomes.

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They can be well applicable for integron annotation on whole genome, draft genome and

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long scaffolds, but not feasible for integron identification over large metagenomic short

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reads datasets (100-150 bp). Thus, the method that could be used to quickly quantify the

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intI1 levels in diverse environmental samples based on large metagenomic data sets as

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well as to clarify what types of ARGs or ARG combinations would be easily carried by

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integrons with the potential horizontal gene transfer, is in demand for anthropogenic

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pollution assessment.

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In the present study, the high-throughput sequencing (HTS) technique was combined

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with a constructed intI1 gene database and genome assembly approach to (1) quantify the

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abundance of intI1 in various environmental samples; and (2) investigate the diverse

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structures of ARG-carrying gene cassettes carried by class 1 integrons from different

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environments. Figure 1 presents the pipeline of this study, and the details are described in

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Materials and Methods.

76 77

MATERIALS AND METHODS

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Sample collection. Basic information of the 64 environmental samples in the present

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study is summarized in Table S1, including 23 water samples (drinking water, river water,

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sewage, swine wastewater and treated wastewater), 24 sludge samples (anaerobic

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digestion sludge and activated sludge), 10 feces samples (chicken and pig) and 7 sediment

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samples, in which 39 data sets, including 18 sludge samples, 6 sediments, 5 river water

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samples and 10 feces samples have been used in our previous studies of ARGs abundance

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survey in different environments.11,

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procedures is described in Supporting Information S1.

12

Detailed information of sample collection

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DNA extraction and sequencing. The genomic DNAs of all environmental samples

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were extracted separately using FastDNA SPIN Kit for Soil (MP Biomedicals, France)

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following the instruction. The concentration and purity of DNAs were determined using

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microspectrophotometry (NanoDrop® ND-1000, NanoDrop Technologies, US). 5 μg DNA

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for each sample was used for library construction and paired-end (2×100 bp reads)

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metagenomic sequencing on Illumina Hiseq2000 platform by Beijing Genomics Institute

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(BGI). The base-calling pipeline (Illumina Pipeline-0.3) was applied to process raw

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fluorescence images and call reads.13

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Construction of class 1 integrase intI1 database and annotation. The construction

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of class 1 integrase intI1 database was based on the sequences in National Center for

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Biotechnology Information (NCBI) database. Firstly, 146 nucleotide sequences were

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obtained in total by using the keyword search “intI1” (category of “Gene” in NCBI). After

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further dereplication and validation by comparing against Pfam and CDsearch database,

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50 sequences were retained, composing the preliminary intI1 database. To retrieve more

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intI1 sequences in NCBI database, the NCBI protein non-redundant (nr) database

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(downloaded on October 10th, 2016) was compared with the preliminary intI1 database

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under cutoff with E-value of 1e-5, and 104,964 sequences were obtained. Subsequently,

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after filtration under cutoff at 80% similarity and 50% hit length and removal of partial

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sequences, 301 candidate intI1 sequences were obtained. Then these candidate intI1

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sequences were compared against Pfam and CDsearch database for further validation

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(Figure 1).

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All the metagenomic sequences after quality control were searched for intI1-like

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sequences against constructed class 1 integrase intI1 database using BLASTX under an

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E-value ≤ 1e-5.4 A read was considered as an intI1-like sequence if its best BLASTX hit

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alignment to intI1 sequences in the customized database satisfied the cutoff values, i.e. ≥

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90% similarity and 50 nt hit length.14 To validate the above annotation results, 200

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sequences which have been annotated as intI1-like sequences were randomly extracted

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from the metagenomic data sets, and then aligned against NCBI database (based on best

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hits) for validation.11, 15 Finally, 98% of these 200 sequences were annotated as class 1

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integrase, perfectly matching the BLASTX results obtained by comparing against the

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customized class 1 integrase database. The abundance of intI1 was calculated according to

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the following equation, in the unit of „copy of intI1 per cell‟:

118 𝑛

119

Abundance = ∑

𝑁i (intI1-like sequence) ×100/𝐿i (intI1 reference sequence)

𝑖=1

N16S sequence ×100/L16S sequence

× N16S copy number

(1)

120 121

Where, 𝑁i (intI1-like sequence) is the number of the intI1-like reads annotated as a specific

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intI1 reference sequence; 𝐿i

123

sequence (bp); N16S sequence is the number of the 16S rRNA gene sequences identified from

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the metagenomic sequencing data by comparing with Greengenes database;11 L16S sequence is

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the average length of 16S rRNA genes in the Greengenes database,16 which was used as

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the reference database for the 16S sequence identification via the local BLAST

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approach,17 that is, 1432 bp was used in Equation (1); n is the number of the mapped intI1

(intI1 reference sequence)

is the length of the intI1 reference

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reference sequences; 100 is the sequence length (bp) of the metagenomic sequencing reads;

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N16S

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average copy number of 16S rRNA genes within cells was calculated using

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CopyRighter.18 The Mann-Whitney test (P-value) was implemented to compare whether

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the means of the intI1 abundances among various environments are significantly different,

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and the relative standard deviation (RSD) was used to measure the dispersion of samples

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belonging to the same environmental niche (Table S8).11 Statistical analyses were

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performed by PAleontological STatistics software (PAST, version 2.15).

copy number

is the average copy number of 16S rRNA genes per cell number. The

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Amplification of gene cassettes and sequencing. Seven environmental samples

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(influent, activated sludge, effluent, chicken (20-day and 80-day) and pig (1-month and

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8-month) fecal samples) with high level of class 1 integrase intI1 were selected for further

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study on arrangement of ARGs and associated genes loaded on gene cassettes carried by

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class 1 integron. The pretreatment and DNA extraction of these samples were reported in

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previous studies.11, 12 PCR reactions were performed to amplify variable gene cassettes of

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class 1 integrons using the primers 5‟CS-GGCATCCAAGCAGCAAG (1190-1206) and

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3‟CS-AAGCAGACTTGACCTGA (1342-1326)19, 20 under an initial denaturation at 94 oC

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for 5 min, followed by 30 cycles of 94 oC for 60 s, 55 oC for 45 s, and 72 oC for 5 min,

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with a final extension at 72 oC for 10 min. PCR products were analyzed by electrophoresis

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on 1% (w/v) agarose gel in 1× TAE buffer at 100 V for 20 min. Afterwards, 6 µg PCR

147

products for each sample were used for ~350 bp shotgun library construction and Illumina

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HTS on HiSeq2500 platform generating 2×125 bp paired-end reads (Groken Bioscience,

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Hong Kong, China). After filtering out those sequences with low quality,11 sequencing

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depths of 2.2 Gb were finally obtained for each of influent (Inf), effluent (Eff), activated

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sludge (AS), 20-day chicken feces (CF20), 80-day chicken feces (CF80), 1-month pig

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feces (PF1), and 8-month pig feces (PF8).

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Gene cassettes assembly of class 1 integrons. After quality control, metagenomic

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sequencing reads of gene cassettes carried by class 1 integrons were assembled using

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IDBA algorithm (version 1.1.1).21 The ORFs prediction was conducted using Prodigal

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(version 2).22 Then the predicted ORF sequences were searched for ARG-like ORFs

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against a structured ARG reference database15 using BLASTX under an E-value ≤ 1e-10,

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similarity ≥ 80% and hit length ≥ 70%.23 Afterwards, ARG-carrying gene cassettes

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(ACGCs) were extracted from assembled metagenomes. Then the presumed protein ORF

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sequences of ACGCs were compared with the local non-redundant NCBI NR database

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using BLASTP with an E-value ≤ 1e-2.24 Finally, all annotations and arrangements of

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ORFs on assembled gene cassettes of class 1 integrons were summarized and checked

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manually to analyze the distribution and co-occurrence of ARGs. The average coverage,

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indicating abundance (copy/Gb), of each assembled gene cassette was quantified by

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mapping metagenomic sequencing reads to the gene cassette using CLC Genomics

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Workbench with a minimum similarity of 95% and over 95% of the read length.12

167 168

RESULTS AND DISCUSSION

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Class 1 integrase intI1 database. After validation, 148 protein sequences of intI1

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were retained, composing the database of class 1 integrase intI1 (Table S2). In the

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constructed class 1 integrase database, the length of intI1 sequences varies from 115

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amino acids (aa) to 512 aa, with average length of 297 aa. These intI1 sequences

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originated from bacterial hosts covering 26 species, including Escherichia coli, Klebsiella

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pneumonia, Pseudomonas aeruginosa, Salmonella enterica, Acinetobacter baumannii,

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Enterobacter cloacae, etc. (Table S2).

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The abundance of class 1 integrase intI1 in various environments. The abundance

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of class 1 integrase intI1 was obtained through comparing the metagenomes against

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constructed intI1 database. Figure 2 shows the abundance of intI1 genes in 64 samples

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from 8 typical environments, including sediment, river water, drinking water, STP ADS,

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STP AS, STP effluent, STP influent, feces and wastewater from livestock farm, over a

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range of 3.83 × 10-4 – 4.26 × 100 copy of intI1/cell. Among these environmental niches,

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the abundance of intI1 follows sediment < river water < STP ADS < drinking water < STP

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AS < STP effluent < STP influent < feces & wastewater from livestock farm, with the

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abundances of 3.83 × 10-4 – 7.44 × 10-3 intI1/cell (sediment), 1.40 × 10-3 – 5.48 × 10-3

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intI1/cell (river water), 1.87 × 10-2 – 4.77 × 10-2 intI1/cell (STP ADS), 4.07 × 10-3 – 1.64 ×

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10-1 intI1/cell (drinking water), 2.23 × 10-2 – 8.90 × 10-2 intI1/cell (STP AS), 1.98 × 10-2 –

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1.82 × 10-1 intI1/cell (STP Effluent), 9.56 × 10-2 – 3.01 × 10-1 intI1/cell (STP Influent),

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3.34 × 10-3 – 4.26 × 100 intI1/cell (feces & wastewater from livestock farm). The highest

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abundance of intI1 genes was detected in grown chicken feces (3.79 – 4.26 intI1/cell), 1–3

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orders of magnitude higher than those of other environmental samples. It should be noted

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that these environmental niches were representative of typical environments affected by

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the anthropogenic activities and pollution, from slightly impacted (sediment) to the most

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seriously impacted (feces and wastewater from livestock farm). The variations of intI1

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abundance closely matched with the levels of anthropogenic impact and pollution levels in

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the various environments.

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The abundance of intI1 genes in drinking water samples varies over a wide range of

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4.07 × 10-3 – 1.64 × 10-1 intI1/cell. The lowest abundance of intI1 gene was found in

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drinking water collected from Macau, China, while the highest abundance was detected in

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Shandong province in China, even higher than that of pig feces. The abundance of intI1 in

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50% of the drinking water samples was higher than that of all the sediment and river water.

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Previously, most studies on intI1 in drinking water mainly focused on the shift during

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drinking water treatment, and reported that the traditional disinfection drinking water

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treatment process decreased the absolute level (copy of intI1 per liter) but increased the

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relative level of intI1 (copy of intI1 per 16S rRNA genes).25, 26 However, few studies have

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observed intI1 in drinking water at user ends. The observed widespread intI1 in tap water

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samples of this study showed that integrons can be residual after traditional drinking water

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treatment. The potential risks caused by integron-mediated ARGs transfer among

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microorganisms in drinking water should be paid more attention to.

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In STP, the abundance of intI1 genes follows Influent > Effluent > AS, that is, the

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traditional treatment process for wastewater could effectively remove class 1 integrons

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(~65.6%). Additionally, the abundance of intI1 in CF80 was observed to be 55-79 folds

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higher than that of CF20. On the contrary, the abundance of intI1 harbored in PF1 was

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greatly higher than that of PF8 (29-48 folds). These variations are consistent with the

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application of antibiotics on livestock farms; decreasing amounts of antibiotics are applied

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during pig growth, while chickens are fed with antibiotics the entire time.27, 28

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The correlation of class 1 integrase intI1 with ARGs. Considering that some of the

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integrons may carry ARGs and wondering whether intI1 could be a proxy for the

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assessment of ARG abundance in diverse environments, the abundance of ARGs was

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obtained by comparing against structured ARG database.15 Then the correlation of ARGs

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with intI1 was assessed by correlation coefficient (Pearson‟s r), summarized in Table S4.

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The r of ARGs with intI1 in all the various environmental niches is 0.852, indicating that

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the class 1 integrase intI1 could be the indicator for ARG abundance in environmental

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samples, especially for river water (r = 0.970, n=5), drinking water (r = 0.710, n=8), STP

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AS (r = 0.841, n=13), STP influent (r = 0.995, n=4) and feces & wastewater from

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livestock farm (r = 0.937, n=12), with Pearson‟s r > 0.500. Many previous studies

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demonstrated that intI1 genes had strong correlation with sulI gene by qPCR method.29, 30

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Chen and Zhang29 found positive correlation (r = 0.756, P < 0.05) between gene copy

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numbers of intI1 and sulI in rural domestic sewage and municipal wastewater by qPCR.

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Similarly, significant correlation (r = 0.74) was found between sulI and intI1 in anaerobic

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digestion sludge, and moderate correlation between intI1 and tetO (r = 0.55) was also

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revealed by using qPCR.30 The strong correlation of sulI with intI1 is not surprising

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because sulI is typically associated with class 1 integrons.30 However, further exploration

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of correlation between levels of intI1 and various ARGs was limited by traditional

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methods and existing primers. In the present study, the applied structured ARG database

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comprised of 25 ARG types (e.g. tetracycline resistance gene) and 619 ARG subtypes (e.g.

236

tetA, tetB, tetC, etc.), greatly providing the facility to search for numerous ARGs in

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environmental samples.15 Totally, 498 subtypes belonging to 20 ARG types were

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discovered in these environmental samples (Table S11). Among them, 49 ARG subtypes

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were detected in more than 50% samples and in positive correlation with intI1 (r > 0.500,

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Table S5). For instance, the subtypes of tetracycline_tetA (r = 0.9950), multidrug_mdtF (r

241

= 0.9938), beta-lactam_VEB-6 (r = 0.9926), beta-lactam_VEB-1 (r = 0.9905) and

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aminoglycoside 6-N-acetyltransferase (r = 0.9860) had high correlation with intI1

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abundance. Among them, beta-lactam_VEB and aminoglycoside 6-N-acetyltransferase

244

were frequently observed on assembled gene cassettes,31, 32 while tetA and mdtF have not

245

been discovered carried by integrons previously but frequently found co-occurrence with

246

integrons on plasmids.33 In recent years, ARG database has been used for study of ARG

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abundance and diversity in different environments,11 while this is the first time to apply it

248

combined with intI1 abundance to explore the correlation between intI1 and ARGs. The

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significantly high correlation revealed in the present study (Pearson‟s r = 0.852) suggested

250

that class 1 integrase intI1 could be an important indicator of ARGs, benefitting the quick

251

assessment of ARG abundance in environmental samples in future studies. This finding

252

supports the previous proposal to use intI1 gene as a proxy for anthropogenic pollution.6

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The ARG-carrying gene cassettes carried by class 1 integrons. To further study the

254

structure of gene cassettes carried by class 1 integrons and explore ARG-carrying gene

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cassettes (ACGCs), seven types of environmental samples with high level of class 1

256

integrase intI1, including activated sludge (AS), influent (Inf), effluent (Eff), chicken feces

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(20-day and 80-day, CF20 and CF80) and pig feces (1-month and 8-month, PF1 and PF8)

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were selected for PCR amplification of gene cassettes and then shotgun sequencing.

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Figure 3 shows the agarose gel electrophoresis of PCR for amplification of gene cassettes

260

carried by class 1 integrons for the selected samples. There observed various bands in

261

different lengths for these samples, showing that the samples of Inf, AS, CF20, CF80, PF1

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and PF8 had relatively high diversity of gene cassettes carried by integrons.

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Totally, 277 ACGCs were identified out of the 12,845 gene cassettes assembled from

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the shotgun sequencing results (Table S7). There were 69 (25%) assembled ACGCs

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longer than 1,000 bp, carrying 1–9 ORFs, and 40% of these ACGCs carried at least 2

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ARGs (Table S9). Among all the assembled ACGCs, seven types of ARGs were detected,

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including ARGs encoding resistance to aminoglycoside, beta-lactam, chloramphenicol,

268

erythromycin, sulfonamide, tetracycline, and trimethoprim. Aminoglycoside resistance

269

genes were most frequently observed on gene cassettes, carried by 57% assembled

270

ACGCs, followed by trimethoprim resistance genes (42%) and beta-lactam resistance

271

genes (33%). ACGCs that carried both aminoglycoside and trimethoprim resistance genes

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were most frequently detected with percentage of 37% among 111 ACGCs that carried ≥ 2

273

ARGs,

274

aminoglycoside (18%) (Figure S2). Table S10 presents all the ARG types and subtypes

275

carried by ACGCs. Totally, 53 ARG subtypes were observed on these ACGCs. Among

276

them, ARG subtype of aminoglycoside acetyltransferase was most frequently carried by

277

13% ACGCs (37 out of 277 in total), followed by trimethoprim dfrA (9%).

followed

by

aminoglycoside–beta-lactam

(19%)

and

aminoglycoside–

278

Among all the 277 detected ACGCs carried by class 1 integrons, totally 181 different

279

ACGCs that carried diverse ARG combinations were found. Table 1 shows the

280

arrangements of selected representative ACGCs extracted from different samples that

281

carried more than two ARGs. To verify the accuracy of this approach, the sequences of

282

these ACGCs from different samples were extracted and compared against NCBI database.

283

Some of the gene cassettes have been reported in previous studies, for instance, blaPSE-1–

284

aadA2, aadA2–catB2, etc.34 Meanwhile, several new gene cassettes were unprecedentedly

285

discovered in the present study, including aadA7–blaOXA-2, dfr16–blaOXA-35, etc.

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Among the 12 representative ACGCs, 5 gene cassettes (INF_GC_105, EFF_GC_9,

287

PF8_GC_215, CF20_GC_55 and CF80_GC_74) perfectly matched with the reference

288

sequences in NCBI database with the identity and hit length of 100%. The observed

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significantly high accuracy of ACGCs annotation verifies the metagenomic assembly

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based method used in this study.

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Traditional qPCR methods may not be able to quantify the multiple ARG-carrying

292

gene cassettes accurately, as amplification is limited by the conserved domain and the

293

target length. Here, the abundance (copy/Gb) of diverse gene cassettes carried by class 1

294

integrons could be decided by mapping metagenomic sequences to ACGCs, answering the

295

research gap that could not be resolved by qPCR. To further assess the abundance of

296

ACGCs in different niches, the abundance of ACGCs was obtained through mapping all

297

the 64 metagenomes to 277 ACGCs, respectively. Figure 4 presents the total 23

298

combinations of ARG types on ACGCs observed in these samples, for instance,

299

aminoglycoside (AMI)

300

aminoglycoside-trimethoprim (AMI-TRI) resistance genes, etc. The total abundance of

301

these ACGCs in the 8 environmental niches was in 0.3 – 822 (copy/Gb), with diversity

302

(different combinations of ARG types) of 7 – 23. The ACGCs that carried only

303

aminoglycoside resistance genes had the highest percentages with 37% in river water, 27%

304

in STP ADS, 26% in STP AS, 29% in STP Effluent and 39% in STP Influent. Differently,

305

ACGCs carrying sulfonamide, aminoglycoside-trimethoprim and tetracycline resistance

306

genes were in the highest abundance in sediment (36%), drinking water (73%) and feces

307

& wastewater from livestock farm (31%) niches, respectively. Though STP has been

308

considered as hot-spot reservoir of integrons and gene cassettes, and various gene

309

cassettes had been observed, for example, dfrA17-aadA5 (AMI-TRI resistance genes),

310

aadA2 (AMI resistance genes),6, 19 the information of their abundance was not available.

311

Besides, aminoglycoside resistance genes had been frequently discovered on gene

resistance

genes, trimethoprim (TRI) resistance genes,

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Environmental Science & Technology

312

cassettes carried by isolates, including aadA1, aadA2, aadA4, aadA5, aacA29b and

313

aac(6‟)-1b in previous studies.19, 35 This is the first time to illustrate the high frequency of

314

aminoglycoside resistance genes on gene cassettes of integrons, and explored its

315

combination with other ARG types (i.e. beta-lactam, chloramphenicol, erythromycin and

316

trimethoprim) on gene cassettes. Thus, the approach developed here not only helps

317

investigate new gene cassettes carried by class 1 integrons, but also provides an efficient

318

way to quantify gene cassettes carried by class 1 integrons.

319

In the present study, the HTS technique combined with constructed intI1 gene database

320

and genome assembly approach was developed for rapid quantification of intI1 and

321

identification of ARGs carried by gene cassettes of integrons in various environments. It

322

conquers the deficiencies of traditional PCR/qPCR and current sequences-based integron

323

identification method, largely feasible for intI1 assessment and ACGCs identification via

324

metagenomic short sequences, benefitting environmental risk assessments and studies on

325

horizontal transfer of ARGs among diverse microbial communities. In future, this pipeline

326

could be further optimized by supplementing novel intI1 sequences into database and

327

covering larger metagenomic data sets over diverse environments, for more

328

comprehensive assessment of ARGs as an anthropogenic pollution.

329 330

ASSOCIATED CONTENT

331

Supporting Information

332

The Supporting Information is available free of charge on the ACS Publications website at

333

DOI:

334 335

Sample collection procedures, identification of ARG-like sequences, basic information of 64 environmental samples, intI1 sequences in database, abundance ACS Paragon Plus Environment

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of intI1 in diverse environmental samples, the correlation of intI1 abundance and ARG abundance, correlation coefficient of ARG subtypes (r > 0.5000), DNA concentration of PCR products, the detected ACGCs in assembled metagenomic datasets, Mann-Whitney test P-value of intI1 abundance among 8 environmental ecosystems, the gene cassettes that carried different numbers of ARGs and ORFs, ARG types and subtypes carried by ACGCs, ARG profiles in 64 environmental samples, the structure of class 1 integron, the percentages of the ACGCs that carried multiple ARG types.

336 337 338 339 340 341 342 343 344 345

AUTHOR INFORMATION

346

Corresponding Author

347

*Telephone: +852-2857-8551. Fax: 0-852-2559-5337. E-mail: [email protected] and/or

348

[email protected]. (Tong Zhang)

349

Notes

350

The authors declare no competing financial interest

351 352

ACKNOWLEDGMENTS

353

This study was financially supported by the Hong Kong GRF (HKU17209914E). Dr.

354

Liping Ma thanks The University of Hong Kong for the postdoctoral fellowship. Mr.

355

An-Dong Li thanks The University of Hong Kong for postgraduate studentship. Ms.

356

Xiao-Le Yin thanks The University of Hong Kong for research assistant fellowship.

357 358

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497

Legends

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Figure 1. The pipeline of this study.

499

Figure 2. Abundance of intI1 genes in different environmental samples.

500

Figure 3. The agarose gel electrophoresis of PCR for amplification of gene cassettes

501

carried by class 1 integrons for Inf, Eff, AS, CF20, CF80, PF1 and PF8 samples.

502

Figure 4. The percentage and abundance of ARG-carrying gene cassettes.

503

Table 1. The representatives of ACGCs that carried more than two ARGs.

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Sample collection

DNA extraction

504 505

High-throughput sequencing

PCR amplification of gene cassettes

Construction of class 1 integrase intI1 database

High-throughput sequencing



intI1 sequences downloaded from NCBI database using keywords



De-duplication, identification, validation and removing partial sequences



Retrieving more intI1 sequences by comparing with NR database based on similarity homology



Identification, validation partial sequences



Totally 148 sequences of intI1 were obtained

and

Metagenomic assembly

removing

ARG identification using structured ARG database

Level of intI1 in environmental samples from 8 diverse niches

Gene annotation for ARGcarrying gene cassettes

Quick quantification of intI1 genes in various environmental samples

Identification of ARGs on gene cassettes carried by class 1 integron

Figure 1. The pipeline of this study.

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10

copy of intI1/cell

1

0.1

0.01

1E-3

In Fe flu c e en fro s t m & W liv e s as to tew ck fa ater rm

t

506

ST P

ST

P

ST

P

Ef flu

en

AS

S AD P ST

ki rin D

R

iv

er

ng

w

w

at

at er

t en di m Se

er

1E-4

507

Figure 2. Abundance of intI1 genes in different environmental samples.

508 509 510

Boxes denote the interquartile range (IQR) between the first and third quartiles (25th and 75th percentiles), and the line inside the boxes denotes the median. STP: sewage treatment plant; ADS: anaerobic digestion sludge; AS: activated sludge

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511

512 513

Figure 3. The agarose gel electrophoresis of PCR for amplification of gene cassettes

514

carried by class 1 integrons for Inf, Eff, AS, CF20, CF80, PF1 and PF8 samples.

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515 AMI

BET

* TET – 31%

Feces & Wastewater from livestock farm

CHL ERY

*

STP Influent

AMI – 39%

SUL TET TRI

* AMI – 29%

STP Effluent

AMI-AMI AMI-BET

AMI-CHL

* AMI – 26%

STP AS

AMI-ERY AMI-TRI

*

STP ADS

AMI – 27%

BET-CHL BET-ERY

*

Drinking water

BET-TRI

AMI-TRI – 73%

ERY-ERY ERY-TRI

*

River water

AMI – 37%

AMI-AMI-AMI

AMI-BET-AMI AMI-BET-BET

* SUL – 36%

Sediment

AMI-CHL-AMI AMI-TRI-AMI

0%

516 517

Abundance (copy/Gb) Diversity

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

AMI-TRI-BET

Sediment

River water

Drinking water

STP ADS

STP AS

0.3

0.7

3

7

9

32

149

Feces & Wastewater from livestock farm 822

7

11

20

21

19

19

22

23

STP Effluent

STP Influent

518 519

Figure 4. The percentage and abundance of ARG-carrying gene cassettes. The type of ACGCs with the highest percentage is marked with „*‟.

520

AMI: aminoglycoside; BET: beta-lactam; CHL: chloramphenicol; ERY: erythromycin; SUL: sulfonamide; TET: tetracycline; TRI: trimethoprim

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Table 1 The representatives of ACGCs that carried more than two ARGs. Contig ID

Number of ORFs

INF_GC_105

2

INF_GC_191

2

EFF_GC_9

2

EFF_GC_18

2

PF1_GC_167

4

PF1_GC_708

2

PF8_GC_215

2

PF8_GC_703

2

AS_GC_151

2

AS_GC_439

2

CF20_GC_55

2

CF80_GC_74

2

The ORFs carried by ACGCs beta-lactamase PSE-1 aminoglycoside adenyltransferase aadA2 aminoglycoside adenyltransferase aadA2 chloramphenicol acetyltransferase catB2 aminoglycoside adenyltransferase aadA chloramphenicol resistance protein cmlA7 aminoglycoside adenyltransferase aadA7 OXA-2 beta-lactamase rifampin ADP-ribosyltransferase OXA-129 beta-lactamase OXA-129 beta-lactamase aminoglycoside-3'-adenyltransferase trimethoprim dihydrofolate reductase dfr16 beta-lactamase OXA-35 aminoglycoside adenyltransferase aadA chloramphenicol resistance protein cmlA7 aminoglycoside resistance protein aadA trimethoprim-insensitive class A dihydrofolate reductase dfrA14 erythromycin esterase type 1 metallo-beta-lactamase VIM-1 aminoglycoside adenyltransferase aadA trimethoprim dihydrofolate reductase type 5 streptothricin acetyltransferase aadA trimethoprim dihydrofolate reductase dfrA14 trimethoprim dihydrofolate reductase dfrA5 streptothricin acetyltransferase aadA

Annotated best hit in NCBI database Identity Hit length

Reported in previous study (‘√’ or ‘×’)

References

100%



36

100%

98%



37

31

100%

100%



38

1527

13

88%

17%

×

---

1099

11

98%

100%

×

---

554

9

100%

83%

×

---

1331

23

100%

100%



38

622

47

100%

96%



39

1199

21

100%

99%

×

---

751

17

99%

88%

×

---

723

23

100%

100%



39

758

34

100%

100%



40

Length (bp)

Abundance (copy/Gb)

1106

13

100%

845

23

1626

522

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