The Prevalence of Integrons as the Carrier of Antibiotic Resistance

Subscriber access provided by UB + Fachbibliothek Chemie | (FU-Bibliothekssystem)

Article

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

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

Environmental Science & Technology is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 28

Environmental Science & Technology

1

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*

7

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,

11

Shenzhen, China

12

Corresponding author:

13

Prof. Tong Zhang

14

Email: [email protected] or [email protected]

ACS Paragon Plus Environment


Page 2 of 28

15

ABSTRACT

16

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.


The

present

study

applied

Page 3 of 28

33 34


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%


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



36

INTRODUCTION

37

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

38

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

39

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

40

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

41

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

42

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

43

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

44

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

45

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

46

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

47

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

48

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

49

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

50

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

51

resistance to sulfonamide and quaternary ammonium compounds, respectively.8

52

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

53

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

54

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

55

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

56

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


Page 4 of 28

Page 5 of 28


59

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

60

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

61

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

62

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

63

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

64

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

65

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

66

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

67

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

68

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

69

pollution assessment.

70

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

71

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

72

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

73

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

74

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

75

Materials and Methods.

76 77

MATERIALS AND METHODS

78

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

79

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

80

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

81

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



82

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

83

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

84

survey in different environments.11,

85

procedures is described in Supporting Information S1.

12

Detailed information of sample collection

86

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

87

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

88

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

89

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

90

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

91

metagenomic sequencing on Illumina Hiseq2000 platform by Beijing Genomics Institute

92

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

93

fluorescence images and call reads.13

94

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

95

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

96

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

97

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

98

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

99

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

100

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

101

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

102

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

103

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

104

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


Page 6 of 28

Page 7 of 28


105

sequences were compared against Pfam and CDsearch database for further validation

106

(Figure 1).

107

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

108

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

109

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

110

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

111

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

112

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

113

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

114

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

115

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

116

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

117

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

122

intI1 reference sequence; 𝐿i

123

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

124

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

125

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

126

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

127

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



128

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

129

N16S

130

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

131

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

132

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

133

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

134

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

135

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

copy number

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

136

Amplification of gene cassettes and sequencing. Seven environmental samples

137

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

138

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

139

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

140

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

141

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

142

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

143

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

144

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

145

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

146

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

148

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

149

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

150

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


Page 8 of 28

Page 9 of 28


151

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

152

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

153

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

154

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

155

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

156

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

157

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

158

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

159

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

160

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

161

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

162

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

163

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

164

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

165

mapping metagenomic sequencing reads to the gene cassette using CLC Genomics

166

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

167 168

RESULTS AND DISCUSSION

169

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

170

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

171

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

172

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

173

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



174

pneumonia, Pseudomonas aeruginosa, Salmonella enterica, Acinetobacter baumannii,

175

Enterobacter cloacae, etc. (Table S2).

176

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

177

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

178

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

179

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

180

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

181

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

182

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

183

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

184

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

185

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

186

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

187

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

188

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

189

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

190

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

191

that these environmental niches were representative of typical environments affected by

192

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

193

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

194

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

195

the various environments.

196

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


Page 10 of 28

Page 11 of 28


197

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

198

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

199

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

200

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

201

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

202

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

203

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

204

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

205

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

206

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

207

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

208

microorganisms in drinking water should be paid more attention to.

209

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

210

traditional treatment process for wastewater could effectively remove class 1 integrons

211

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

212

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

213

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

214

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

215

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

216

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

217

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

218

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

219

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



220

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

221

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

222

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

223

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

224

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

225

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

226

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

227

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

228

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

229

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

230

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

231

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

232

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

233

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

234

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

235

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

237

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

238

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

239

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

240

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

242

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


Page 12 of 28

Page 13 of 28


243

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

247

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

249

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

253

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

255

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

257

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

258

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

259

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

262

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

263

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

264

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

265

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



Page 14 of 28

266

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

267

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

272

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.

286

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


Page 15 of 28


289

significantly high accuracy of ACGCs annotation verifies the metagenomic assembly

290

based method used in this study.

291

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,



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

Page 16 of 28

Page 17 of 28


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

REFERENCES

359

1. Caballero-Flores, G. G.; Acosta-Navarrete, Y. M.; Ramirez-Diaz, M. I.; Silva-Sanchez,

360

J.; Cervantes, C., Chromate-resistance genes in plasmids from antibiotic-resistant

361

nosocomial enterobacterial isolates. Fems Microbiol Lett 2012, 327, (2), 148-154.

362

2. Mackie, R. I.; Koike, S.; Krapac, I.; Chee-Sanford, J.; Maxwell, S.; Aminov, R. I.,

363

Tetracycline residues and tetracycline resistance genes in groundwater impacted by swine

364

production facilities. Anim Biotechnol 2006, 17, (2), 157-176.



365

3. Okoh, A. I.; Igbinosa, E. O., Antibiotic susceptibility profiles of some Vibrio strains

366

isolated from wastewater final effluents in a rural community of the Eastern Cape

367

Province of South Africa. Bmc Microbiol 2010, 10, (1), 143.

368

4. Zhang, T.; Zhang, X. X.; Ye, L., Plasmid Metagenome Reveals High Levels of

369

Antibiotic Resistance Genes and Mobile Genetic Elements in Activated Sludge. Plos One

370

2011, 6, (10), e26041.

371

5. Cury, J.; Jove, T.; Touchon, M.; Neron, B.; Rocha, E. P. C., Identification and analysis

372

of integrons and cassette arrays in bacterial genomes. Nucleic Acids Res 2016, 44, (10),

373

4539-4550.

374

6. Gillings, M. R.; Gaze, W. H.; Pruden, A.; Smalla, K.; Tiedje, J. M.; Zhu, Y. G., Using

375

the class 1 integron-integrase gene as a proxy for anthropogenic pollution. ISME J 2015, 9,

376

(6), 1269-1279.

377

7. Ling, A. L.; Pace, N. R.; Hernandez, M. T.; LaPara, T. M., Tetracycline Resistance

378

and Class 1 Integron Genes Associated with Indoor and Outdoor Aerosols. Environ Sci

379

Technol 2013, 47, (9), 4046-4052.

380

8. Partridge, S. R.; Tsafnat, G.; Coiera, E.; Iredell, J. R., Gene cassettes and cassette

381

arrays in mobile resistance integrons. Fems Microbiol Rev 2009, 33, (4), 757-784.

382

9. Wei, Q. H.; Jiang, X. F.; Li, M.; Li, G.; Hu, Q. F.; Lu, H. X.; Chen, G. Q.; Zhou, Y. L.;

383

Lu, Y., Diversity of Gene Cassette Promoter Variants of Class 1 Integrons in

384

Uropathogenic Escherichia coli. Curr Microbiol 2013, 67, (5), 543-549.

385

10. Moura, A.; Soares, M.; Pereira, C.; Leitao, N.; Henriques, I.; Correia, A.,

386

INTEGRALL: a database and search engine for integrons, integrases and gene cassettes.

387

Bioinformatics 2009, 25, (8), 1096-1098.

388

11. Li, B.; Yang, Y.; Ma, L. P.; Ju, F.; Guo, F.; Tiedje, J. M.; Zhang, T., Metagenomic and

389

network analysis reveal wide distribution and co-occurrence of environmental antibiotic

390

resistance genes. ISME J 2015, 9, (11), 2490-2502.

391

12. Ma, L. P.; Xia, Y.; Li, B.; Yang, Y.; Li, L. G.; Tiedje, J. M.; Zhang, T., Metagenomic

392

Assembly Reveals Hosts of Antibiotic Resistance Genes and the Shared Resistome in Pig,

393

Chicken, and Human Feces. Environ Sci Technol 2016, 50, (1), 420-427.

394

13. Qin, J. J.; Li, R. Q.; Raes, J.; Arumugam, M.; Burgdorf, K. S.; Manichanh, C.; Nielsen,

395

T.; Pons, N.; Levenez, F.; Yamada, T.; Mende, D. R.; Li, J. H.; Xu, J. M.; Li, S. C.; Li, D. ACS Paragon Plus Environment

Page 18 of 28

Page 19 of 28


396

F.; Cao, J. J.; Wang, B.; Liang, H. Q.; Zheng, H. S.; Xie, Y. L.; Tap, J.; Lepage, P.;

397

Bertalan, M.; Batto, J. M.; Hansen, T.; Le Paslier, D.; Linneberg, A.; Nielsen, H. B.;

398

Pelletier, E.; Renault, P.; Sicheritz-Ponten, T.; Turner, K.; Zhu, H. M.; Yu, C.; Li, S. T.;

399

Jian, M.; Zhou, Y.; Li, Y. R.; Zhang, X. Q.; Li, S. G.; Qin, N.; Yang, H. M.; Wang, J.;

400

Brunak, S.; Dore, J.; Guarner, F.; Kristiansen, K.; Pedersen, O.; Parkhill, J.; Weissenbach,

401

J.; Bork, P.; Ehrlich, S. D.; Wang, J.; Consortium, M., A human gut microbial gene

402

catalogue established by metagenomic sequencing. Nature 2010, 464, (7285), 59-65.

403

14. Kristiansson, E.; Fick, J.; Janzon, A.; Grabic, R.; Rutgersson, C.; Weijdegard, B.;

404

Soderstrom, H.; Larsson, D. G. J., Pyrosequencing of Antibiotic-Contaminated River

405

Sediments Reveals High Levels of Resistance and Gene Transfer Elements. Plos One 2011,

406

6, (2), e17038.

407

15. Yang, Y.; Li, B.; Ju, F.; Zhang, T., Exploring Variation of Antibiotic Resistance Genes

408

in Activated Sludge over a Four-Year Period through a Metagenomic Approach. Environ

409

Sci Technol 2013, 47, (18), 10197-10205.

410

16. DeSantis, T. Z.; Hugenholtz, P.; Larsen, N.; Rojas, M.; Brodie, E. L.; Keller, K.;

411

Huber, T.; Dalevi, D.; Hu, P.; Andersen, G. L., Greengenes, a chimera-checked 16S rRNA

412

gene database and workbench compatible with ARB. Appl Environ Microb 2006, 72, (7),

413

5069-5072.

414

17. Albertsen, M.; Hugenholtz, P.; Skarshewski, A.; Nielsen, K. L.; Tyson, G. W.; Nielsen,

415

P. H., Genome sequences of rare, uncultured bacteria obtained by differential coverage

416

binning of multiple metagenomes. Nat Biotechnol 2013, 31, (6), 533-538.

417

18. Angly, F. E.; Dennis, P. G.; Skarshewski, A.; Vanwonterghem, I.; Hugenholtz, P.;

418

Tyson, G. W., CopyRighter: a rapid tool for improving the accuracy of microbial

419

community profiles through lineage-specific gene copy number correction. Microbiome

420

2014, 2, (1), 11.

421

19. Ma, L. P.; Zhang, X. X.; Zhao, F. Z.; Wu, B.; Cheng, S. P.; Yang, L. Y., Sewage

422

treatment plant serves as a hot-spot reservoir of integrons and gene cassettes. J Environ

423

Biol 2013, 34, (2), 391-399.

424

20. Levesque, C.; Piche, L.; Larose, C.; Roy, P. H., Pcr Mapping of Integrons Reveals

425

Several Novel Combinations of Resistance Genes. Antimicrob Agents Ch 1995, 39, (1),

426

185-191. ACS Paragon Plus Environment


427

21. Peng, Y.; Leung, H. C. M.; Yiu, S. M.; Chin, F. Y. L., IDBA-UD: a de novo assembler

428

for single-cell and metagenomic sequencing data with highly uneven depth.

429

Bioinformatics 2012, 28, (11), 1420-1428.

430

22. Hyatt, D.; Chen, G. L.; LoCascio, P. F.; Land, M. L.; Larimer, F. W.; Hauser, L. J.,

431

Prodigal: prokaryotic gene recognition and translation initiation site identification. Bmc

432

Bioinformatics 2010, 11, (1), 119.

433

23. Hu, Y. F.; Yang, X.; Qin, J. J.; Lu, N.; Cheng, G.; Wu, N.; Pan, Y. L.; Li, J.; Zhu, L. Y.;

434

Wang, X.; Meng, Z. Q.; Zhao, F. Q.; Liu, D.; Ma, J. C.; Qin, N.; Xiang, C. S.; Xiao, Y. H.;

435

Li, L. J.; Yang, H. M.; Wang, J.; Yang, R. F.; Gao, G. F.; Wang, J.; Zhu, B.,

436

Metagenome-wide analysis of antibiotic resistance genes in a large cohort of human gut

437

microbiota. Nat Commun 2013, 4.

438

24. Sentchilo, V.; Mayer, A. P.; Guy, L.; Miyazaki, R.; Tringe, S. G.; Barry, K.; Malfatti,

439

S.; Goessmann, A.; Robinson-Rechavi, M.; Van der Meer, J. R., Community-wide plasmid

440

gene mobilization and selection. ISME J 2013, 7, (6), 1173-1186.

441

25. Chao, Y. Q.; Ma, L. P.; Yang, Y.; Ju, F.; Zhang, X. X.; Wu, W. M.; Zhang, T.,

442

Metagenomic analysis reveals significant changes of microbial compositions and

443

protective functions during drinking water treatment. Sci Rep-Uk 2013, 3.

444

26. Shi, P.; Jia, S. Y.; Zhang, X. X.; Zhang, T.; Cheng, S. P.; Li, A. M., Metagenomic

445

insights into chlorination effects on microbial antibiotic resistance in drinking water.

446

Water Res 2013, 47, (1), 111-120.

447

27. Wegener, H. C., Antibiotics in animal feed and their role in resistance development.

448

Curr Opin Microbiol 2003, 6, (5), 439-445.

449

28. Jensen, H. H.; Hayes, D. J., Impact of Denmark's ban on antimicrobials for growth

450

promotion. Curr Opin Microbiol 2014, 19, 30-36.

451

29. Chen, H.; Zhang, M. M., Occurrence and removal of antibiotic resistance genes in

452

municipal wastewater and rural domestic sewage treatment systems in eastern China.

453

Environ Int 2013, 55, 9-14.

454

30. Miller, J. H.; Novak, J. T.; Knocke, W. R.; Young, K.; Hong, Y. J.; Vikesland, P. J.;

455

Hull, M. S.; Pruden, A., Effect of Silver Nanoparticles and Antibiotics on Antibiotic

456

Resistance Genes in Anaerobic Digestion. Water Environ Res 2013, 85, (5), 411-421.

457

31. Girlich, D.; Poirel, L.; Leelaporn, A.; Karim, A.; Tribuddharat, C.; Fennewald, M.; ACS Paragon Plus Environment

Page 20 of 28

Page 21 of 28


458

Nordmann, P., Molecular epidemiology of the integron-located VEB-1 extended-spectrum

459

beta-lactamase in nosocomial enterobacterial isolates in Bangkok, Thailand. J Clin

460

Microbiol 2001, 39, (1), 175-182.

461

32. Dubois, W.; Poirel, L.; Marie, C.; Arpin, C.; Nordmann, P.; Quentin, C., Molecular

462

characterization of a novel class 1 integron containing blaGES-1 and a fused product of

463

aac(3)-ib/Aac(6 ')-Ib ' gene cassettes in Pseudomonas aeruginosa. Antimicrob Agents Ch

464

2002, 46, (3), 638-645.

465

33. Vinue, L.; Saenz, Y.; Somalo, S.; Escudero, E.; Moreno, M. A.; Ruiz-Larrea, F.; Torres,

466

C., Prevalence and diversity of integrons and associated resistance genes in faecal

467

Escherichia coli isolates of healthy humans in Spain. J Antimicrob Chemoth 2008, 62, (5),

468

934-937.

469

34. Ahmed, A. M.; Maruyama, A.; Khalifa, H. O.; Shimamoto, T.; Shimamoto, T.,

470

Seafood as a Reservoir of Gram-negative Bacteria Carrying Integrons and Antimicrobial

471

Resistance Genes in Japan. Biomed Environ Sci 2015, 28, (12), 924-927.

472

35. Meng, X. F.; Zhang, Z. F.; Li, K. T.; Wang, Y.; Xia, X. D.; Wang, X.; Xi, M. L.; H., M.

473

J.; Cui, S. H.; Yang, B. W., Antibiotic Susceptibility and Molecular Screening of Class I

474

Integron in Salmonella Isolates Recovered from Retail Raw Chicken Carcasses in China.

475

Microbial Drug Resistance 2016, 1076-6294, 1-6.

476

36. Casin, I.; Breuil, J.; Brisabois, A.; Moury, F.; Grimont, F.; Collatz, E.,

477

Multidrug-resistant human and animal Salmonella typhimurium isolates in France belong

478

predominantly to a DT104 clone with the chromosome- and integron-encoded

479

beta-lactamase PSE-1. J Infect Dis 1999, 179, (5), 1173-1182.

480

37. Chen, Y. T.; Lauderdale, T. L.; Liao, T. L.; Shiau, Y. R.; Shu, H. Y.; Wu, K. M.; Yan, J.

481

J.; Su, I. J.; Tsai, S. F., Sequencing and comparative genomic analysis of pK29, a

482

269-kilobase conjugative plasmid encoding CMY-8 and CTX-M-3 beta-lactamases in

483

Klebsiella pneumoniae. Antimicrob Agents Ch 2007, 51, (8), 3004-3007.

484

38. Verdet, C.; Benzerara, Y.; Gautier, V.; Adam, O.; Ould-Hocine, Z.; Arlet, G.,

485

Emergence of DHA-1-producing Klebsiella spp. in the Parisian region: Genetic

486

organization of the ampC and ampR genes originating from Morganella morganii.

487

Antimicrob Agents Ch 2006, 50, (2), 607-617.

488

39. Guerra, B.; Junker, E.; Miko, A.; Helmuth, R.; Mendoza, M. C., Characterization and ACS Paragon Plus Environment


489

localization of drug resistance determinants in multidrug-resistant, integron-carrying

490

Salmonella enterica serotype typhimurium strains. Microb Drug Resist 2004, 10, (2),

491

83-91.

492

40. Abbassi, M. S.; Torres, C.; Achour, W.; Vinue, L.; Saenz, Y.; Costa, D.; Bouchami, O.;

493

Ben Hassen, A., Genetic characterisation of CTX-M-15-producing Klebsiella pneumoniae

494

and Escherichia coli strains isolated from stem cell transplant patients in Tunisia. Int J

495

Antimicrob Ag 2008, 32, (4), 308-314.

496


Page 22 of 28

Page 23 of 28


497

Legends

498

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.



Page 24 of 28

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.


Page 25 of 28


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



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.


Page 26 of 28

Page 27 of 28


515 AMI

BET

* TET – 31%


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



521

Page 28 of 28

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


The Prevalence of Integrons as the Carrier of Antibiotic Resistance

Recommend Documents