Metagenomic Assembly Reveals Hosts of Antibiotic Resistance

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Metagenomic Assembly Reveals Hosts of Antibiotic Resistance Genes and the Shared Resistome in Pig, Chicken and Human Feces Liping Ma, Yu Xia, Bing Li, Ying Yang, Li-Guan Li, James M. Tiedje, and Tong Zhang Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.5b03522 • Publication Date (Web): 09 Dec 2015 Downloaded from http://pubs.acs.org on December 19, 2015

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Metagenomic Assembly Reveals Hosts of Antibiotic Resistance

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Genes and the Shared Resistome in Pig, Chicken and Human Feces

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Authors: Liping Ma1, Yu Xia1, Bing Li1, Ying Yang1, Li-Guan Li1, James M Tiedje2* and Tong

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Zhang1*

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Author affiliation:

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1

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

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2

Department of Plant, Soil, and Microbial Sciences, Michigan State University, United States

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Corresponding author:

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1. Tong Zhang (Associate Professor)

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Environmental Biotechnology Lab, The University of Hong Kong, Pokfulam Road, Hong Kong

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Email: [email protected]

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Tel: +852-28578551

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2. James M Tiedje (Professor)

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Department of Plant, Soil, and Microbial Sciences, Michigan State University, United States

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Email: [email protected]

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Tel: +517-355-9021

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ABSTRACT

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The risk associated with antibiotic resistance disseminating from animal and human feces is an

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urgent public issue. In the present study, we sought to establish a pipeline for annotating

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antibiotic resistance genes (ARGs) based on metagenomic assembly to investigate ARGs and

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their co-occurrence with associated genetic elements. Genetic elements found on the assembled

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genomic fragments include mobile genetic elements (MGEs) and metal resistance genes (MRGs).

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We then explored the hosts of these resistance genes and the shared resistome of pig, chicken and

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human fecal samples. High levels of tetracycline, multidrug, erythromycin and aminoglycoside

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resistance genes were discovered in these fecal samples. In particular, significantly high level of

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ARGs (7762 ×/Gb) was detected in adult chicken feces, indicating higher ARG contamination

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level than other fecal samples. Many ARGs arrangements (e.g., macA-macB and tetA-tetR) were

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discovered shared by chicken, pig and human feces. In addition, MGEs such as the aadA5-

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dfrA17-carrying class 1 integron were identified on an assembled scaffold of chicken feces, and

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are carried by human pathogens. Differential coverage binning analysis revealed significant ARG

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enrichment in adult chicken feces. A draft genome, annotated as multi-drug resistant Escherichia

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coli, was retrieved from chicken feces metagenomes and was determined to carry diverse ARGs

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(multidrug, acriflavine and macrolide). The present study demonstrates the determination of

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ARG hosts and the shared resistome from metagenomic data sets and successfully establishes the

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relationship between ARGs, hosts, and environments. This ARG annotation pipeline based on

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metagenomic assembly will help to bridge the knowledge gaps regarding ARG-associated genes

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and ARG hosts with metagenomic data sets. Moreover, this pipeline will facilitate the evaluation

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of environmental risks in the genetic context of ARGs.

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INTRODUCTION

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The emergence of antibiotic-resistant bacteria (ARB) and antibiotic resistance genes (ARGs)

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has become an urgent public health issue worldwide.1 ARGs have been detected in various

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environments, including wastewater treatment plants,2 animal feces and soil,3-6 river water,7

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drinking water,8 and glaciers.9 Although antibiotic resistance is a natural phenomenon that

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predates the modern selective pressure of clinical antibiotic use,10 the aggravation of antibiotic

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resistance has been reported in diverse environments. The emergence of ARGs could

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significantly increase the spread of antibiotic resistance, especially via mobile genetic elements

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through conjugation, transduction and transformation.1 High ARG levels were frequently found

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in livestock and human feces, likely resulting from the wide application of antibiotics and the

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bacterial acquisition of antibiotic resistance mechanisms.11 Thus, in the present study, chicken,

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pig and human feces and municipal wastewater samples reported to be ARG hot spots5, 6, 12 were

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investigated.

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Recently, high-throughput sequencing-based metagenomic analysis has been used to explore

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the diversity and abundance of ARGs in various samples.6, 13, 14 In a previous study by our group,

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the non-redundant structured Clean Antibiotic Resistance Genes Database (ARDB – 25 ARG

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types and 620 ARG subtypes) was constructed and used to annotate ARG-like genes. The

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database was also used to investigate the abundance of ARGs in activated sludge sampled over a

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four-year period15 and in 50 samples from various environments.6 The same approach has also

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been used to study ARG profiles in marine sediment16 and sludge samples from different

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wastewater reactors17. These studies demonstrated that a metagenomic approach and a structured

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ARG database are powerful novel tools for the high-throughput identification and quantification

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of ARGs in different environments. However, key information such as ARG hosts, ARG co-

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occurrence, ARG co-occurrence with metal resistance genes (MRGs), and the antibiotic

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resistome shared by different environments remains unknown. Network analysis of ARGs and

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bacterial populations might provide indirect information about ARG hosts via co-occurrence

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patterns.6 Recently, several studies have begun investigating the open reading frames (ORFs) of

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ARGs on the assembled contigs of genomes from cultured strains.18, 19 These studies found that

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soil bacteria and human pathogens shared some antibiotic resistomes (e.g., tetR-tetA(G)),18

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indicating a possible exchange of ARGs between environmental bacteria and clinical pathogens.

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Furthermore, these studies demonstrated the importance of knowledge regarding the hosts of

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specific ARGs and the resistomes shared by different environments. However, the annotation of

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metagenomic raw reads cannot provide a comprehensive survey of ARG arrangements on

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bacterial genomes, ARG hosts and the resistomes shared by different environmental samples.

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In the present study, we applied an approach based on assembled metagenomic sequences to

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identify ARG-carrying contigs (ACCs) in feces (human, pig, and chicken) and municipal

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wastewater samples shown to possess high levels of diverse ARGs. We then explored the

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correlations of ARGs with hosts, integrons, MRGs and environmental niches following the

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research pipeline shown in Figure 1.

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MATERIALS AND METHODS

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Sample Collection and Sequencing. ARG profiles of different environmental samples were

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compared based on unassembled raw reads from our previous publication.6 This comparison

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illustrated the high abundance of ARGs in wastewater influents and fecal samples from chickens,

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pigs and humans. In addition, the study suggested these sources as the main sources of ARG

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contamination in the environment. To detect ARG-carrying contigs and to explore the shared

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resistome, animal (pig and chicken) feces, human gut microbiomes and wastewater treatment

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plant influents were selected from our previous study6 and reanalyzed. Basic information of the

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16 selected samples is summarized in Table S1. Chicken (20-day and 80-day) and pig (1-month

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and 8-month) fecal samples were collected from livestock farms in southern China in October

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2012. Chicken (20-day and 80-day) samples were abbreviated as CF20d and CF80d, and pig (1-

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month and 8-month) samples were abbreviated as PF1m and PF8m, respectively. The sampling

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procedure, fixation method, DNA extraction procedure, and Illumina sequencing method have

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been described previously.6 After discarding low-quality reads, sequencing depths of 2.5~3 Gb

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were obtained for each sample (Table S1). Metagenomic data for human feces (Zhuhai, southern

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China) were downloaded from the NCBI SRA (National Center for Biotechnology Information

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Sequence Read Archive) database (accession number SRX095751, SRX095764, SRX095769

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and SRX095775) and had a total size of 8.6 Gb.

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Metagenome Assembly and ORF Prediction. After quality control, reads were assembled

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using CLC Genomics Workbench (Version 6.0.2; CLC Bio, Aarhus, Denmark) with a Kmer of

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63.20 Assembled contigs longer than 500 bp were used for downstream analysis. In total, 95,042,

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108,016, 22,338, 42,665, 55,982 and 158,007 contigs (≥ 500 bp) were obtained from the CF20d,

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CF80d, PF1m, PF8m, Influent (wastewater influent from Hong Kong Shatin wastewater

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treatment plant) and HumanGut datasets, respectively (Table S2). ORFs within contigs were

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predicted using MetaGeneMark (Georgia Institute of Technology, Atlanta, Georgia, USA).21 The

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average coverage of each contig/ORF was quantified by mapping metagenomic reads to the

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contig/ORF using CLC Genomics Workbench with a minimum similarity of 95% over 95% of

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the read length.

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Identification of ARG-like ORFs. The nucleotide sequences of the ORFs were searched for

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ARGs against the non-redundant structured Clean ARDB developed by our group15 using

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BLASTX with an E-value ≤ 10−10.22 An ORF was designated an ARG-like sequence if its best

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BLASTX alignment to ARG sequences was at least 80% similar with a query coverage of at least

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70%. This identification approach was determined to be highly precise (precision of 99.1%) by

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Hu et al.22. Identified ARG-like ORFs were automatically sorted into 25 ‘ARG types’ (e.g.,

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tetracycline resistance genes) and 620 ‘ARG subtypes’ (e.g., tetA, tetB, tetC) using a package of

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customized scripts.

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Annotation of ORFs on ACCs. The presumed protein ORF sequences were compared with

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the local non-redundant NCBI NR database (downloaded on 18 July, 2013) using BLASTP with

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an E-value ≤ 10−2.23 Using a customized script, these ORFs were then annotated by a

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customized script according to the corresponding descriptions retrieved for each ORF from the

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NR database. Finally, all ORF annotations and ORF arrangements on ACCs were summarized

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and checked manually to analyze ARG distribution and co-occurrence.

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Taxonomy Annotation of ACCs. The predicted protein sequences of ORFs on ACCs were

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compared with the local NCBI NR database using BLASTP with an E-value ≤ 10−5.20 Search

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results were parsed and annotated with MEGAN (MEtaGenome ANalyzer, Version 5).24 If more

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than

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kingdom/phylum/class/order/family/genus, the contig was assigned to that taxon25 using a

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customized R program for voting. Sequences of interesting ACCs (e.g., ACCs carrying a

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combination of ARGs and associated genetic elements or ACCs shared by different

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environmental niches) were submitted to NCBI using BLASTN to obtain sequence taxonomy

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annotations at the species level.

50%

of

the

ORFs

on

a

contig

were

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Sequence Composition-Independent Binning and Retrieval of the ARG-Carrying

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Genome Bin. Sequence composition-independent binning26 was conducted to compare the

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distributions of ARGs in different samples. Binning was facilitated using the metagenomes

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generated from CF20d and CF80d (chicken) and PF1m and PF8m (pig) fecal samples. After

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mapping the reads from each of the two metagenomes to the assembled contigs and plotting the

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two coverage values against each other in R, distinct contig groupings were identified.26 Contigs

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were colored according to their taxonomic affiliation and guanine-cytosine (GC) content,

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respectively. Contigs carrying ARGs were also marked with red triangle icons. The ARG-

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carrying draft genome was retrieved according to the approach proposed by Albertsen et al.26

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The completeness and potential contamination of the genome bin was evaluated using hidden

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Markov models (HMMs) of 107 essential single-copy genes conserved in 95% of bacteria.27

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Then, the ARGs on the retrieved draft genome were identified. Functional analysis of the

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genome bin was performed using KEGG (Kyoto Encyclopedia of Genes and Genomes)

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Mapper.28

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Accession Number. The assembled metagenomic genomes were deposited in MG-RAST (Metagenomic Analysis Server) under the accession numbers 4572808.3~4572813.3.

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RESULTS AND DISCUSSION

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Assembly and ACCs. After comparison against the non-redundant Structured Clean ARDB

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database using BLASTX, 52, 170, 114, 33, 108 and 156 ORFs were annotated as ARG-like

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sequences in the CF20d, CF80d, PF1m, PF8m, Influent, and HumanGut samples (Table S2),

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respectively. These ARG-like ORFs in the CF20d, CF80d, PF1m, PF8m, Influent, and

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HumanGut samples were carried by 49, 160, 107, 31, 99 and 113 ACCs (Table S3), respectively.

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In some cases, multiple ARGs were found on the same contig. For example, in the HumanGut

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sample, 49 ACCs (43% of ACCs) carrying more than 2 ARGs with an average length of 5.6 kbp

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were found. Many of these ACCs also carried genes associated with ARGs, such as MRGs,

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transposases, integrases and virulence factors (Table S3).

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ARG-like ORFs were assigned to different ARG types and ARG subtypes according to the

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structured ARG database. The abundance of a particular ARG type, indicated by coverage (in

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units of ‘times per Giga base’, ×/Gb), was calculated by summing the coverages of ARG-like

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ORFs belonging to that ARG type. Figure 2 shows the abundance of ARG-like ORFs in feces

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and influent samples for the 22 total ARG types detected. The highest ARG abundance was

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found in the CF80d sample, with a total abundance of 7762 ×/Gb. Of the ARGs detected,

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tetracycline resistance genes were the most abundant in the PF1m, PF8m, Influent and

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HumanGut samples, with abundances of 1090, 526, 115 and 525 ×/Gb, respectively. For the

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CF20d and CF80d samples, the tetracycline resistance genes were the second highest ARG type,

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with abundances of 1362 ×/Gb and 827 ×/Gb, respectively. Multidrug resistance genes were

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extremely abundant in the CF80d sample, with an abundance of 2986 ×/Gb.

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Tetracycline resistance genes, the most widespread and dominant ARGs, were detected with

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high diversity in feces and influent samples. In all, 4, 16, 8, 5, 11 and 12 subtypes of tetracycline

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resistance genes were found in the CF20d, CF80d, PF1m, PF8m, Influent and HumanGut

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samples, respectively. The CF80d sample was the most diverse, containing tetA, tetA(33),

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tetA(39), tetB, tetC, tetG, tetH, tetL, tetM, tetO, tetX, tetY, and other genes (Table S4). To further

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explore the diversity of the tetracycline resistance ARG subtype, a phylogenetic tree was

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constructed consisting of the tetA-like ORFs in these samples and the reference tetA protein

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sequences in the ARDB database (Figure S2). This tree revealed that the tetA genes in chicken

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feces, pig feces, influent and human feces were highly homologous, suggesting that homologous

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tetA genes can be captured by diverse hosts and shared between different fecal and influent

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samples.

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Host Identification of ACCs. A comprehensive taxonomic survey of ACCs was conducted to

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enhance our knowledge of 1) the hosts of these specific ARGs and 2) the types of ARGs

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commonly carried by a particular host. ACCs were first classified according to the ARG types

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each ACC carried. Then, contig taxonomies were determined by using BLASTP with the NR

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database and by annotation with MEGAN and R voting. Table S5 summarizes the taxonomy

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annotation results at the genus level for ACCs and the ARG types carried by each ACC,

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successfully establishing connections between hosts and ARGs via contigs. Only 13~28% of the

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contigs in the CF80d, PF1m, Influent and HumanGut samples could be annotated at the genus

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level. The other contigs could not be classified, indicating our limited knowledge of the ARG

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hosts in these environmental samples. This limitation is common in the taxonomy identification

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of contigs generated from metagenomic data.6 Although >30% and >50% of contigs were

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annotated at the family and phylum levels, the low annotation percentage at the genus level

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hinders the identification of ARG hosts. Despite this low annotation percentage, the Escherichia

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genus exhibited high correlation with multidrug resistance genes, which were carried by 40%,

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33%, 56% and 75% of the Escherichia-related ACCs in the CF80d, PF1m, Influent and

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HumanGut samples, respectively. These Escherichia-related multidrug-ACCs were highly

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abundant in several samples, especially CF80d (Table S6).

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Based on the widely accepted assumption that different contigs with similar coverages may

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be derived from the same species,26 the coverage values of Escherichia-related multidrug-ACCs

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were grouped into a single group using an RSD (relative standard deviation) cutoff of 23,602-, >15,477- and >15,430-fold, respectively. The total abundance of ARGs in chicken

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feces increased by 137%, while the total abundance of ARGs in pig feces decreased by 49%.

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One ARG-carrying genome bin (consisting of 390 contigs) was successfully retrieved from

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metagenome data sets using the differential coverage approach (Figure S4). The extracted

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genome bin was annotated as E. coli, with a completeness of 57% and no redundancy (Table

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S13). In addition, the abundance of this E. coli bin was increased 77-fold in CF80d compared to

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CF20d. In all, 9 out of 390 contigs carried ARGs, including 1 acriflavine ARG, 1 macrolide ARG,

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7 multidrug ARGs and 1 other ARG (Table S14). Functional analysis of this E. coli strain using

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MEGAN KEGG revealed that the strain might cause human disease and could possess antibiotic

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and metal resistance. Therefore, this E. coli strain is likely a high-risk human pathogen.

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In previous studies, the differential coverage binning of multiple metagenomes was mainly

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used to retrieve draft genomes.26 Here, for the first time, sequence composition-independent

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binning was used to explore ARG distributions in fecal samples using multiple metagenomes.

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Binning analysis showed a significant enrichment of ARGs in adult chicken feces and an

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apparent decrease of ARGs in adult pig feces. These variations are consistent with application of

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antibiotics on livestock farms: decreasing amounts of antibiotics are applied during pig growth,

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while chickens are fed antibiotics the entire time.45, 46

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Our study established a metagenome assembly-based method for identifying ARG-carrying

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contig/genomes in environmental samples. This method successfully answered several questions

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that could not be resolved using traditional PCR/qPCR approaches or short read-based

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metagenomic analysis. We used this approach not only to identify ARGs on contigs but also to

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quantify the abundance of ARGs in samples based on coverage, to identify the hosts of ACCs, to

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explore the shared antibiotic resistome among bacteria, and to determine the correlations of

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ARGs with associated genetic elements. In the future, this approach could facilitate antibiotic

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resistance analysis using metagenomic data sets to explore the ARG distributions in various

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environmental samples and could expand our horizons on the presence, co-occurrence and hosts

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of ARGs. Finally, this approach could greatly benefit environmental risk assessments of ARGs.

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ASSOCIATED CONTENT

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Supporting Information

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Additional information is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

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Corresponding Author

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*Telephone: +852-2857-8551. Fax: 0-852-2559-5337. E-mail: [email protected]. (Tong Zhang)

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*Telephone: +517-355-9021. Fax: 517-353-2917. E-mail: [email protected]. (James M Tiedje)

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Notes

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The authors declare no competing financial interest

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ACKNOWLEDGMENTS

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

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thanks The University of Hong Kong for the postgraduate studentship. Dr. Yu Xia and Bing Li

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thank The University of Hong Kong for their postdoctoral fellowships. Dr. Ying Yang thanks The

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University of Hong Kong for the research assistant fellowship. The work at MSU was supported

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by the Center for Health Impacts of Agriculture (CHIA).

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Figure Legends Figure 1. The analysis pipeline of this study. Figure 2. The abundance (coverage of ARG-like ORFs, ×/Gb) of ARG-like ORFs in samples. MLS: Macrolide-lincosamide-streptogramin.

Abundance (coverage,×/Gb) = 

#N mapped reads $

× 100/L S

!"

Where Nmapped reads is the number of the reads mapped to ARG-like ORFs, LARG-like ORF is the sequence length of the corresponding target ARG-like ORF sequence, n is the number of ARG-like ORFs belonging to the same ARG type, 100 is the sequence length of the Illumina reads, and S is the size of the dataset (Gb). Figure 3. The arrangements of shared multiple-ARG-carrying contigs and their putative hosts (best hits on NCBI) in different samples. Figure 4. A complete class 1 integron (carrying aadA5 and dfrA17) detected on contig CF20d_5145 (4765 bp) and annotated as a fragment of Escherichia coli O25b:H4ST131 str. EC958 (HG941719.1) and Salmonella enteric subsp. enterica serovar Newport str. USMARC-S3124.1 (CP006631.1) by NCBI BLASTN (best hit with a query coverage of 86%, an e-value of 0.0, and an identity of 100%). Figure 5. The distribution of ARG-carrying contigs in chicken metagenome datasets via differential coverage-based binning. All nodes represent scaffolds, scaled by the square root of their length, and colored by community taxonomy.

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Figure 1. The analysis pipeline of this study.

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Figure 2. The abundance (coverage of ARG-like ORFs, ×/Gb) of ARG-like ORFs in samples. MLS: Macrolide-lincosamide-streptogramin. Abundance (coverage,×/Gb) =  Where, Nmapped

reads

#N

mapped reads

$

× 100/L S

!"

is the number of the reads mapped to ARG-like ORFs, LARG-like

ORF

is the

sequence length of the corresponding target ARG-like ORF sequence, n is the number of the ARG-like ORFs belonging to the same ARG type, 100 is the sequence length of the Illumina reads, and S is the size of the dataset (Gb).

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Contig ID (length)

Host

Arrangement protease ATP-binding subunit ClpA cold-shock protein CspD

(100% coverage, 99% identity)

macA macB

Escherichia coli

macA

(100% coverage, 99% identity)

macB

Escherichia coli HumanGut_13041 (96% coverage, 100% identity) (3,698 bp) Influent_46410 (1,100 bp)

Escherichia coli

Influent_2944 (996 bp)

macA

macB

transposase

(98% coverage, 99% identity)

HumanGut_13130 (3,894 bp)

PF1m_8410 (3,476 bp)

macB

Escherichia coli

Virulence factor virK

macB

(100% coverage, 99% identity)

CF20d_4856 (5,890 bp)

CF80d_26857 (3,327 bp)

Others

ATP-dependent Clp protease adaptor protein ClpS

(100% coverage, 99% identity)

Influent_28755 (1,036 bp)

MRG

macB

Escherichia coli

PF1m_14709 (1,503 bp)

MGE macA

macB

Escherichia coli

CF80d_574 (4,277 bp)

ARG

tetR

transposase

tetA

multidrug transporter

transposon Tn1721 resolvase

tetR

Proteus mirabilis

tetA

amidase Tn1721 transposase

multidrug transporter isochorismatase transposase

(99% coverage, 99% identity) arsenic transporter Corynebacterium glutamicum arsR

hypothetical protein

tetR

tetA

(42% coverage, 99% identity)

Chlamydia trachomatis

transposase tetR

phage integrase family protein

tetA

(96% coverage, 99% identity) conjugative transfer ATPase

Acinetobacter baumannii

tetR

tetA

(100% coverage, 100% identity) integral membrane protein tetracycline repressor protein homolog Trh

CF20d_7850 (1,942 bp)

Corynebacterium glutamicum

CF80d_9370 (2,733 bp)

Corynebacterium glutamicum

PF1m_11930 (3,929 bp)

Corynebacterium glutamicum

tetR

tetA

mycinamicin resistance protein homolog MyrA

(100% coverage, 99% identity) mycinamicin resistance protein homolog MyrA

tetA

hypothetical protein

(83% coverage, 100% identity)

tetA

mycinamicin resistance protein homolog MyrA

(99% coverage, 99% identity)

Figure 3. The arrangements of shared multiple-ARG-carrying contigs and their putative hosts (best hits on NCBI) in different samples.

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CF20d_5145 (4765 bp) GCN5-like N-acetyltransferase 0

1000

qacEdelta1 sul1

aadA5 2000

dfrA17

3000

intI1 4000

4765 (bp)

Class 1 integron ARG MGE Others

Length of 86% of the contig Identity of 100% aligned to two strains

 Escherichia coli O25b:H4-ST131 str. EC958 (HG941719.1)  Salmonella enteric subsp. enterica serovar Newport str. USMARC-S3124.1 (CP006631.1)

Figure 4. A complete class 1 integron (carrying aadA5 and dfrA17) detected on contig CF20d_5145 (4765 bp) and annotated as a fragment of Escherichia coli O25b:H4ST131 str. EC958 (HG941719.1) and Salmonella enteric subsp. enterica serovar Newport str. USMARC-S3124.1 (CP006631.1) by NCBI BLASTN (best hit with a query coverage of 86%, an e-value of 0.0, and an identity of 100%).

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Figure 5. The distribution of ARG-carrying contigs in chicken metagenome datasets via differential coverage-based binning. All nodes represent scaffolds, scaled by the square root of their length, and colored by community taxonomy.

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