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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Environmental Science & Technology

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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

1 ACS Paragon Plus Environment


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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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.

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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


attributed

to

the

same


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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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Page 24 of 29

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.

24

ACS Paragon Plus Environment

Page 25 of 29


Figure 1. The analysis pipeline of this study.

25



Page 26 of 29

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).

26


Page 27 of 29


Contig ID (length)

Host

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

(100% coverage, 99% identity)

macA macB

Escherichia coli

macA


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


HumanGut_13130 (3,894 bp)

PF1m_8410 (3,476 bp)

macB

Escherichia coli

Virulence factor virK

macB


CF20d_4856 (5,890 bp)

CF80d_26857 (3,327 bp)

Others

ATP-dependent Clp protease adaptor protein ClpS


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


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)


PF1m_11930 (3,929 bp)


tetR

tetA

mycinamicin resistance protein homolog MyrA

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

tetA

hypothetical protein


tetA

mycinamicin resistance protein homolog MyrA


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

27



Page 28 of 29

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%).

28


Page 29 of 29


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.

29


Metagenomic Assembly Reveals Hosts of Antibiotic Resistance

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