Antibiotic Resistance Genes and Correlations with Microbial

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Antibiotic resistance genes and correlations with microbial community and metal resistance genes in full-scale biogas reactors as revealed by metagenomic analysis Gang Luo, Bing Li, Li-Guan Li, Tong Zhang, and Irini Angelidaki Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b05100 • Publication Date (Web): 08 Mar 2017 Downloaded from http://pubs.acs.org on March 8, 2017

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Environmental Science & Technology

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Antibiotic resistance genes and correlations with microbial community

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and metal resistance genes in full-scale biogas reactors as revealed by

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metagenomic analysis

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Gang Luo1#*, Bing Li2#, Li-Guan Li3, Tong Zhang3, Irini Angelidaki4∗

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1

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Department of Environmental Science and Engineering, Fudan University, 200433,

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Shanghai, China

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2

Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3),

Key Laboratory of Microorganism Application and Risk Control of Shenzhen,

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Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China

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3

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Kong SAR, China

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4

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DK-2800, Kgs Lyngby, Denmark

Environmental Biotechnology Laboratory, The University of Hong Kong, Hong

Department of Environmental Engineering, Technical University of Denmark,

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∗

Corresponding author: email: [email protected] (I. Angelidaki), tel: +45 45251429, fax:+45

45932850 Address: Department of Environmental Engineering, Technical University of Denmark, DK-2800, Kgs Lyngby, Denmark; [email protected] (G. Luo), tel/fax: +86 65642297 Address: Department of Environmental Science and Engineering, Fudan University, 200433, Shanghai, China #

The authors contributed equally to the paper 1

ACS Paragon Plus Environment


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Abstract：：Digested residues from biogas plants are often used as biofertilisers for

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agricultural crops cultivation. The antibiotic resistance genes (ARGs) in digested

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residues pose a high risk to the public health due to their potential spread to the

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disease-causing microorganisms and thus reduce the susceptibility of disease-causing

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microorganisms to antibiotics in medical treatment. High-throughput sequencing

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(HTS)-based metagenomic approach was used in the present study to investigate the

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variations of ARGs in full-scale biogas reactors and the correlations of ARGs with

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microbial communities and metal resistance genes (MRGs). The total abundance of

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ARGs in all the samples varied from 7×10-3 to 1.08×10-1 copy of ARG/copy of

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16S-rRNA gene, and the samples obtained from thermophilic biogas reactors had

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lower total abundance of ARGs, indicating the superiority of thermophilic anaerobic

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digestion for ARGs removal. ARGs in all the samples were composed of 175 ARG

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subtypes, however, only 7 ARG subtypes were shared by all the samples. Principal

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component analysis and canonical correspondence analysis clustered the samples into

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three groups (samples from manure-based mesophilic reactors, manure-based

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thermophilic reactors and sludge-based mesophilic reactors), and substrate,

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temperature, hydraulic retention time (HRT) as well as volatile fatty acids (VFAs)

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were identified as crucial environmental variables affecting the ARGs compositions.

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Procrustes analysis revealed microbial community composition was the determinant

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of ARGs composition in biogas reactors, and there was also a significant correlation

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between ARGs composition and MRGs composition. Network analysis further

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revealed the co-occurrence of ARGs with specific microorganisms and MRGs. 2


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Abstract Graphic

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1. Introduction

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Anaerobic digestion (AD) has increasingly been used in the treatment of organic

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wastes and agricultural residues. AD has the advantages of low energy input and

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generation of renewable energy in the form of biogas. The digested residues are

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generally reused as fertilizer. In this way, nutrients in the organic wastes are recycled 1.

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The utilization of digested residues helps to increase crop production and reduce the

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use of mineral fertilizers 2. Nevertheless, the content of antibiotic resistance genes

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(ARGs) in the digested residues might increase the spread of antibiotic resistance 3, 4,

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and therefore the emergence and spreading of ARGs are currently urgent public health

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issues globally 5.

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Spreading of ARGs in the environment is a result of the extensive antibiotic use in

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humans and animals 6. It has been reported that farm antibiotic use is correlated with

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the rise and spread of ARGs in human bacterial pathogens

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were shown to not only select for metal resistance genes (MRGs), but also ARGs 9,10.

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Berg et al. demonstrated that Cu exposure co-selected for resistance to clinically

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important antibiotics (e.g. vancomycin)

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microbial populations via horizontal gene transfer 26. Thereby, bacteria with antibiotic

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resistance can be formed, which could easily infect humans by contact or

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consumption of raw vegetables

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integrons and insertion sequences, are crucial for horizontal gene transfer of ARGs in

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environments

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communities due to high mobility caused by horizontal gene transfer

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. In addition, metals

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. ARGs can be spread among different

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. Mobile genetic elements, including plasmids,

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. ARGs were speculated to be uncorrelated with microbial

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15, 16

. However,

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it is possible that some of the mobile genetic elements, which are responsible for

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horizontal gene transfer, possess a narrow-host-range restricted to one or few certain

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species

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primary determinant of ARGs in soil samples

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ARGs and microbial communities as well as MRGs, and aims to provide new insights

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of the occurrence of ARGs in biogas reactors. Besides, the effects of operational

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conditions of biogas reactors on the removal of ARGs were previously investigated in

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lab-scale biogas reactors treating sewage sludge from wastewater treatment plants

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(WWTPs)

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The diversity and abundance of ARGs in full-scale biogas reactors treating various

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substrates are still unknown, and also the key environmental variables shaping the

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ARG composition remains to be elucidated.

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Various molecular tools (qPCR, DNA microarray) have been developed to detect

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selected ARGs 1, 20, 21. However, PCR bias and unspecific binding of primers limit the

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application of these methods, and it is hard to realize the broad-spectrum detection

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and quantification of ARGs in environmental samples 22. High-throughput sequencing

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(HTS)-based metagenomic approach has been successfully used in the analysis of

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functional genes and microbial community compositions of microbiomes obtained

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from biogas reactors, soil and WWTPs 23, 24, 25. With the high sequencing depth, ARGs

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can also be analyzed from the metagenomic data, and the analytical methods have

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been developed recently 22, 26.

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In Denmark, there are more than 40 centralized biogas plants, and they are running

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. For instance, a recent study found that microbial communities were the

2, 17

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. The present study is correlating

. However, only limited ARGs were quantified by qPCR approach 3, 19.

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27, 28

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with manure and industrial wastes as feedstock

. Moreover, most of the WWTPs

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have full-scale biogas reactors treating the primary and secondary sludge. The

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understanding of the presence of ARGs in the digested residues from full-scale biogas

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reactors is required to properly define the risks posed by land application. The present

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study made a detailed comparative analysis of ARGs in various full-scale biogas

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reactors via the HTS-based metagenomic approach to provide new insight of ARG

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profiles in biogas reactors. The objectives of the study were: (1) to reveal the diversity

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and abundance of ARGs; (2) to identify the key environmental variables determining

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the ARG contents; (3) to investigate the correlation between ARGs and microbial

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communities; and (4) to understand the co-occurrence of ARGs and MRGs in various

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full-scale biogas reactors.

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2. Materials and methods

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2.1 Sampling and DNA extraction

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The samples were obtained from Danish full-scale biogas reactors, which had been

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running for more than two years under similar operational conditions. The detailed

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information about operational condition and performance of each reactor is shown in

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Table 1. MT means the biogas reactors fed with manure and operated under

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thermophilic conditions. MM represents the biogas reactors fed with manure and

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operated under mesophilic conditions, and SM means the biogas reactors fed with

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sludge from WWTP and operated under mesophilic conditions. All the samples were

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collected in May of 2013. For the biogas reactors fed with manure, manure was a

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mixture mainly containing pig and cattle manure. For the biogas reactors fed with 6


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sludge, sludge was obtained from wastewater treatment plants. Samples MT2a and

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MT2b were collected from Blåhoj, and samples MT3a and MT3b were obtained from

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Lemvig biogas plants. Both Blåhoj and Lemvig biogas plants have two biogas

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reactors running in series, and thus samples were collected from both steps of the

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serial process. All the biogas plants were operated under normal conditions at the time

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of sampling, and no major changes had occurred prior to sampling, which could

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ensure the samples as representative as possible. Only one sample for each reactor

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was collected in our study since our previous study showed that HTS-based

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metagenomic approach was reproducible for ARG quantification22. The samples for

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the microbial analysis were collected in sterile tubes (15 mL) and frozen immediately

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in a cooler with dry ice. The samples for chemical analysis were collected in 0.5 L

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bottles and put in a cooler box with ice. All biogas reactors had sampling points in the

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effluent lines close to the reactors to ensure the samples as representative as possible.

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The sampling valve was opened for 5 min before sample acquisition to flush the

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sampling valve and pipeline. All the samples were transported to the laboratory within

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24 hours, and QIAamp DNA Stool Mini Kit (QIAGEN, 51504) was used to extract

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total genomic DNA of each sample. Thermo NanoDrop 1000 spectrophotometer was

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used to measure the DNA concentration and purity, and the related information can be

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found in Table S1.

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2.2 Metagenomic sequencing and quality filtering

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The genomic DNA was sent to Beijing Genomics Institute for library construction and

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sequencing using Illumina Hiseq 2000 platform by applying 101 bp paired-end 7



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strategy. Sequence reads with low quality or ambiguous were removed, and then the

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pair-end sequence reads were merged into tags to decrease the sequencing errors. The

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average length of tags was around 170 bp. The information of the raw sequenced data

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is shown in Table S1. All the metagenomic datasets were uploaded to MG-RAST

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(Rapid Annotation using Subsystems Technology for Metagenomes), and the

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accession number for each sample can be found in Table S1.

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2.3 ARGs analysis and MRGs analysis

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For the ARG annotation, the metagenomic tags of each sample were blasted against

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the structured non-redundant clean antibiotic resistance genes database (ARDB)

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(http://ardb.cbcb.umd.edu/) with the e-value at 1×10-5 by BLASTX 26. The lengths of

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antibiotic resistance genes in ARDB range widely from 186 to 4728 bp, and the

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average length is 1235 bp 22. The sequence was considered to be ARGs-like sequence

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when its best hit had similarity no less than 90 % to the reference sequences and had a

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query coverage no less than 25 amino acids

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automatically sorted into different types and subtypes of ARGs by a package of

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customized scripts, and the abundance of ARGs was normalized by the ARG

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reference sequence length and 16S rRNA gene sequence length according to our

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previous study

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commonly used database for the investigation of ARGs currently 30-36.

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For the MRGs analysis, MRGs annotation was conducted similarly by searching

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against the metal resistance genes database (http://bacmet.biomedicine.gu.se). The

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sequences were considered to be MRGs following the same cutoffs, i.e., e-value ≤

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. The ARG-like sequence were

22, 29

. The reference database used in our study is ARDB, which is

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1×10-5, similarity≥90% and query coverage ≥25 amino acids 37. The abundance of

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MRGs was also normalized by the MRG reference sequence length and 16S rRNA

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gene sequence length

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validated in our previous studies 26, 38.

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2.4 Microbial community analysis

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Microbial community analysis was conducted by MetaPhlAn, which mapped

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metagenomic tags against a catalogue of clade-specific marker sequences currently

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spanning the bacterial and archaeal phylogenies 39. The MetaPhlAn software and the

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database

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http://huttenhower.sph.harvard.edu/metaphlan/. All the parameters of MetaPhlAn

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utilized default settings except for the threshold of the e-value of 1×10−15.

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2.5 Data analysis

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Heatmaps of ARGs, microbial community and MRGs, principal components analysis

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(PCA),

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http://www.r-project.org/) with packages VEGAN, igraph, and Hmisc 18, 22. CANOCO

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5.0 were used for the canonical correspondence analysis (CCA) to correlate the ARGs

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compositions to environmental variables. All the environmental variables used for the

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CCA analysis were shown in Table S2. The co-occurrence of ARG subtypes with

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microbial community and MRGs were explored using network analysis based on

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strong (ρ>0.8) and significant (P-value2.0×10-4 copy of ARG per copy of 16S-rRNA gene in at least one sample) ARG subtypes in the anaerobic digestion samples.

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ARG

0.4 MT2a

SM5 SM1

PC 2 (27.4%)

MT4

SM3

MT2b SM2

MT3a MT3b

0.0

SM4

MT1

MM2

-0.4 MM1

-0.4

-0.2

MM3

0.0

0.2

0.4

PC 1 (49.9%)

1.0

(A) MM1 MM3

MM2

CCA2 (21.2%)

HRT

Sub

MT3b MT1 MT3a SM5

SM2 MT2b VFA Temp

SM4 SM1 SM3

-0.8

MT4 MT2a

-0.8

1.0 CCA1 (39.84%)

(B) Fig 3 PCA (A) and CCA (B) analysis of all the samples based on ARGs subtypes (Sub means substrate, and Temp means temperature)

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Fig 4 Venn diagram showing the number of shared and unique ARGs (at the subtype level) among MM, MT and SM.

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(A)

(B) Fig 5 Procrustes analyses of ARGs with microbial community (A) and MRGs (B)

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(A)

(B) Figure 6 The network analysis revealing the co-occurrence patterns of ARG subtypes with microbial taxa (A) and MRGs (B). The nodes in (A) were coloured according to genus/family and ARG types. The nodes in (B) were colored according to ARG types and MRGs. A connection represents a strong (Spearman’s correlation coefficient ρ>0.8) and significant (P-value

Antibiotic Resistance Genes and Correlations with Microbial

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