Appl Microbiol Biotechnol DOI 10.1007/s00253-014-5511-3
ENVIRONMENTAL BIOTECHNOLOGY
Abundant rifampin resistance genes and significant correlations of antibiotic resistance genes and plasmids in various environments revealed by metagenomic analysis Liping Ma & Bing Li & Tong Zhang
Received: 26 October 2013 / Revised: 27 December 2013 / Accepted: 28 January 2014 # Springer-Verlag Berlin Heidelberg 2014
Abstract In the present study, a newly developed metagenomic analysis approach was applied to investigate the abundance and diversity of antibiotic resistance genes (ARGs) and mobile genetic elements (MGEs) in aquaculture farm sediments, activated sludge, biofilm, anaerobic digestion sludge, and river water. BLASTX analysis against the Comprehensive Antibiotic Resistance Database was conducted for the metagenomic sequence data of each sample and then the ARG-like sequences were sorted based on structured subdatabase using customized scripts. The results showed that freshwater fishpond sediment had the highest abundance (196 ppm), and anaerobic digestion sludge possessed the highest diversity (133 subtypes) of ARGs among the samples in this study. Significantly, rifampin resistance genes were universal in all the diverse samples and consistently accounted for 26.9~38.6 % of the total annotated ARG sequences. Furthermore, a significant linear correlation (R2 =0.924) was found between diversities (number of subtypes) of ARGs and diversities of plasmids in diverse samples. This work provided a wide spectrum scan of ARGs and MGEs in different environments and revealed the prevalence of rifampin resistance genes and the strong correlation between ARG diversity and plasmid diversity for the first time.
Keywords Rifampin resistance gene . Mobile genetic element . Plasmid . Metagenomic analysis
Electronic supplementary material The online version of this article (doi:10.1007/s00253-014-5511-3) contains supplementary material, which is available to authorized users. L. Ma : B. Li : T. Zhang (*) Environmental Biotechnology Laboratory, Department of Civil Engineering, The University of Hong Kong, Hong Kong, SAR, China e-mail: [email protected]
Introduction A large amount of various antibiotics have been widely applied for human therapy and animal feeding. The overuse and misuse of antibiotics in many countries, especially some developing countries which have not successfully built up antibiotic application guidelines, accelerated the emergence and spread of multi-antibiotic-resistant bacteria and antibiotic resistance genes (ARGs) worldwide (Davies and Davies 2010). This represents a serious challenge to human health and threatens the effectiveness of antibiotics to treat diseases (Pruden et al. 2012). The presence of horizontal gene transfer of ARGs mediated by mobile genetic elements (MGEs, such as plasmids) among bacteria exacerbates this situation (Allen et al. 2010; Zhang et al. 2011). In recent years, ARGs have been discovered widely disseminated in various environments, including soil (Kyselkova et al. 2012), wastewater treatment plants (Yang et al. 2013), river water (Su et al. 2012), drinking water (Shi et al. 2013), seawater (Di Cesare et al. 2012), sediments (Nonaka et al. 2007), etc. The wide extent of ARGs would bring great potential threats to human health. Especially, the ARGs in sediments may infect benthonic organisms and then transfer to human beings via food chain, requiring for intensive study and risk assessment. The ARGs in effluent after being treated in wastewater treatment plants are usually straightly discharged into a natural water environment. However, previous studies of ARGs in these environments only focused on several ARG types, such as tetracycline, sulfonamide, quinolone, and macrolide resistance genes, instead of a comprehensive profile of ARGs (Luo et al. 2011; Nonaka et al. 2007; Xu et al. 2011). Besides, the detection of the ARGs related to the above antibiotics was limited by primers available for polymerase chain reaction (PCR) and real-time quantitative PCR (qPCR) (Brown and Balkwill 2009; Castiglioni et al. 2008; Di Cesare et al. 2012).
Appl Microbiol Biotechnol
Recently, metagenomic analysis has been applied to detect ARGs in wastewater-polluted river sediment samples (Kristiansson et al. 2011) and activated sludge (Zhang et al. 2011), revealing high levels of ARGs and providing overall abundances of ARGs and MGEs. These studies demonstrated an approach to explore the comprehensive profile of ARGs and MGEs simultaneously without PCR bias and also address some questions (for example, the correlation between ARGs and MGEs) which could not be answered previously by traditional methods based on PCR/qPCR. However, the number of different ARG types and their percentages were still unknown in the above studies due to the uncategorized ARGs database. In this study, six samples were collected from diverse environments, including aquaculture farm sediments, activated sludge, biofilm, anaerobic digestion sludge, and river water. The metagenomic analysis approach combined with a structured sub-database of Comprehensive Antibiotics Resistance Database (CARD) (McArthur et al. 2013) was conducted to annotate and classify ARG-like sequences into different types and subtypes. Besides, the prevalence of MGEs, including plasmids, integrons, and transposons, which were frequently reported to carry multiple ARGs and transport among microorganisms (Zhang et al. 2011), was also detected using metagenomic analysis. The objectives of this study were (1) to establish a rapid, accurate, and universal metagenomic analysis approach to detect ARGs using structured sub-database of CARD; (2) to evaluate the abundance and diversity of ARGs and MGEs in various environments; and (3) to explore the potential correlation between ARGs and MGEs in diverse samples.
Materials and methods Collection of samples Two aquaculture farm sediment samples, freshwater fishpond sediment (FFPS) and marine fish farm sediment (MFFS), were collected from San Tin and Yim Tin Tai (Hong Kong, China), respectively, in September of 2011 (Table S1). Activated sludge (AS), biofilm (BF), and anaerobic digestion sludge (ADS) samples were collected from the aeration tanks and anaerobic digester of Stanley sewage treatment plants (Hong Kong, China) in March of 2012. All the sediment and sludge samples were stored in an ice box right after collection, and then delivered immediately back to the lab, and stored in refrigerator under −20 °C before DNA extraction. River water (RW) was sampled from a local reservoir in July of 2012. The microorganisms in the river water sample were then collected by filtration using mixed cellulose ester membranes with a pore size of 0.45 μm. After that, the membrane was stored at −20 °C before DNA extraction.
DNA extraction and high-throughput sequencing The total genomic DNA of six samples was extracted using the FastDNA® SPIN Kit for Soil (MP Biomedicals, France), and DNA concentrations were determined using NanoDrop® microspectrophotometry (ND-1000, NanoDrop Technologies, DE, USA). Triplicate-extracted DNA of each sample was mixed to minimize the potential variation during the DNA extraction process. Approximately 8 μg of DNA products for each sample were used for library construction. A sequence library was constructed from ~180 bp DNA fragments and then sequenced by Illumina Hiseq 2000 (BGI, Shenzhen, China). The base-calling pipeline (Version Illumina Pipeline 0.3) was employed to process the raw fluorescence images and call reads. Raw reads with unknown nucleotides >10 % or low quality nucleotides >50 % (quality value0.900 shown in Table S6. This might suggest that the diversity of ARGs in the environment could be influenced by the diversity of the plasmids.
Table 1 The abundance and diversity of MGEs in samples from diverse environments Samples
Freshwater fishpond sediment (FFPS) Marine fish farm sediment (MFFS) Activated sludge (AS) Biofilm (BF) Anaerobic digestion sludge (ADS) River water (RW) a
Integron
Insertion sequence
Plasmid
Total MGEsa
Abundance (ppm)b
Diversityc
Abundance (ppm)
Diversity
Abundance (ppm)
Diversity
Abundance (ppm)
Diversity
12 2 47 62 46 6
59 21 169 130 184 61
6 2 30 40 36 11
59 29 97 96 129 71
1,243 307 1,558 2,630 2,083 929
411 289 514 473 579 426
1,261 310 1,635 2,732 2,165 946
529 339 780 699 892 558
Total MGEs = integron + insertion sequence + plasmid
b
Abundance: the portion of MGE-like sequences in total metagenomic sequences. The unit is “ppm” (parts per million, one read in one million reads)
c
Diversity: the number of the annotated MGE types
Appl Microbiol Biotechnol Table 2 Prediction of the diversities of ARGs for natural water sediment samples based on the linear correlation model
Figure 4 showed the correlations of the diversities and abundances between ARGs and plasmids in diverse samples. There were no significant correlations (
ENVIRONMENTAL BIOTECHNOLOGY
Abundant rifampin resistance genes and significant correlations of antibiotic resistance genes and plasmids in various environments revealed by metagenomic analysis Liping Ma & Bing Li & Tong Zhang
Received: 26 October 2013 / Revised: 27 December 2013 / Accepted: 28 January 2014 # Springer-Verlag Berlin Heidelberg 2014
Abstract In the present study, a newly developed metagenomic analysis approach was applied to investigate the abundance and diversity of antibiotic resistance genes (ARGs) and mobile genetic elements (MGEs) in aquaculture farm sediments, activated sludge, biofilm, anaerobic digestion sludge, and river water. BLASTX analysis against the Comprehensive Antibiotic Resistance Database was conducted for the metagenomic sequence data of each sample and then the ARG-like sequences were sorted based on structured subdatabase using customized scripts. The results showed that freshwater fishpond sediment had the highest abundance (196 ppm), and anaerobic digestion sludge possessed the highest diversity (133 subtypes) of ARGs among the samples in this study. Significantly, rifampin resistance genes were universal in all the diverse samples and consistently accounted for 26.9~38.6 % of the total annotated ARG sequences. Furthermore, a significant linear correlation (R2 =0.924) was found between diversities (number of subtypes) of ARGs and diversities of plasmids in diverse samples. This work provided a wide spectrum scan of ARGs and MGEs in different environments and revealed the prevalence of rifampin resistance genes and the strong correlation between ARG diversity and plasmid diversity for the first time.
Keywords Rifampin resistance gene . Mobile genetic element . Plasmid . Metagenomic analysis
Electronic supplementary material The online version of this article (doi:10.1007/s00253-014-5511-3) contains supplementary material, which is available to authorized users. L. Ma : B. Li : T. Zhang (*) Environmental Biotechnology Laboratory, Department of Civil Engineering, The University of Hong Kong, Hong Kong, SAR, China e-mail: [email protected]
Introduction A large amount of various antibiotics have been widely applied for human therapy and animal feeding. The overuse and misuse of antibiotics in many countries, especially some developing countries which have not successfully built up antibiotic application guidelines, accelerated the emergence and spread of multi-antibiotic-resistant bacteria and antibiotic resistance genes (ARGs) worldwide (Davies and Davies 2010). This represents a serious challenge to human health and threatens the effectiveness of antibiotics to treat diseases (Pruden et al. 2012). The presence of horizontal gene transfer of ARGs mediated by mobile genetic elements (MGEs, such as plasmids) among bacteria exacerbates this situation (Allen et al. 2010; Zhang et al. 2011). In recent years, ARGs have been discovered widely disseminated in various environments, including soil (Kyselkova et al. 2012), wastewater treatment plants (Yang et al. 2013), river water (Su et al. 2012), drinking water (Shi et al. 2013), seawater (Di Cesare et al. 2012), sediments (Nonaka et al. 2007), etc. The wide extent of ARGs would bring great potential threats to human health. Especially, the ARGs in sediments may infect benthonic organisms and then transfer to human beings via food chain, requiring for intensive study and risk assessment. The ARGs in effluent after being treated in wastewater treatment plants are usually straightly discharged into a natural water environment. However, previous studies of ARGs in these environments only focused on several ARG types, such as tetracycline, sulfonamide, quinolone, and macrolide resistance genes, instead of a comprehensive profile of ARGs (Luo et al. 2011; Nonaka et al. 2007; Xu et al. 2011). Besides, the detection of the ARGs related to the above antibiotics was limited by primers available for polymerase chain reaction (PCR) and real-time quantitative PCR (qPCR) (Brown and Balkwill 2009; Castiglioni et al. 2008; Di Cesare et al. 2012).
Appl Microbiol Biotechnol
Recently, metagenomic analysis has been applied to detect ARGs in wastewater-polluted river sediment samples (Kristiansson et al. 2011) and activated sludge (Zhang et al. 2011), revealing high levels of ARGs and providing overall abundances of ARGs and MGEs. These studies demonstrated an approach to explore the comprehensive profile of ARGs and MGEs simultaneously without PCR bias and also address some questions (for example, the correlation between ARGs and MGEs) which could not be answered previously by traditional methods based on PCR/qPCR. However, the number of different ARG types and their percentages were still unknown in the above studies due to the uncategorized ARGs database. In this study, six samples were collected from diverse environments, including aquaculture farm sediments, activated sludge, biofilm, anaerobic digestion sludge, and river water. The metagenomic analysis approach combined with a structured sub-database of Comprehensive Antibiotics Resistance Database (CARD) (McArthur et al. 2013) was conducted to annotate and classify ARG-like sequences into different types and subtypes. Besides, the prevalence of MGEs, including plasmids, integrons, and transposons, which were frequently reported to carry multiple ARGs and transport among microorganisms (Zhang et al. 2011), was also detected using metagenomic analysis. The objectives of this study were (1) to establish a rapid, accurate, and universal metagenomic analysis approach to detect ARGs using structured sub-database of CARD; (2) to evaluate the abundance and diversity of ARGs and MGEs in various environments; and (3) to explore the potential correlation between ARGs and MGEs in diverse samples.
Materials and methods Collection of samples Two aquaculture farm sediment samples, freshwater fishpond sediment (FFPS) and marine fish farm sediment (MFFS), were collected from San Tin and Yim Tin Tai (Hong Kong, China), respectively, in September of 2011 (Table S1). Activated sludge (AS), biofilm (BF), and anaerobic digestion sludge (ADS) samples were collected from the aeration tanks and anaerobic digester of Stanley sewage treatment plants (Hong Kong, China) in March of 2012. All the sediment and sludge samples were stored in an ice box right after collection, and then delivered immediately back to the lab, and stored in refrigerator under −20 °C before DNA extraction. River water (RW) was sampled from a local reservoir in July of 2012. The microorganisms in the river water sample were then collected by filtration using mixed cellulose ester membranes with a pore size of 0.45 μm. After that, the membrane was stored at −20 °C before DNA extraction.
DNA extraction and high-throughput sequencing The total genomic DNA of six samples was extracted using the FastDNA® SPIN Kit for Soil (MP Biomedicals, France), and DNA concentrations were determined using NanoDrop® microspectrophotometry (ND-1000, NanoDrop Technologies, DE, USA). Triplicate-extracted DNA of each sample was mixed to minimize the potential variation during the DNA extraction process. Approximately 8 μg of DNA products for each sample were used for library construction. A sequence library was constructed from ~180 bp DNA fragments and then sequenced by Illumina Hiseq 2000 (BGI, Shenzhen, China). The base-calling pipeline (Version Illumina Pipeline 0.3) was employed to process the raw fluorescence images and call reads. Raw reads with unknown nucleotides >10 % or low quality nucleotides >50 % (quality value0.900 shown in Table S6. This might suggest that the diversity of ARGs in the environment could be influenced by the diversity of the plasmids.
Table 1 The abundance and diversity of MGEs in samples from diverse environments Samples
Freshwater fishpond sediment (FFPS) Marine fish farm sediment (MFFS) Activated sludge (AS) Biofilm (BF) Anaerobic digestion sludge (ADS) River water (RW) a
Integron
Insertion sequence
Plasmid
Total MGEsa
Abundance (ppm)b
Diversityc
Abundance (ppm)
Diversity
Abundance (ppm)
Diversity
Abundance (ppm)
Diversity
12 2 47 62 46 6
59 21 169 130 184 61
6 2 30 40 36 11
59 29 97 96 129 71
1,243 307 1,558 2,630 2,083 929
411 289 514 473 579 426
1,261 310 1,635 2,732 2,165 946
529 339 780 699 892 558
Total MGEs = integron + insertion sequence + plasmid
b
Abundance: the portion of MGE-like sequences in total metagenomic sequences. The unit is “ppm” (parts per million, one read in one million reads)
c
Diversity: the number of the annotated MGE types
Appl Microbiol Biotechnol Table 2 Prediction of the diversities of ARGs for natural water sediment samples based on the linear correlation model
Figure 4 showed the correlations of the diversities and abundances between ARGs and plasmids in diverse samples. There were no significant correlations (
