Chemosphere 112 (2014) 1–8
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Metagenomic profiles and antibiotic resistance genes in gut microbiota of mice exposed to arsenic and iron Xuechao Guo, Su Liu, Zhu Wang, Xu-xiang Zhang, Mei Li, Bing Wu ⇑ State Key Lab of Pollutant Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, PR China
h i g h l i g h t s Changes of mice gut microbiota under As and/or Fe exposure were analyzed. Co-exposure of As and Fe mitigated effects on gut microbial community in mice. Exposure of As and/or Fe changed types and abundance of ARGs. Changes of gut microbiota influenced host metabolic profiles. Gut microbiota should be considered during risk assessment of As and/or Fe.
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Article history: Received 30 December 2013 Received in revised form 13 March 2014 Accepted 16 March 2014
Handling Editor: S. Jobling Keywords: Arsenic Iron Gut microbiota Mouse Antibiotic resistance gene
a b s t r a c t Iron (Fe) has been widely applied to treat arsenic (As)-contaminated water, and Fe could influence bioavailability and toxicity of As. However, little is known about the impact of As and/or Fe on gut microbiota, which plays important roles in host health. In this study, high-throughput sequencing and quantitative real time PCR were applied to analyze the impact of As and Fe on mouse gut microbiota. Co-exposure of As and Fe mitigated effects on microbial community to a certain extent. Correlation analysis showed the shifts in gut microbiota caused by As and/or Fe exposure might be important reason of changes in metabolic profiles of mouse. For antibiotic resistance genes (ARGs), co-exposure of As and Fe increased types and abundance of ARGs. But for high abundance ARGs, such as tetQ, tetO and tetM, coexposure of As and Fe mitigated effects on their abundances compared to exposure to As and Fe alone. No obvious relationship between ARGs and mobile genetic elements were found. The changes in ARGs caused by metal exposure might be due to the alteration of gut microbial diversity. Our results show that changes of gut microbial community caused by As and/or Fe can influence host metabolisms and abundances of ARGs in gut, indicating that changes of gut microbiota should be considered during the risk assessment of As and/or Fe. Ó 2014 Elsevier Ltd. All rights reserved.
1. Introduction Gut harbors diverse microbes that play a key role in well-being of their host. The gut microbiota acts in a concerted manner to achieve metabolic communication with the host, and many different bacterial genera and species are involved in metabolite production (Wikoff et al., 2009; Mestdagh et al., 2012; Martinez et al., 2013). Changes in gut microbiota are linked with inflammatory and metabolic disorders (Nicholson et al., 2012). Many researches have showed that metal exposure could change the gut microbiota (Dostal et al., 2012). On the other hand, the gut microbiota could ⇑ Corresponding author. Address: School of the Environment, Nanjing University, No. 163 Xianlin Road, Nanjing, PR China. Tel./fax: +86 25 89680720. E-mail address: [email protected] (B. Wu). http://dx.doi.org/10.1016/j.chemosphere.2014.03.068 0045-6535/Ó 2014 Elsevier Ltd. All rights reserved.
change transportation and metabolism of metals (Wiele et al., 2010). Thus, it is necessary to identify impacts of metal on the gut microbiota under oral metal exposure. Arsenic (As) as ubiquitous metalloid has been paid much attention due to its high toxicity. Consumption of drinking water is the main source of As exposure. Iron (Fe) coagulation/flocculation has been widely applied in the actual treatment of As-contaminated water due to its low cost and high efficiency (Mohan and Pittman, 2007). Our previous study demonstrated that combined exposure of As and Fe in mouse could significantly reduce hepatic toxicity of As (Liu et al., 2013). In addition, some altered host-gut co-metabolites in serum and urine were identified, indicating the possible changes of gut microbiota. Thus, it is necessary to explore the impacts of As and/or Fe on gut microbiota to better understand their combined effects.
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Gut microbiota is an important antibiotic resistance genes (ARGs) reservoir, playing important roles in host health. Recently, it has been suggested that environmental pollution could affect abundance of resistance traits. In fact, there are various known mechanisms by which the resistance traits may be retained or propagated in the presence of metals (Baker-Austin et al., 2006; Stepanauskas et al., 2006). Bacterial resistance mechanisms exist to mitigate toxicological effects of excessive bioavailable metals as part of their stress response strategy. Defense-associated metals are often closely associated with those responsible for antibiotic resistance on mobile genetic elements (MGEs) (Beaber et al., 2004). These genes can encode for generic detoxifying mechanisms (e.g. efflux pumps), which non-specifically reduce intracellular concentrations of both metals and antibiotics (cross resistance) (Berg et al., 2010; Knapp et al., 2011). Thus, constant exposure to metals can increase ARGs’ frequency in the gene pool in environmental or gut bacteria. There are many reports on correlation between tolerance to metals (including As and Fe) and antibiotic resistance in environment (Tuckfield and McArthur, 2008; Kaur et al., 2011; Ji et al., 2012). However, effects of As and/or Fe on the ARGs in gut microbiota are still unknown. In the present study, we exposed pure water, As alone, Fe alone and As + Fe to male mice for 90 d, respectively. After exposure, gut microbiota were analyzed by high-throughput sequencing. Relationships between gut microbiota and mouse metabolic profiles were characterized by correlation analysis. The ARGs in gut microbiota were determined based on high throughput sequencing and verified by quantitative real-time PCR (qRT-PCR). This study firstly provides the effects of As and/or Fe exposure on gut microbiota and ARGs. Combined with results on metabolic profiles of mouse serum and urine, this study might be very useful for understanding of toxicological effects and mechanism of actions of As and/or Fe exposure. 2. Materials and methods 2.1. Animal treatment Five-week-old male mice (Mus musculus, ICR) were purchased from the experimental animal center of Academy of Military Medical Science of China. Forty mice (about 18 g) were randomly assigned to four groups (ten mice in one group). The mice in four groups were exposed to pure water, 3 mg L1 As, 5 mg L1 Fe and 3 mg 1 As + 5 mg L1 Fe under ambient conditions (25 ± 3 °C, 50 ± 5% relative humidity, and a 12/12 h light/dark cycle) for 90 d, respectively. The concentrations selected for As and Fe were based on our preliminary work (Liu et al., 2013). Arsenic oxide was obtained from NSI Solution Inc. Ferric chloride was obtained from Sigma Chemical Co. All experimental processes were in accordance with NIH Guide for the Care and Use of Laboratory animals. And the protocol was approved by the Committee on the Ethics of Animal Experiments of the Nanjing Military General Hospital. 2.2. Histopathological analysis On day 90, mice were anesthetized with diethyl ether to minimize suffering. Parts of intestine were dissected and fixed in 10% formalin solution. After 24–28 h the samples were dehydrated in a grade alcohol series and embedded in paraffin wax. Sections of 4–5 lm thickness were stained with hematoxylin–eosin (H&E) for pathological studies. 2.3. DNA extraction Fecal samples were collected from each mouse on day 90 to identify the effects of long-term metal exposure on gut microbiota.
Approximately 200 mg of feces was applied for total genomic DNA extraction in duplicate using FastDNA Soil Kit (MP Biomedicals, USA). Concentration and quality of the extracted DNA were determined using Nanodrop (ND-1000, NanoDrop Technologies, USA). DNA from the feces was pooled by treatment group were sequenced by pyrosequencing and Illumina sequencing (Looft et al., 2012). 2.4. Pyrosequencing Feces genomic DNA was amplified with a set of primers targeting the hypervariable V3–V4 region of 16S rRNA gene. PCRs were conducted in a reaction system (50 lL) containing 1 Amplification Buffer (Invitrogen, USA), 0.4 mM dNTP, 2 mM MgSO4, 0.4 lM each fusion primer, 1 lL template DNA and 2 U DNA Polymerase (Invitrogen, USA). Nucleotide ‘‘barcode’’ was permuted for each sample to separate corresponding reads from data pool generated in a pyrosequencing run. The protocols for PCR amplification were initial denaturation at 94 °C for 3 min, followed by 30 cycles of 94 °C for 30 s, annealing at 62 °C for 30 s and extension at 70 °C for 45 s, with a final elongation step at 70 °C for 7 min. In order to minimize the impact of potential early-round PCR errors, amplicon libraries were prepared by a cocktail of three independent PCR products for each sample. After purification using quick-spin PCR Product Purification Kit (iNtRON Biotechnology), the PCR products were quantified using Nanodrop. Then they were sent out for pyrosequencing on the Roche 454 FLX Titanium platform (Roche, Nutley, NJ). The fragment size for each library is about 300 bp. The obtained data are publicly available at European Nucleotide Archive database (Accession PRJEB3374). Following pyrosequencing, pipeline initial process tool in Ribosomal Database Project’s (RDP) was applied to remove sequences containing more than one ambiguous base ‘N’ or/and shorter than 150 bp, and check completeness of the barcodes and the adaptor. After denoising, filtering out chimeras, and removing archaeal sequences, the library size of each sample was normalized to the same sequences (the smallest sequences among four samples) to make the samples at the same sequencing depth (Ye and Zhang, 2011). Then, all effective sequences were assigned to taxonomic ranks with MEGAN program using the Lowest Common Ancestor (LCA) algorithm (Huson et al., 2007). Default parameters (absolute cutoff: BLAST bit score 35, and the relative cutoff: 10% of the top hits) were applied. 2.5. Illumina sequencing Mouse fecal genomic DNA was further sequenced using Illumina Hiseq 2000. About 10 lg DNA samples were applied to construct a library consisting of 180 bp DNA fragment sequences according to manufacturer’s instructions. Then, the sequencing strategy was paired end sequencing, 101 bp reads and 8 bp index sequence. More than 1 Gb of sequences was generated for each DNA sample. The raw reads containing three or more ‘N’ or contaminated with adaptors were removed to obtain clean reads (91.79–98.27% of raw reads). The obtained data are publicly available at European Nucleotide Archive database (Accession PRJEB3374). The clean reads from Illumina sequencing were submitted to MG-RAST (Meta Genome Rapid Annotation using Subsystem Technology, v3.2) deposited in the Argonne National Library (http:// metagenomics.nmpdr.org) under ID numbers of 4491170.3 (Control), 4491169.3 (As alone), 4491168.3 (Fe alone) and 4491167.3 (As + Fe). The nucleotide data were annotated by Clusters of Orthologous Groups (COGs) to identify their function (Koenig et al., 2011; Yu and Zhang, 2012). The maximum e-value cutoff,
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min.% identify cutoff, and min. alignment cutoff were set to 1e-10, 80%, and 50%, respectively. 2.6. Detection of ARGs and mobile gene elements For ARGs analysis, a database of resistance genes was created by downloading all sequences from Antibiotic Resistance Database (ARDB) (23137 sequences of 380 ARGs encoding resistance to 249 antibiotics) (Liu and Pop, 2009). All clean data from Illumina sequencing were compared against ARGs using BLASTx at a cutoff of e-value < 105. A read was annotated as an ARG according to its best BLAST hit if the hit had a sequence similarity of above 90% over an alignment of at least 25 amino acids (Kristiansson et al., 2011). In addition, MGEs involved in horizontal transfer of ARGs were also analyzed. The MGEs detected in this study included plasmids, insertion sequences (ISs) and integrons. The plasmids, ISs and integrons databases were downloaded from NCBI RefSeq database (2408 sequences) (Zhang et al., 2011), ISfinder (2578 sequences, 22 families of insertion sequences) (Siguier and Lestrade, 2006) and INTEGRAll (1447integrase genes and 8053 gene cassettes) (Moura et al., 2009), respectively. A read was identified as MGE if the BLAST hit had a nucleotide sequence identity of above 90% over an alignment of at least 50 bp.
2.7. Quantitative real-time PCR In order to verify abundances of ARGs from Illumina sequencing, four ARGs (tetQ, tetO, tetW and tetM) were selected and measured by qRT-PCR. The methods described by Shi et al. (2013) and Zhang et al. (2009b) were applied. Briefly, plasmids containing target genes were obtained by molecular cloning. Primers and related information for the four ARGs are shown in Table 1. The PCR protocols were denaturation at 94 °C for 5 min, followed by 35 cycles of denaturation at 94 °C for 30 s, annealing at given temperatures (Table 1) for 45 s and extension at 72 °C for 45 s, and a final extension at 72 °C for 10 min. PCR products were analyzed by gel electrophoresis and further confirmed by DNA sequencing. To ensure reproducibility, duplicate reactions were performed for each permutation of sample and primer set, and sterile water was used as negative control. The qRT-PCR was performed in Corbett Real-Time PCR Machine with the Rotor-Gene 6000 Series Software 1.7 (QIAGEN, Netherland) using the following protocol: 95 °C for 5 min, followed by 40 cycles of denaturation at 95 °C for 10 s, annealing at the 60 °C for 30 s. Each reaction was conducted in triplicate. Calibration curves were generated using 10-fold serial dilution of the plasmid containing target gene. In order to minimize the variation of
Table 1 Primer of tetQ, tetO, tetW, tetM and 16s rRNA used in quantitative real-time PCR. Target gene
Primer sequence (50 -30 )
Product size (bp)
Annealing temperature (°C)
Reference
tetQ
F: GCTCACATTGATGCAGGAA A R: CGTAGAAGCCCGGACAGTAA F: GTGCCATCCTTGAGGAAAA A R: TGCTTTCATACTGCACTCCG F: GAGAGCCTGCTATATGCCAGC R: GGGCGTATCCACAATGTTAAC F: GTGGACAAAGGTACAACGAG R: CGGTAAAGTTCGTCACACAC F: CCTACGGGAGGCAGCAG R: AATCCGCGGCTGGCA
153
58
Szczepanowski et al. (2009)
189
58
Szczepanowski et al. (2009)
168
64
Aminov et al. (2001)
406
55
Ng et al. (2001)
174
55
Ma et al. (2011)
tetO tetW tetM 16s rRNA
Fig. 1. Representative images of H&E-stained histologic sections of mouse intestine exposed to pure water, As alone, Fe alone, or As + Fe.
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extraction efficiency, eubacterial 16S rRNA gene was quantified simultaneously, and the relative abundance of target genes was normalized to each total bacterial community.
2.8. Statistical analysis The correlation analysis between altered metabolites and gut microbiota community was performed using SPSS software (Version 15). The heat maps were analyzed and constructed by R language.
3. Results and discussion 3.1. Histopathological maps Representative images of H&E-stained histologic sections of mouse intestine are shown in Fig. 1. Compared with control group, As alone exposure induced small-bowel mucosal edema of intestine. Fe alone exposure caused the necrosis of intestine. For coexposure of As and Fe, little damage was observed. We deduce that co-exposure of As and Fe has antagonistic effects on mouse intestine. The results are similar with our previous report, in which the biological effects of co-exposure of As and Fe were shown (Liu et al., 2013).
3.2. Impacts of As and Fe on gut microbial community Gut microbiota community in mice was determined by sequencing of 16S rRNA. After denoising, filtering out chimeras, and removing archaeal sequences, a total of 85,282 reads were generated for the four groups. The library size of each sample was normalized to 9354 sequences (the smallest sequences among samples). Results showed that the majority of classified sequences in mouse gut microbial ecosystem belonged to Firmicutes, Bacteroidetes and Proteobacteria phyla (Fig. 2A), which were similar with the studies for mammalian intestinal environment (Zhang et al., 2009a; Cho et al., 2012; Looft et al., 2012). At genus level, the abundance of genera is shown in Fig. 2B. Barnesiella, Lactobacillus, Bacteroides, and Clostridium XlVa genera were consistently abundant in mouse gut. Metal exposure caused the changes in gut microbial diversity. Specific changes associated with metal treatment included an increase in abundance of Firmicutes, Tenericutes, Proteobacteria, and Tenericutes phyla, and a decrease in Bacteroidetes and TM7 phyla. The Acidobacteria and Cyanobacteria/Chloroplast were only found in As group, and Verrucomicrobia was only found in Fe and As + Fe groups. At genus level, for Firmicutes phylum, Lactobacillus genus increased in As + Fe group and decreased in As alone and Fe alone groups. Of Bacteroidetes, Barnesiella and Bacteroides genera were decreased in As alone and Fe alone groups. Compared with control group, there was higher similarity for the genera in As + Fe group than those in As alone and Fe alone groups (Fig. 2B), indicating
Fig. 2. Diversity and annotated metabolism-related genes of gut microbiota in mice exposed to As and/or Fe. (A) Gut microbiota diversity at phylum level. (B) Gut microbiota diversity at genus level. (C) Annotated metabolism-related genes of gut microbiota.
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the antagonistic effects of co-exposure of As and Fe on microbial community. 3.3. Impacts of As and Fe on metabolic gene abundance of gut microbiota To identify functions of gut microbiota, the mouse fecal metagenome was analyzed using MG-RAST program. The nucleotide sequences were annotated against COGs database. Metabolism, the most abundant category in four groups, was selected for further analysis. Results are shown in Fig. 2C. Metal treatment increased gene abundance of carbohydrate transport and metabolism, but decreased gene abundance of inorganic ion transport and metabolism and secondary metabolites biosynthesis, transport and catabolism. In addition, amino acid transport metabolism was also altered by the metal treatment. No obvious difference was found on metabolic gene abundance of gut microbiota among different metal-treated groups. Changes in microbial gene abundance might result from changes in microbial community, and interesting community shifts were detected in this study. Metal treatment increased the abundance of Firmicutes, and decreased the abundance of Bacteroidetes. It has been proven that the increase of Firmicutes and decrease of Bacteroidetes could make the gut microbiota more efficient at utilization of carbohydrate from the diet (Turnbaugh et al., 2006; Nich-
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olson et al., 2012), For example, obese mice have lower levels of Bacteroidetes relative to Firmicutes in their feces compared with lean mice (Ley et al., 2005). The obese mice have improved energy-harvesting capacity, presumably because of this shift. In this study, the same shift might lead to the changes in carbohydrate transport and metabolism (Fig. 2C) in mice exposed to As and/or Fe. In addition, the decrease of inorganic ion transport and metabolism in metal treatment groups might be the protective response to metal exposure by reducing the ion acquisition. The relationships between microbial community and inorganic ion transport and metabolism are less reported and need to be further investigated. 3.4. Relationship between host metabolites and gut microbiota Our previous study has demonstrated that exposure of As and/ or Fe could changed serum and urine metabolic profiles (Liu et al., 2013). Some altered metabolites, such as hippurate, pyruvate, lacate, and TMA were found as host-gut microbiota co-metabolites. Alterations in host-gut microbiota co-metabolites were previously reported as a consequence of perturbation in gut microbiota (Yap et al., 2008; Martin et al., 2010; Claus et al., 2011). We applied correlation analysis to characterize the relationship between altered metabolites and gut microbiota community. The results are shown in Fig. 3. Histidine, NAD, ATP, and hippurate, involving in carbohy-
Fig. 3. Correlation of gut microbiota and altered metabolic profiles in mice exposed to As and/or Fe. The correlation analysis was performed using SPSS software, and results are shown by R language. The scale bar shows the correlation coefficient. Green represents the positive correlation, and red represents the negative correlation. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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drate and amino acid metabolisms, had positive correlation with Firmicutes, Tenericutes, Deferribacteres, and Verrucomicrobia, but high negative correlation with Bacteroidetes and TM7. Lipids, glutamine, alanine, lactate, isoleucine and acetone, referring to lipid, carbohydrate and amino acid metabolisms, had negative correlation with Firmicutes, Tenericutes, Deferribacteres, and Verrucomicrobia, but high positive correlation with Bacteroidetes and TM7. Proteobacteria, Actinnobacteria, Cyanobacteria/Chloroplast and Acidobacteria had high positive correlation with TMAO and Taurine, and high negative correlation with acetate and alanine. Further, based on previous reports, some abundant genera are linked with these host-gut co-metabolites (Ridlon et al., 2006; Samuel et al.,
2008; Swann et al., 2011). For example, Bacteroides is related with the metabolism of bile acids, lactate and succinate in host. Clostridium IV is involved in the metabolism of short-chain fatty acid, hippurate and lipid. Lactobacillus can participate in the lipid, choline and bile acids metabolisms. These high correlations suggest that the alteration of gut microbiota might influence metabolic profiles of host. Further, in our previous study, we found that co-exposure of As and Fe had antagonistic effects on metabolic profiles, which might be due to the coprecipitation of Fe and As and their reduced bioavailability Liu et al., 2013. In this study, we found that the reduced bioavailability might also mitigate effects on microbial community caused by co-exposure of As and Fe to a certain extent.
Fig. 4. Impacts of As and/or Fe exposure on antibiotic resistance genes (ARGs). (A) Abundance of ARGs. (B) Type of ARGs. (C) Abundance of resistant genes for different antibiotics. (D) Abundance of special ARGs. (E) Abundance of genes tetQ and tetO obtained from quantitative real-time PCR. (F) Abundance of genes tetW and tetM obtained from quantitative real-time PCR.
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Combined with above results on correlation analysis, we deduce that the shifts in microbial community caused by As and/or Fe exposure might be important reason of changes in metabolic profiles of mouse. 3.5. Changes of ARGs and MGEs Metal treatment caused a detectable increase in abundance of ARGs (Fig. 4A). The results were similar with previous studies, which found that constant exposure to metals could increase frequency of ARGs in gene pool in environmental or gut bacteria (Knapp et al., 2008). Exposure of As alone and Fe alone decreased the types of ARGs, but the co-exposure of As and Fe increased the types of ARGs (Fig. 4B). Among the ARGs, the abundance of tetracycline resistance genes predominated, which has been proven to be abundant in farm animals (Looft et al., 2012). Metal exposure significantly increased the abundance of tetracycline resistance genes (Fig. 4C). Genes tetQ, tetO and tetM predominated among the ARGs (Fig. 4D). Exposure of As alone and Fe alone increased the abundance of tetQ, but decreased the abundance of tetO and tetM. However, the co-exposure of As and Fe had antagonistic effects on these high-abundant ARGs. qRT-PCR was applied to verify the abundances of four tet genes in mouse feces. Results are shown in Fig. 4E and F. The abundance of the four genes from qRT-PCR had similar trend with the results from high-throughput sequencing.
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Above results showed the exposure of As and/or Fe could increase the abundances of ARGs in gut microbiota and change the types of ARGs, which offer a fitness advantage to bacteria living in the constant press. However, this would be an undesirable collateral effect because these resistance gene clusters could be transferred to Escherichia coli or other potential pathogens in the gut and increase their antibiotics resistance. It is well known that antibiotic resistance can be achieved by acquisition of MGEs through horizontal gene transfer, and the mobility of ARGs is closely related to plasmids, integrons and transposons (Boerlin and Reid-Smith, 2008; Stokes and Gillings, 2011). For example, the tet genes coding for tetracycline efflux proteins are normally part of plasmids in water environment (Chopra and Roberts, 2001; Roberts, 2005). Thus, in this study, MGEs (including plasmids, integrons and ISs) in gut microbiota of mice exposed to As and/or Fe are analyzed based on metagenomic data. Results are shown in Fig. 5. Metal exposure significantly reduced the relative abundance of MGEs, especially for integrons. Co-exposure of As and Fe had less influence on integrons and plasmids than exposure of As alone and Fe alone, but higher influences on ISs. Compared with the changes in types and abundances of ARGs, no obvious relationship between ARGs and MGEs could be found. It has been proven that the shift of microbial community might subsequently drive the alteration of ARGs since some ARGs were located in the host genes, not in MGEs (Shi et al., 2013). Thus, in this study, the changes in abundances and types of ARGs might be due to the alteration of gut microbial community. However, the conclusion need to be further analyzed to identify the mechanism of ARGs transfer. 4. Conclusions Exposure of As alone and Fe alone could alter the diversity and functions of gut microbiota and abundance of ARGs and MGEs. However, co-exposure of As and Fe had antagonistic effects on microbial community and ARGs to a certain extent. The alterations of gut microbiota might be an important reason of changes in metabolic profiles of mice serum and urine. The changes of gut microbiota should be considered during the risk assessment of As and/or Fe. Acknowledgements This research was supported by grants from the Natural Science Foundation of Jiangsu Province (SBK201320987), Foundation of State Key Laboratory of Pollution Control and Resource Reuse, Science Foundation of Nanjing University and National Natural Science Foundation of China (51208250). The authors declare no conflict of interest. References
Fig. 5. Impacts of As and/or Fe exposure on mobile genetic elements. (A) Relative abundance of plasmids. (B) Relative abundance of integrons. (C) Relative abundance of insertion sequences.
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Metagenomic profiles and antibiotic resistance genes in gut microbiota of mice exposed to arsenic and iron Xuechao Guo, Su Liu, Zhu Wang, Xu-xiang Zhang, Mei Li, Bing Wu ⇑ State Key Lab of Pollutant Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, PR China
h i g h l i g h t s Changes of mice gut microbiota under As and/or Fe exposure were analyzed. Co-exposure of As and Fe mitigated effects on gut microbial community in mice. Exposure of As and/or Fe changed types and abundance of ARGs. Changes of gut microbiota influenced host metabolic profiles. Gut microbiota should be considered during risk assessment of As and/or Fe.
a r t i c l e
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Article history: Received 30 December 2013 Received in revised form 13 March 2014 Accepted 16 March 2014
Handling Editor: S. Jobling Keywords: Arsenic Iron Gut microbiota Mouse Antibiotic resistance gene
a b s t r a c t Iron (Fe) has been widely applied to treat arsenic (As)-contaminated water, and Fe could influence bioavailability and toxicity of As. However, little is known about the impact of As and/or Fe on gut microbiota, which plays important roles in host health. In this study, high-throughput sequencing and quantitative real time PCR were applied to analyze the impact of As and Fe on mouse gut microbiota. Co-exposure of As and Fe mitigated effects on microbial community to a certain extent. Correlation analysis showed the shifts in gut microbiota caused by As and/or Fe exposure might be important reason of changes in metabolic profiles of mouse. For antibiotic resistance genes (ARGs), co-exposure of As and Fe increased types and abundance of ARGs. But for high abundance ARGs, such as tetQ, tetO and tetM, coexposure of As and Fe mitigated effects on their abundances compared to exposure to As and Fe alone. No obvious relationship between ARGs and mobile genetic elements were found. The changes in ARGs caused by metal exposure might be due to the alteration of gut microbial diversity. Our results show that changes of gut microbial community caused by As and/or Fe can influence host metabolisms and abundances of ARGs in gut, indicating that changes of gut microbiota should be considered during the risk assessment of As and/or Fe. Ó 2014 Elsevier Ltd. All rights reserved.
1. Introduction Gut harbors diverse microbes that play a key role in well-being of their host. The gut microbiota acts in a concerted manner to achieve metabolic communication with the host, and many different bacterial genera and species are involved in metabolite production (Wikoff et al., 2009; Mestdagh et al., 2012; Martinez et al., 2013). Changes in gut microbiota are linked with inflammatory and metabolic disorders (Nicholson et al., 2012). Many researches have showed that metal exposure could change the gut microbiota (Dostal et al., 2012). On the other hand, the gut microbiota could ⇑ Corresponding author. Address: School of the Environment, Nanjing University, No. 163 Xianlin Road, Nanjing, PR China. Tel./fax: +86 25 89680720. E-mail address: [email protected] (B. Wu). http://dx.doi.org/10.1016/j.chemosphere.2014.03.068 0045-6535/Ó 2014 Elsevier Ltd. All rights reserved.
change transportation and metabolism of metals (Wiele et al., 2010). Thus, it is necessary to identify impacts of metal on the gut microbiota under oral metal exposure. Arsenic (As) as ubiquitous metalloid has been paid much attention due to its high toxicity. Consumption of drinking water is the main source of As exposure. Iron (Fe) coagulation/flocculation has been widely applied in the actual treatment of As-contaminated water due to its low cost and high efficiency (Mohan and Pittman, 2007). Our previous study demonstrated that combined exposure of As and Fe in mouse could significantly reduce hepatic toxicity of As (Liu et al., 2013). In addition, some altered host-gut co-metabolites in serum and urine were identified, indicating the possible changes of gut microbiota. Thus, it is necessary to explore the impacts of As and/or Fe on gut microbiota to better understand their combined effects.
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Gut microbiota is an important antibiotic resistance genes (ARGs) reservoir, playing important roles in host health. Recently, it has been suggested that environmental pollution could affect abundance of resistance traits. In fact, there are various known mechanisms by which the resistance traits may be retained or propagated in the presence of metals (Baker-Austin et al., 2006; Stepanauskas et al., 2006). Bacterial resistance mechanisms exist to mitigate toxicological effects of excessive bioavailable metals as part of their stress response strategy. Defense-associated metals are often closely associated with those responsible for antibiotic resistance on mobile genetic elements (MGEs) (Beaber et al., 2004). These genes can encode for generic detoxifying mechanisms (e.g. efflux pumps), which non-specifically reduce intracellular concentrations of both metals and antibiotics (cross resistance) (Berg et al., 2010; Knapp et al., 2011). Thus, constant exposure to metals can increase ARGs’ frequency in the gene pool in environmental or gut bacteria. There are many reports on correlation between tolerance to metals (including As and Fe) and antibiotic resistance in environment (Tuckfield and McArthur, 2008; Kaur et al., 2011; Ji et al., 2012). However, effects of As and/or Fe on the ARGs in gut microbiota are still unknown. In the present study, we exposed pure water, As alone, Fe alone and As + Fe to male mice for 90 d, respectively. After exposure, gut microbiota were analyzed by high-throughput sequencing. Relationships between gut microbiota and mouse metabolic profiles were characterized by correlation analysis. The ARGs in gut microbiota were determined based on high throughput sequencing and verified by quantitative real-time PCR (qRT-PCR). This study firstly provides the effects of As and/or Fe exposure on gut microbiota and ARGs. Combined with results on metabolic profiles of mouse serum and urine, this study might be very useful for understanding of toxicological effects and mechanism of actions of As and/or Fe exposure. 2. Materials and methods 2.1. Animal treatment Five-week-old male mice (Mus musculus, ICR) were purchased from the experimental animal center of Academy of Military Medical Science of China. Forty mice (about 18 g) were randomly assigned to four groups (ten mice in one group). The mice in four groups were exposed to pure water, 3 mg L1 As, 5 mg L1 Fe and 3 mg 1 As + 5 mg L1 Fe under ambient conditions (25 ± 3 °C, 50 ± 5% relative humidity, and a 12/12 h light/dark cycle) for 90 d, respectively. The concentrations selected for As and Fe were based on our preliminary work (Liu et al., 2013). Arsenic oxide was obtained from NSI Solution Inc. Ferric chloride was obtained from Sigma Chemical Co. All experimental processes were in accordance with NIH Guide for the Care and Use of Laboratory animals. And the protocol was approved by the Committee on the Ethics of Animal Experiments of the Nanjing Military General Hospital. 2.2. Histopathological analysis On day 90, mice were anesthetized with diethyl ether to minimize suffering. Parts of intestine were dissected and fixed in 10% formalin solution. After 24–28 h the samples were dehydrated in a grade alcohol series and embedded in paraffin wax. Sections of 4–5 lm thickness were stained with hematoxylin–eosin (H&E) for pathological studies. 2.3. DNA extraction Fecal samples were collected from each mouse on day 90 to identify the effects of long-term metal exposure on gut microbiota.
Approximately 200 mg of feces was applied for total genomic DNA extraction in duplicate using FastDNA Soil Kit (MP Biomedicals, USA). Concentration and quality of the extracted DNA were determined using Nanodrop (ND-1000, NanoDrop Technologies, USA). DNA from the feces was pooled by treatment group were sequenced by pyrosequencing and Illumina sequencing (Looft et al., 2012). 2.4. Pyrosequencing Feces genomic DNA was amplified with a set of primers targeting the hypervariable V3–V4 region of 16S rRNA gene. PCRs were conducted in a reaction system (50 lL) containing 1 Amplification Buffer (Invitrogen, USA), 0.4 mM dNTP, 2 mM MgSO4, 0.4 lM each fusion primer, 1 lL template DNA and 2 U DNA Polymerase (Invitrogen, USA). Nucleotide ‘‘barcode’’ was permuted for each sample to separate corresponding reads from data pool generated in a pyrosequencing run. The protocols for PCR amplification were initial denaturation at 94 °C for 3 min, followed by 30 cycles of 94 °C for 30 s, annealing at 62 °C for 30 s and extension at 70 °C for 45 s, with a final elongation step at 70 °C for 7 min. In order to minimize the impact of potential early-round PCR errors, amplicon libraries were prepared by a cocktail of three independent PCR products for each sample. After purification using quick-spin PCR Product Purification Kit (iNtRON Biotechnology), the PCR products were quantified using Nanodrop. Then they were sent out for pyrosequencing on the Roche 454 FLX Titanium platform (Roche, Nutley, NJ). The fragment size for each library is about 300 bp. The obtained data are publicly available at European Nucleotide Archive database (Accession PRJEB3374). Following pyrosequencing, pipeline initial process tool in Ribosomal Database Project’s (RDP) was applied to remove sequences containing more than one ambiguous base ‘N’ or/and shorter than 150 bp, and check completeness of the barcodes and the adaptor. After denoising, filtering out chimeras, and removing archaeal sequences, the library size of each sample was normalized to the same sequences (the smallest sequences among four samples) to make the samples at the same sequencing depth (Ye and Zhang, 2011). Then, all effective sequences were assigned to taxonomic ranks with MEGAN program using the Lowest Common Ancestor (LCA) algorithm (Huson et al., 2007). Default parameters (absolute cutoff: BLAST bit score 35, and the relative cutoff: 10% of the top hits) were applied. 2.5. Illumina sequencing Mouse fecal genomic DNA was further sequenced using Illumina Hiseq 2000. About 10 lg DNA samples were applied to construct a library consisting of 180 bp DNA fragment sequences according to manufacturer’s instructions. Then, the sequencing strategy was paired end sequencing, 101 bp reads and 8 bp index sequence. More than 1 Gb of sequences was generated for each DNA sample. The raw reads containing three or more ‘N’ or contaminated with adaptors were removed to obtain clean reads (91.79–98.27% of raw reads). The obtained data are publicly available at European Nucleotide Archive database (Accession PRJEB3374). The clean reads from Illumina sequencing were submitted to MG-RAST (Meta Genome Rapid Annotation using Subsystem Technology, v3.2) deposited in the Argonne National Library (http:// metagenomics.nmpdr.org) under ID numbers of 4491170.3 (Control), 4491169.3 (As alone), 4491168.3 (Fe alone) and 4491167.3 (As + Fe). The nucleotide data were annotated by Clusters of Orthologous Groups (COGs) to identify their function (Koenig et al., 2011; Yu and Zhang, 2012). The maximum e-value cutoff,
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min.% identify cutoff, and min. alignment cutoff were set to 1e-10, 80%, and 50%, respectively. 2.6. Detection of ARGs and mobile gene elements For ARGs analysis, a database of resistance genes was created by downloading all sequences from Antibiotic Resistance Database (ARDB) (23137 sequences of 380 ARGs encoding resistance to 249 antibiotics) (Liu and Pop, 2009). All clean data from Illumina sequencing were compared against ARGs using BLASTx at a cutoff of e-value < 105. A read was annotated as an ARG according to its best BLAST hit if the hit had a sequence similarity of above 90% over an alignment of at least 25 amino acids (Kristiansson et al., 2011). In addition, MGEs involved in horizontal transfer of ARGs were also analyzed. The MGEs detected in this study included plasmids, insertion sequences (ISs) and integrons. The plasmids, ISs and integrons databases were downloaded from NCBI RefSeq database (2408 sequences) (Zhang et al., 2011), ISfinder (2578 sequences, 22 families of insertion sequences) (Siguier and Lestrade, 2006) and INTEGRAll (1447integrase genes and 8053 gene cassettes) (Moura et al., 2009), respectively. A read was identified as MGE if the BLAST hit had a nucleotide sequence identity of above 90% over an alignment of at least 50 bp.
2.7. Quantitative real-time PCR In order to verify abundances of ARGs from Illumina sequencing, four ARGs (tetQ, tetO, tetW and tetM) were selected and measured by qRT-PCR. The methods described by Shi et al. (2013) and Zhang et al. (2009b) were applied. Briefly, plasmids containing target genes were obtained by molecular cloning. Primers and related information for the four ARGs are shown in Table 1. The PCR protocols were denaturation at 94 °C for 5 min, followed by 35 cycles of denaturation at 94 °C for 30 s, annealing at given temperatures (Table 1) for 45 s and extension at 72 °C for 45 s, and a final extension at 72 °C for 10 min. PCR products were analyzed by gel electrophoresis and further confirmed by DNA sequencing. To ensure reproducibility, duplicate reactions were performed for each permutation of sample and primer set, and sterile water was used as negative control. The qRT-PCR was performed in Corbett Real-Time PCR Machine with the Rotor-Gene 6000 Series Software 1.7 (QIAGEN, Netherland) using the following protocol: 95 °C for 5 min, followed by 40 cycles of denaturation at 95 °C for 10 s, annealing at the 60 °C for 30 s. Each reaction was conducted in triplicate. Calibration curves were generated using 10-fold serial dilution of the plasmid containing target gene. In order to minimize the variation of
Table 1 Primer of tetQ, tetO, tetW, tetM and 16s rRNA used in quantitative real-time PCR. Target gene
Primer sequence (50 -30 )
Product size (bp)
Annealing temperature (°C)
Reference
tetQ
F: GCTCACATTGATGCAGGAA A R: CGTAGAAGCCCGGACAGTAA F: GTGCCATCCTTGAGGAAAA A R: TGCTTTCATACTGCACTCCG F: GAGAGCCTGCTATATGCCAGC R: GGGCGTATCCACAATGTTAAC F: GTGGACAAAGGTACAACGAG R: CGGTAAAGTTCGTCACACAC F: CCTACGGGAGGCAGCAG R: AATCCGCGGCTGGCA
153
58
Szczepanowski et al. (2009)
189
58
Szczepanowski et al. (2009)
168
64
Aminov et al. (2001)
406
55
Ng et al. (2001)
174
55
Ma et al. (2011)
tetO tetW tetM 16s rRNA
Fig. 1. Representative images of H&E-stained histologic sections of mouse intestine exposed to pure water, As alone, Fe alone, or As + Fe.
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extraction efficiency, eubacterial 16S rRNA gene was quantified simultaneously, and the relative abundance of target genes was normalized to each total bacterial community.
2.8. Statistical analysis The correlation analysis between altered metabolites and gut microbiota community was performed using SPSS software (Version 15). The heat maps were analyzed and constructed by R language.
3. Results and discussion 3.1. Histopathological maps Representative images of H&E-stained histologic sections of mouse intestine are shown in Fig. 1. Compared with control group, As alone exposure induced small-bowel mucosal edema of intestine. Fe alone exposure caused the necrosis of intestine. For coexposure of As and Fe, little damage was observed. We deduce that co-exposure of As and Fe has antagonistic effects on mouse intestine. The results are similar with our previous report, in which the biological effects of co-exposure of As and Fe were shown (Liu et al., 2013).
3.2. Impacts of As and Fe on gut microbial community Gut microbiota community in mice was determined by sequencing of 16S rRNA. After denoising, filtering out chimeras, and removing archaeal sequences, a total of 85,282 reads were generated for the four groups. The library size of each sample was normalized to 9354 sequences (the smallest sequences among samples). Results showed that the majority of classified sequences in mouse gut microbial ecosystem belonged to Firmicutes, Bacteroidetes and Proteobacteria phyla (Fig. 2A), which were similar with the studies for mammalian intestinal environment (Zhang et al., 2009a; Cho et al., 2012; Looft et al., 2012). At genus level, the abundance of genera is shown in Fig. 2B. Barnesiella, Lactobacillus, Bacteroides, and Clostridium XlVa genera were consistently abundant in mouse gut. Metal exposure caused the changes in gut microbial diversity. Specific changes associated with metal treatment included an increase in abundance of Firmicutes, Tenericutes, Proteobacteria, and Tenericutes phyla, and a decrease in Bacteroidetes and TM7 phyla. The Acidobacteria and Cyanobacteria/Chloroplast were only found in As group, and Verrucomicrobia was only found in Fe and As + Fe groups. At genus level, for Firmicutes phylum, Lactobacillus genus increased in As + Fe group and decreased in As alone and Fe alone groups. Of Bacteroidetes, Barnesiella and Bacteroides genera were decreased in As alone and Fe alone groups. Compared with control group, there was higher similarity for the genera in As + Fe group than those in As alone and Fe alone groups (Fig. 2B), indicating
Fig. 2. Diversity and annotated metabolism-related genes of gut microbiota in mice exposed to As and/or Fe. (A) Gut microbiota diversity at phylum level. (B) Gut microbiota diversity at genus level. (C) Annotated metabolism-related genes of gut microbiota.
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the antagonistic effects of co-exposure of As and Fe on microbial community. 3.3. Impacts of As and Fe on metabolic gene abundance of gut microbiota To identify functions of gut microbiota, the mouse fecal metagenome was analyzed using MG-RAST program. The nucleotide sequences were annotated against COGs database. Metabolism, the most abundant category in four groups, was selected for further analysis. Results are shown in Fig. 2C. Metal treatment increased gene abundance of carbohydrate transport and metabolism, but decreased gene abundance of inorganic ion transport and metabolism and secondary metabolites biosynthesis, transport and catabolism. In addition, amino acid transport metabolism was also altered by the metal treatment. No obvious difference was found on metabolic gene abundance of gut microbiota among different metal-treated groups. Changes in microbial gene abundance might result from changes in microbial community, and interesting community shifts were detected in this study. Metal treatment increased the abundance of Firmicutes, and decreased the abundance of Bacteroidetes. It has been proven that the increase of Firmicutes and decrease of Bacteroidetes could make the gut microbiota more efficient at utilization of carbohydrate from the diet (Turnbaugh et al., 2006; Nich-
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olson et al., 2012), For example, obese mice have lower levels of Bacteroidetes relative to Firmicutes in their feces compared with lean mice (Ley et al., 2005). The obese mice have improved energy-harvesting capacity, presumably because of this shift. In this study, the same shift might lead to the changes in carbohydrate transport and metabolism (Fig. 2C) in mice exposed to As and/or Fe. In addition, the decrease of inorganic ion transport and metabolism in metal treatment groups might be the protective response to metal exposure by reducing the ion acquisition. The relationships between microbial community and inorganic ion transport and metabolism are less reported and need to be further investigated. 3.4. Relationship between host metabolites and gut microbiota Our previous study has demonstrated that exposure of As and/ or Fe could changed serum and urine metabolic profiles (Liu et al., 2013). Some altered metabolites, such as hippurate, pyruvate, lacate, and TMA were found as host-gut microbiota co-metabolites. Alterations in host-gut microbiota co-metabolites were previously reported as a consequence of perturbation in gut microbiota (Yap et al., 2008; Martin et al., 2010; Claus et al., 2011). We applied correlation analysis to characterize the relationship between altered metabolites and gut microbiota community. The results are shown in Fig. 3. Histidine, NAD, ATP, and hippurate, involving in carbohy-
Fig. 3. Correlation of gut microbiota and altered metabolic profiles in mice exposed to As and/or Fe. The correlation analysis was performed using SPSS software, and results are shown by R language. The scale bar shows the correlation coefficient. Green represents the positive correlation, and red represents the negative correlation. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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drate and amino acid metabolisms, had positive correlation with Firmicutes, Tenericutes, Deferribacteres, and Verrucomicrobia, but high negative correlation with Bacteroidetes and TM7. Lipids, glutamine, alanine, lactate, isoleucine and acetone, referring to lipid, carbohydrate and amino acid metabolisms, had negative correlation with Firmicutes, Tenericutes, Deferribacteres, and Verrucomicrobia, but high positive correlation with Bacteroidetes and TM7. Proteobacteria, Actinnobacteria, Cyanobacteria/Chloroplast and Acidobacteria had high positive correlation with TMAO and Taurine, and high negative correlation with acetate and alanine. Further, based on previous reports, some abundant genera are linked with these host-gut co-metabolites (Ridlon et al., 2006; Samuel et al.,
2008; Swann et al., 2011). For example, Bacteroides is related with the metabolism of bile acids, lactate and succinate in host. Clostridium IV is involved in the metabolism of short-chain fatty acid, hippurate and lipid. Lactobacillus can participate in the lipid, choline and bile acids metabolisms. These high correlations suggest that the alteration of gut microbiota might influence metabolic profiles of host. Further, in our previous study, we found that co-exposure of As and Fe had antagonistic effects on metabolic profiles, which might be due to the coprecipitation of Fe and As and their reduced bioavailability Liu et al., 2013. In this study, we found that the reduced bioavailability might also mitigate effects on microbial community caused by co-exposure of As and Fe to a certain extent.
Fig. 4. Impacts of As and/or Fe exposure on antibiotic resistance genes (ARGs). (A) Abundance of ARGs. (B) Type of ARGs. (C) Abundance of resistant genes for different antibiotics. (D) Abundance of special ARGs. (E) Abundance of genes tetQ and tetO obtained from quantitative real-time PCR. (F) Abundance of genes tetW and tetM obtained from quantitative real-time PCR.
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Combined with above results on correlation analysis, we deduce that the shifts in microbial community caused by As and/or Fe exposure might be important reason of changes in metabolic profiles of mouse. 3.5. Changes of ARGs and MGEs Metal treatment caused a detectable increase in abundance of ARGs (Fig. 4A). The results were similar with previous studies, which found that constant exposure to metals could increase frequency of ARGs in gene pool in environmental or gut bacteria (Knapp et al., 2008). Exposure of As alone and Fe alone decreased the types of ARGs, but the co-exposure of As and Fe increased the types of ARGs (Fig. 4B). Among the ARGs, the abundance of tetracycline resistance genes predominated, which has been proven to be abundant in farm animals (Looft et al., 2012). Metal exposure significantly increased the abundance of tetracycline resistance genes (Fig. 4C). Genes tetQ, tetO and tetM predominated among the ARGs (Fig. 4D). Exposure of As alone and Fe alone increased the abundance of tetQ, but decreased the abundance of tetO and tetM. However, the co-exposure of As and Fe had antagonistic effects on these high-abundant ARGs. qRT-PCR was applied to verify the abundances of four tet genes in mouse feces. Results are shown in Fig. 4E and F. The abundance of the four genes from qRT-PCR had similar trend with the results from high-throughput sequencing.
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Above results showed the exposure of As and/or Fe could increase the abundances of ARGs in gut microbiota and change the types of ARGs, which offer a fitness advantage to bacteria living in the constant press. However, this would be an undesirable collateral effect because these resistance gene clusters could be transferred to Escherichia coli or other potential pathogens in the gut and increase their antibiotics resistance. It is well known that antibiotic resistance can be achieved by acquisition of MGEs through horizontal gene transfer, and the mobility of ARGs is closely related to plasmids, integrons and transposons (Boerlin and Reid-Smith, 2008; Stokes and Gillings, 2011). For example, the tet genes coding for tetracycline efflux proteins are normally part of plasmids in water environment (Chopra and Roberts, 2001; Roberts, 2005). Thus, in this study, MGEs (including plasmids, integrons and ISs) in gut microbiota of mice exposed to As and/or Fe are analyzed based on metagenomic data. Results are shown in Fig. 5. Metal exposure significantly reduced the relative abundance of MGEs, especially for integrons. Co-exposure of As and Fe had less influence on integrons and plasmids than exposure of As alone and Fe alone, but higher influences on ISs. Compared with the changes in types and abundances of ARGs, no obvious relationship between ARGs and MGEs could be found. It has been proven that the shift of microbial community might subsequently drive the alteration of ARGs since some ARGs were located in the host genes, not in MGEs (Shi et al., 2013). Thus, in this study, the changes in abundances and types of ARGs might be due to the alteration of gut microbial community. However, the conclusion need to be further analyzed to identify the mechanism of ARGs transfer. 4. Conclusions Exposure of As alone and Fe alone could alter the diversity and functions of gut microbiota and abundance of ARGs and MGEs. However, co-exposure of As and Fe had antagonistic effects on microbial community and ARGs to a certain extent. The alterations of gut microbiota might be an important reason of changes in metabolic profiles of mice serum and urine. The changes of gut microbiota should be considered during the risk assessment of As and/or Fe. Acknowledgements This research was supported by grants from the Natural Science Foundation of Jiangsu Province (SBK201320987), Foundation of State Key Laboratory of Pollution Control and Resource Reuse, Science Foundation of Nanjing University and National Natural Science Foundation of China (51208250). The authors declare no conflict of interest. References
Fig. 5. Impacts of As and/or Fe exposure on mobile genetic elements. (A) Relative abundance of plasmids. (B) Relative abundance of integrons. (C) Relative abundance of insertion sequences.
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