Article pubs.acs.org/est
Fishmeal Application Induces Antibiotic Resistance Gene Propagation in Mariculture Sediment Ying Han, Jing Wang,* Zelong Zhao, Jingwen Chen, Hong Lu, and Guangfei Liu Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, Liaoning, P.R. China S Supporting Information *
ABSTRACT: Antibiotic resistance genes (ARGs) are globally prevalent in mariculture sediment, and their presence is an issue of concern in the context of antibiotic use. Although large amounts of fishmeal have been released into the sediment, the role of fishmeal in ARG dissemination remains unclear. In this study, highthroughput ARG profiles in representative fishmeal products and the impact of fishmeal on the sediment resistome were investigated. A total of 132 unique ARGs and 4 mobile genetic elements (MGEs) were detected in five fishmeal products. ARG abundance and diversity in the mariculture microcosm sediment were significantly increased by the addition of fishmeal, and trends in ARG patterns correlated with the resident bacterial community in sediment (P < 0.05). After DNase treatment of fishmeal removed 84.3% of total ARGs, the remaining nutrients in fishmeal increased the relative abundance but not the diversity of ARGs in microcosm sediment. Our study has revealed for the first time that fishmeal itself is a major reservoir for ARGs, and the shift in the bacterial community induced by the nutrients in fishmeal is the main driver shaping the resistome in mariculture microcosm sediment. Our findings caution against the previously unperceived risk of ARG propagation in fishmeal-receiving ecosystems.
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pressure.20 TMP resistance genes (dfrA1, dfrA5, dfrA12, and df rA15 genes) were screened in sediment bacteria isolated from a fish farm in Pakistan with no known history of antibiotic application by performing polymerase chain reaction (PCR) and amplicon sequencing.21 In addition to antibiotics, the sediment resistome is also affected by the direct introduction of ARGs,22 shifts in the microbial community or HGT.23−25 These factors and underlying mechanisms may influence the dynamics of ARGs in the mariculture environment and should be explored to reduce the potential risks of ARGs. Modern mariculture production relies on the use of formulated feedstuff that commonly incorporates fishmeal as the fundamental ingredient for large-scale and intensive fish breeding. In the aquaculture sector, the total fishmeal consumption in 2006 was estimated to be 3724 thousand tons.26 The total use of terrestrial animal meal as a cheaper protein source for mariculture represents less than 1−2% of global aquaculture foodstuff, but there is room for further growth and expansion.27 Although foodstuff is intensively used in aquaculture production, less than 35% is absorbed by reared animals, and the excess is released into the environment.28 Fishmeal is often made by steaming, drying, rendering, and
INTRODUCTION The increasing prevalence of antibiotic resistance genes (ARGs, collectively referred to as the resistome) among environmental bacteria and even pathogens has become a major global challenge to public health and modern medicine in recent years.1,2ARGs are duplicated by their hosts and spread through horizontal gene transfer (HGT) between cells, a process that is mediated by mobile genetic elements (MGEs, i.e., plasmids, integrons, and transposons),3−5 making them “easy-to-get” and “hard-to-lose”.6−8 Therefore, understanding the dynamics of ARG propagation and identifying their environmental reservoirs are critical for developing strategies to mitigate their spread.9,10 Mariculture sediment is a hot spot for the global exchange of ARGs.11,12 ARGs in mariculture sediment enter the food chain via contaminated seafood and are at a high risk for transfer to human pathogens. To date, most studies investigating antibiotic resistance and aquaculture have focused on the selective pressure exerted by the use of prophylactic and therapeutic antibiotics.13−16 However, there is growing awareness that abundant ARGs and ARB persist in mariculture sediment in the absence of antibiotics.17 For example, tetracycline (TC), sulfonamide (SA), and trimethoprim (TMP) resistance genes are highly persistent in fish farm sediment in the Baltic Sea, even after several years without antibiotic usage.18,19 Antibioticresistant enterococci were recovered from the sediment of a Mediterranean coastal fish farm despite the absence of selective © XXXX American Chemical Society
Received: June 5, 2017 Revised: August 10, 2017 Accepted: August 15, 2017
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DOI: 10.1021/acs.est.7b02875 Environ. Sci. Technol. XXXX, XXX, XXX−XXX
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Environmental Science & Technology packaging low-value fish obtained from wild fisheries or byproducts of fish processing. Previous studies detected certain etiologic agents in formulated feedstuff, such as antibiotics, ARBs, and heavy metals,29 which potentially promote ARG dissemination in the environment. However, ARG abundance in fishmeal itself and the impact of fishmeal on the resistome in mariculture sediment have not yet been reported. As demonstrated by Hofacre et al., strains isolated from fishmeal were resistant to ampicillin, amoxicillin, clavulanic acid, or cephalothin, and most contained integrons.30 However, traditional culture methods often detect low-abundance cultivable microbes while excluding the uncultivable majority of microbes.31 In this study, high-throughput quantitative PCR (qPCR) and Illumina 16S rRNA gene sequencing were combined to explore the broad spectrum of ARGs in fishmeal and the mechanism underlying ARG alterations in mariculture sediment. The objectives of this study are (1) to investigate the diversity and abundance of ARGs in representative fishmeal products; (2) to determine the potential effects of fishmeal on the resistome in mariculture sediment; and (3) to address the major factors influencing the dynamics of sediment resistomes.
oxytetracycline (OTC); eight SAs including sulfapyridine (SPD), sulfadiazine (SDZ), sulfamethoxazole (SMX), sulfathiazole (ST), sulfadimidine (SDM), sulfachlorpyridazine (SCP), sulfadimethoxine (SM2), and TMP; five fluoroquinolones (FQs) including norfloxacin (NORF), enoxacin (ENX), enrofloxacin (ENRO), ciprofloxacin (CIP), and levofloxacin (OFL); three macrolides (MLs) including azithromycin (AZI), roxithromycin (ROX), and clarithromycin (CLA); and other antibiotics, including penicillin G (PEN G), lincomycin (LIN), and vancomycin (VAN), were analyzed in this study. Positive detection was recorded when the signal-to-noise ratio (S/N) of the parent ion to daughter ion transition was ≥3:1 and when compound retention times fell within 0.4 min of the predicted retention times of the mean determined from the initial calibration. Samples were spiked with surrogate standards prior to extraction to assess the recovery of target antibiotics in samples. The recoveries of surrogate standards ranged from 30% to 107%. The recoveries (n = 6) of target antibiotics were 49−112%, and the relative standard deviations (RSD) were less than 20%. The reported final concentrations of antibiotics were corrected for the recoveries of their respective surrogates. Mariculture Microcosm Setup and Incubation. On the basis of the OECD 308 test,33 microcosms were set up in 500 mL flasks with 200 g of sediment and 300 mL of seawater. After a two-week acclimation period under experimental conditions (20 °C, 100 rpm), three groups were established in triplicate. Microcosms spiked with 0%, 0.1%, and 0.5% fishmeal (a fishmeal sample from Peru, FM(PE1)) were designated the U, LM, and HM groups, respectively. All groups were incubated at a constant 20 °C and gently shaken at 100 rpm without disturbing the sediment phase. Seawater was added to the microcosm twice per week to compensate for water loss. Periodic sediment samples (∼10 g) were taken from each flask on days 0, 3, 14, 28, and 50 during incubation and stored at −80 °C until further analyses were carried out. To further estimate the impact of extracellular DNA (eDNA) and nutrients in fishmeal on the sediment resistome, another microcosm experiment was performed under the same conditions. Different treatment groups were investigated in triplicate by adding 0, 0.2, or 1.0 g of DNase I-treated fishmeal (see Text S4 in the SI). DNA was extracted from the treated fishmeal, initial sediment, and Day 50 sediment for ARG detection using a high-throughput qPCR method. Physicochemical Analyses and DNA Extractions of Sediment and Fishmeal. Sediment particle size was tested with a laser particle analyzer (Mastersizer 2000, England). Sediment (∼20 g) was freeze-dried, homogenized, and stored in a 50 mL sterilized tube at −80 °C. The chemical composition of the sediment was tested by performing X-ray fluorescence analysis (XRF-1800, Shimadzu, Japan), and metal elements such as chromium (Cr), copper (Cu), zinc (Zn), and lead (Pb) were determined via inductively coupled plasma-mass spectrometry (ICP-MS, Agilent 7500, Agilent, U.S.A.). Total nitrogen and carbon levels were measured with a Vario EL III Element Analyzer (Elementar, Germany).34 Sediment pH was measured using a pH meter (Mettler Toledo Fiveplus FE20, Shanghai, China) after the dried sediment was soaked in 2 M KCl with a volume ratio of 2.5. The sediment properties are described in the SI. Genomic DNA was extracted from 500 mg of freeze-dried sediment with a FastDNA Spin Kit for Soil (MP Biomedical, U.S.A.) following the manufacturer’s instructions. DNA in fishmeal and terrestrial animal proteins was extracted
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MATERIALS AND METHODS Sample Collection. Commercially available fishmeal products (n = 5) and terrestrial animal protein products (n = 2) were purchased (∼500 g each) in Dalian and Shandong, China. Fishmeal products were processed in China (CN), Peru (PE, n = 2), Russia (RU) and Chile (CL). For terrestrial animal proteins, meat and bone meal (MBM) was imported from Australia, and chicken meal (CM) was imported from the U.S.A. An appropriate amount of each sample was shaken through a 1 mm sieve, spread on 30 × 30 cm2 aluminum foil and collected from the corners and the center for random sampling. Samples were stored in sterile plastic bags at room temperature at approximately 25 °C in the dark. Sediment and water samples for the microcosm study were collected in August 2015 from a mariculture farm (122°37′ N, 39°23′ E) in Dalian, China. The farm had cultured both shrimp and sea cucumbers using blue clams as feedstuff for 8 years, and there was no known history of antibiotics or fishmeal use. The top 10 cm of surface sediment and the overlying seawater were collected at three different locations in the pond. The sediment and water were individually homogenized to avoid sampling fluctuations. All sediment and water samples were kept on ice, transported back to the laboratory within 6 h, and immediately subjected to the mariculture microcosm setup and physiochemical characterization. Detection of Antibiotics in Fishmeal Products using Liquid Chromatography Tandem Mass Spectrometry (HPLC/MS/MS). Extraction procedures were carried out following a reported method with minor modifications.32 Briefly, antibiotics were extracted from fishmeal samples using an accelerated solvent extraction system (ASE 350; Dionex, Sunnyvale, CA, U.S.A.) and concentrated with Oasis HLB cartridges (6 mL, 500 mg, 60 μm; Waters, Milford, MA, U.S.A.), as detailed in the Supporting Information (SI). Sample extracts were analyzed with an LC/MS/MS (Agilent 1100 HPLC and tandem 6410B quadrupole mass spectrometer, Agilent, MA, U.S.A.) equipped with an electron-spray ionization (ESI) source in Multiple Reaction Monitoring (MRM) mode. HPLC conditions and MRM parameters are described in SI. Four tetracyclines (TCs) including tetracycline (TC), doxycycline (DXC), chlortetracycline (CTC) and B
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Community richness and α-diversity were estimated with the observed species, Chao1, Shannon, Simpson, and ACE indices. Rarefaction curves were generated to compare bacterial OTU diversity levels. Differences between microbial communities (βdiversity) were compared using the weighted Unifrac metric, principal coordinate analysis (PCoA) based on Bray−Curtis distances, and the Adonis test. LEfSe (linear discriminant analysis size effect) based on the relative abundance of the microbial community was carried out using an online Galaxy module to identify biomarkers based on a comparison of the relative abundances of untreated and fishmeal-treated microcosms. Potential human pathogenic bacteria (HPB) in microcosm sediment were screened using QIIME through the default “closed reference OTUs picking” method against a human pathogenic bacteria 16S rRNA database45 with a threshold of 99% similarity following the methodology of a previous study.46 Detailed information regarding this screening process is described in the SI (Text S3). Network Analysis and Visualization. Co-occurrence patterns of ARGs and the microbial community were revealed by a network analysis using Cytoscape (version 3.3.0) with the CoNet plug-in.47,48 Pairwise correlations were determined using the Pearson correlation, Spearman correlation, Mutual information, Bray−Curtis dissimilarity, and Kullback−Leibler dissimilarity tests. Only ARGs detected in more than three samples were included in correlation calculations. Correlation efficiencies and P-values were analyzed using a Simes method implemented with the CoNet plug-in.49 Only correlations found to be significant with at least two methods were included to avoid false-positive correlations.50 The network topology was visualized in Gephi (0.9.1) using the Frucherman Reingold algorithm.51 Only correlations with a p-value above 0.8 and a significance level below 0.05 were displayed.52 Statistical Analyses. Averages and standard deviations of all data were determined using Microsoft Excel 2013. The distribution of ARGs in fishmeal products, MBM, and CM were visualized using Circos software (http://circos.ca/) online. Absolute and normalized ARG copies were plotted with OriginPro 9.1 (OriginLab, U.S.A.). Treatment and time course significance were analyzed with the Adonis test in the “vegan” package in R. Statistically significant differences were accepted when the p-value was below 0.05. PcoA was carried out to visualize differences in the comparative ARG abundance and microbial communities based on a Bray−Curtis distance matrix. Heatmaps illustrating natural logarithm-transformed ARG FC values and microbial community abundance were generated with the pheatmap package in R (Version 3.2.5). Correlations between environmental factors, bacterial community, MGEs, and ARGs in microcosm sediment were explored by performing canonical correspondence analysis (CCA). Partial CCA was also carried out in R with the vegan 2.3−5 package. Pearson correlation and other significance tests were performed using SPSS V20.0 (IBM, U.S.A.).
using a TIANamp Feedstuff Animal DNA Kit (TIANGEN BIOTECH CO., LTD, Beijing, China). DNA extraction for each sample was carried out in triplicate and combined to avoid potential bias. DNA quality was tested using a NanoDrop 2000 (Thermo Fisher Scientific Inc. U.S.A.), and the concentrations of DNA extracts were evaluated using a Quant-it Pico Green dsDNA Assay Kit (Invitrogen Corporation, CA, U.S.A.). High-Throughput Quantitative PCR. ARGs in samples were quantified by performing high-throughput qPCR in a Warfergen SmartChip Real-time PCR System (Warfergen, Inc., U.S.A.) following a previously described methodology.10,35−37 Quality assurance/quality control (QA/QC) was performed according to a standard protocol provided by Wafergen Biosystems. Each sample was tested with three technical replicates, and DNA-free water was included as a negative control. An amplification efficiency of 2 ± 0.2 and unique peaks were set for data validation. A threshold cycle (Ct) value of 31 was the detection limit. The copy number of each well was calculated based on Ct values with the following equation: copy number = 10∧((31 − Ct)/(10/3)). The copies of each ARG were normalized by dividing by the corresponding number of 16S rRNA gene copies. The number of 16S rRNA genes per cell was estimated to be 4.1 according to the Ribosomal RNA Operon Copy Number Database (rrnDB version 4.3.3),38 and the number of ARG copies per cell was estimated based on this value. Normalized ARG numbers were transformed to absolute ARG copies based on 16S rRNA encoding copies, which were tested separately in a Stratagene Mx3005p qPCR System (Agilent Technologies, Foster City, CA, U.S.A.).39 Fold change values (FC) were obtained using a comparative Ct method with reference to the control (D0). Normalized ARG copies, absolute ARG copies, and FC values were all calculated using Excel 2013 (Microsoft Office 2013, Microsoft, U.S.A.). 16S rRNA Gene Amplicon Sequencing and Data Processing. DNA extracts from periodic sediment samples and dosed fishmeal were subjected to 16S rRNA gene sequencing by Novogene Bioinformatics Technology Co., Ltd. (Beijing, China). The hypervariable region (V3−V4) of the 16S rRNA gene was amplified using universal bacterial primers (515F and 806R) with specific barcodes on a Bio-Rad T100 Thermal Cycler (Bio-Rad Company, CA, U.S.A.). The reactions were performed in a final 30-μL reaction mixture volume, including 15 μL of Phusion High-Fidelity PCR Master Mix with GC buffer (New England BioLabs Inc., MA, U.S.A.), 2 μM of each primer, and 10 μL of template DNA (5−10 ng). The following PCR program was used: initial denaturation for 1 min at 98 °C, followed by 30 cycles of 10 s at 98 °C, 30 s at 50 °C, 30 s at 72 °C, and a final elongation at 72 °C for 5 min. PCR products were purified and pooled together for amplicon sequencing using an Illumina HiSeq 2500 system. Paired-end sequences were joined after the barcode and primers were removed using FLASH (V1.2.7).40 Ambiguous N, low-quality tags, and chimeras were filtered using Quantitative Insight in Microbial Ecology (QIIME, v1.7.0) to generate high-quality tags with standard parameters.41 The sequences were clustered into Operational Taxonomic Units (OTUs) with a threshold of 97% similarity using the USEARCH method.42 Representative sequences were selected for further OTU annotation using a default method involving assignment to a taxonomy compared to the Greengenes Database43 with the RDP Classifier.44 Perl scripts were used to analyze α-diversity and β-diversity. To compute α-diversity, we rarified the OTU table based on the sample with the fewest sequences using a script in QIIME.
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RESULTS Concentrations of Antibiotics in Fishmeal Products. Of the 23 antibiotics tested in this paper, nearly two-thirds (14 of 23 antibiotics) were detected in fishmeal samples. Between 6 to 11 antibiotics (median = 10) were detected in each sample. Five SAs (SMX, ST, SDM, SCP, and SM2) and one FQ (ENX) were observed in all fishmeal samples, but no TCs, VAN, or MLs were detected. As shown in Figure S1A, the total antibiotic concentrations in fishmeal samples ranged from 16.3 μg/kg to C
DOI: 10.1021/acs.est.7b02875 Environ. Sci. Technol. XXXX, XXX, XXX−XXX
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Environmental Science & Technology 54.0 μg/kg. Concentrations of ΣSAs and ΣFQs ranged from 15.0 μg/kg to 44.3 μg/kg and 1.3 μg/kg to 28.4 μg/kg, respectively. SAs and FQs were most abundant in fishmeal samples, accounting for 74.3% and 23.0% of the total antibiotic concentrations, respectively. LIN (2.3%) and PEN G (0.4%) contributed little to the total antibiotic concentrations (Figure S1B). It is not surprising that fishmeal samples contain antibiotics because these drugs accumulate in fish,32,53 and thermal treatment does not guarantee their full breakdown in food.54 Diversity and Abundance of ARGs in Fishmeal Products. A total of 132 ARGs and 4 MGEs were identified in five fishmeal samples. Up to 95 unique ARGs were found in fishmeal from China, while only 8 ARGs were present in fishmeal from Russia, suggesting ARG occurrence varied among fishmeal samples (Figure S2A). More ARGs were detected in MBM and CM samples, whereas 115 and 74 ARGs were observed, respectively. Antibiotic deactivation and efflux pump were two dominant ARG mechanisms, contributing to 77% of total detected ARGs (Figure S2B). These detected ARGs potentially confer resistance to almost all major antibiotics that are widely used for mariculture production (i.e., TC) as well as critically important human medicines such as aminoglycosides, β-lactam, MLs, and VAN. The high detection frequency of VAN resistance genes in fishmeal was unexpected given that VAN is often considered a “last resort” for the treatment of bacterial infectious diseases. The distribution of ARGs varied significantly among fishmeal samples (Figure 1). Resistance to TC (14.0−28.3%), multiple drugs (11.1−22.5% for a wide range of antimicrobial drugs) and β-lactam (11.1−21.4%) were the three most dominant types, followed by VAN (2.47−22.2%), aminoglycosides (ND (not detected)-20.9%), ML-lincosamide-streptogramin b (MLSb, ND-17.0%), and SAs (ND-11.1%), as shown in Figure
S3. The range of total normalized ARG copies per cell in fishmeal was estimated to range from 6.9 × 10−3 to 4.1 × 10−1, and the absolute abundance of ARGs was 1.2 × 109 to 2.8 × 1011 copies/g of dry solid (Figure S4), suggesting ARG levels in fishmeal samples varied significantly. The ARG profiles in MBM and CM clustered together and differed significantly from those of fishmeal samples (Adonis test, P < 0.01), which was further demonstrated by the hierarchical cluster analysis based on Bray−Curtis distance (Figure S5). All detected ARGs present in the heatmap were divided into three subgroups: (A) abundant in terrestrial animal proteins, such as the tetH, tetQ, tetS, and tetT genes; (B) abundant in fishmeal, including the vanA and vanC-01 genes; and (C) present in relatively low concentrations in all samples. The α-diversity indices of ARGs in terrestrial animal proteins were significantly higher (Table S4), and ARG abundance (copies g−1 of dry solid) in MBM was 1−2 magnitudes higher than in fishmeal. Due to their high protein content, MBM and CM are considered low-cost alternatives to fishmeal. The feasibility of using MBM and CM has been demonstrated in studies showing that replacing fishmeal with MBMs in the diet will not significantly reduce the growth of large yellow croaker.55,56 However, neglecting the higher ARG abundance in MBM and CM may intensify the dissemination of ARGs in aquaculture environments. Thus, more research should be conducted in terms of the ARG risks before replacing FM with MBM and CM. Impact of Fishmeal Application on the Sediment Resistome. Since Peruvian fishmeal products are most commonly sold, and FM (PE1) contained relatively high levels of ARGs, we chose FM (PE1) as a microcosm additive to explore the potential impacts of fishmeal on the sediment resistome. A total of 50 unique ARGs and 5 MGEs were detected in all sediment samples. The initial mariculture sediment harbored only 7 ARG subtypes with a relative abundance of 8.0 × 10−3 copies/cell (Figure 2a), which was on the same order of magnitude estimated by metagenomic analysis in a previous study.57 The normalized ARG copy numbers in both LM and HM samples gradually increased and peaked on Day 50 (2.4 × 10−2 and 4.1 × 10−2 copies/cell), at levels approximately 3- and 5-fold higher than that in the initial sediment, respectively. Normalized MGE copy numbers increased starting from Day 3, and levels more than doubled (2.1 × 10−3 in LM sediment and 3.8 × 10−3 in HM sediment) in the initial sediment (1.6 × 10−3) at the end of the incubation. Total absolute ARG abundance increased from 2.88 × 107 copies/g of dry sediment to 1.39 × 108 and 2.98 × 108 copies/g of dry sediment in LM14 and HM14, respectively (Figure 2b). In addition, we observed an interesting dose-effect of fishmeal on ARG abundance in both the LM and the HM groups (Figure 2). ARG enrichment was supported by calculating the fold change of each ARG. The greatest enrichment, at 106-fold for the mexF gene, which confers multidrug resistance (chloramphenicol and fluoroquinolone), was observed on Day 3 in the HM sediment. ARGs, including the qacEdelta1− 02, strB, and floR genes, which confer resistance to different antibiotics, were enriched more than 20-fold by treatment with fishmeal (Table S7). The overall pattern of ARGs in mariculture microcosm sediment was significantly altered by fishmeal application (Adonis test, P < 0.01). Multidrug resistance dominated the antibiotic resistance in the D0 sample, while MLSb resistance was the major resistance type during the late stage in the
Figure 1. Distribution of ARG types, including MGEs, in fishmeal, MBM, and CM. Data were visualized using the Circos software package (http://circos.ca/). Bar lengths on the outer ring represented the percentage of ARGs in each sample. FM (PE1), FM (PE2), FM (CN), FM (RU), and FM (CL) refer to fishmeal samples from Peru (n = 2), China, Russia, and Chile, respectively. MBM: meat and bone meal; CM: chicken meal. D
DOI: 10.1021/acs.est.7b02875 Environ. Sci. Technol. XXXX, XXX, XXX−XXX
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Figure 2. Effects of fishmeal on ARGs in the sediment of mariculture microcosms. (a) Absolute abundance presented as the sum of ARGs conferring resistance to a type of antibiotics. (b) Normalized abundance presented as the number of ARGs per bacterial cell. (c) Abundance of mobile genetic elements (MGEs) in sediment. (d) Normalized MGE copies per cell. U, LM, and HM represent sediment samples from different fishmeal treatment microcosm groups: untreated group (U), low-level treatment at 0.1% fishmeal (LM), and high-level treatment at 0.5% fishmeal (HM). The numbers represent days of microcosm culture. Significant differences between treatment groups and the control group were tested by performing the Student−Newman−Keuls (S−N−K) test. **P < 0.01.
Figure 3. ARG dynamics in sediment treated with fishmeal during microcosm incubation. Each column is labeled with a sample name. U, LM, and HM represent sediment samples from microcosms spiked with 0, 0.1%, and 0.5% fishmeal, respectively. The numbers represent days of microcosm culture. Plotted values are the logarithmtransformed FC value of each ARG compared to U0 sample. Columns and rows were clustered based on Bray−Curtis distances. (I) Persists in both FM (PE1) and the original sediment and enriched in sediment during incubation; (II) only detected in FM (PE1) but emerged in sediment during incubation; (III) not detected in FM (PE1) or the original sediment but emerged during incubation; and (IV) only detected in FM (PE1) but not in sediment samples. The specific ARG name and corresponding FC value were listed in Table S7.
treated groups. PCoA based on Bray−Curtis distances revealed the HM50 sample to be distinct from the other sediment samples and tended to be closer to FM (PE1) (Figure S6A), as further demonstrated by hierarchical cluster analysis showing the clustering of HM50 samples with FM (PE1). In general, the shift in ARG profiles (rows in heat maps) was classified into four patterns (Figure 3): (I) persisted in both FM (PE1) and the original sediment and enriched in sediment during incubation; (II) detected in FM (PE1) but emerged in sediment during incubation; (III) not detected in FM (PE1) or the original sediment but emerged during incubation; and (IV) only detected in FM (PE1) but not in sediment samples. ARG enrichment through fishmeal application was generally indigenous in the sediment and differed from those enriched in FM (PE1). Most of the dominant ARGs and MGEs in FM (PE1) became undetectable in the sediment following dosing. ARG abundance in the microcosm on Day 3 (5.0 × 107 copies/ g of dry sediment) was remarkably lower than that resulting from the ARGs in fishmeal (1.5 × 108 copies/g of dry sediment), suggesting that a group of ARGs in fishmeal was readily attenuated in sediment under these experimental conditions. However, some fishmeal-specific ARGs persisted in sediment. For example, VAN resistance genes (i.e., vanSB, vanXD, and vanC-03 genes) and aminoglycoside resistance genes (strB gene) emerged during the late stage of the microcosm. An ARG dilution effect was also observed in the soil following sludge amendment.58 Characterization of Microbial Communities and Potential HPB in Microcosm Sediment. A total of 1 031 576 high-quality sequences were obtained from all sediment samples, ranging from 54 027 to 100 454 sequences per sample. These sequences were assigned to 10 258 OTUs at the 97% similarity level, with an average of 4914 OTUs per
sample. The dominant phyla in the sediment were Proteobacteria, Bacteroidetes, Firmicutes, Fusobacteria, and Acidobacteria (Figure S9A), accounting for more than 75% of the total bacterial community. Rarefaction curves of OTUs at a sequencing depth of 19 400 indicated bacterial α-diversity decreased due to fishmeal application, as confirmed by evaluation of the Chao1, observed species and Shannon indices (Figure S7). A total of 28 062 sequences were obtained from the bacterial community in FM (PE1) and clustered into 360 OTUs. There were significantly fewer OTUs in fishmeal than in sediment, resulting from the unfavorable conditions for bacterial growth in fishmeal. The dominant phyla in fishmeal were Proteobacteria, Firmicutes, and Fusobacteria. In general, the bacterial composition in FM (PE1) differed from that in treated sediment (Figure S11A). We assumed that bacterial genera coexisting in fishmeal and treated sediment (LM and HM groups), but not in the initial sediment (D0 sample), were introduced by viable bacteria in FM (PE1). Only 12 genera from fishmeal survived in sediment with an abundance over 1%. These genera either decreased over time (Chitriophage and Desulfofaba) or remained in low abundance (Parabacteroides, Virginsporangium, Halothiobacillus, and Fluviicolal), as shown in Figure S11B. Therefore, most bacteria in fishmeal failed to survive in sediment and contributed little to the bacterial communities in the fishmeal-treated groups. The application of FM (PE1) to sediment significantly increased the abundance of Bacteroidetes and decreased that of Proteobacteria on the phylum level. The steady increase in E
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Figure 4. Network analysis revealing (a) co-occurrence patterns among ARG subtypes and (b) co-occurrence patterns between ARG subtypes and microbial taxa at the genus level. Nodes are colored according to modularity classes. A connection represents a strong (r > 0.8) and significant (P < 0.01) correlation. The size of each node is proportional to the number of connections.
Proteobacteria starting on Day 3 in both the LM and HM groups was attributed to the enrichment of Gammaproteobacteria and Epsilonproteobacteria at the class level (Figure S8B). The Bacteroidetes and Proteobacteria phyla tended to return to their original abundance during incubation (Figure S8B), indicating bacterial communities in sediment are resilient to 0.5% fishmeal. Resilience to disturbance is a common feature of environmental microbial communities.59 Fishmeal application also significantly altered the structure of sediment bacterial communities according to PcoA based on Bray−Curtis distance. The control group, LM, and HM groups clustered separately (Figure S7B). The first two PCos explained 35.54% and 28.32% of the difference, respectively. The LM and HM groups were separated by the first Pc, while samples in the same group were primarily distributed along the second Pc according to the time course. Differences were further demonstrated by the weighted UPGMA clustering of sediment microbial communities (Figure S9). The Desulfobulbaceae and Desulfuromonadales genera were revealed as biomarkers by LEfSe with a high LDA score (more than 4 orders of magnitude), reflecting their high abundance in fishmeal-treated microcosm sediment and low abundance in the control group (Figure S10). Enrichment of the Desulfobulbaceae and Desulf uromonadales genera as anaerobic bacteria indicated enhanced oxygen consumption after fishmeal application. On the basis of 16S rRNA gene sequencing data, 23 species of potential HPB were found in all microcosm sediments. Vibrio parahemolyticus, Vibrio cholerae, and Staphylococcus aureus were dominant, followed by Vibrio vulnificus, Mycobacterium tuberculosis, and Bacillus anthracis (Figure S12A). In this study, high levels of fishmeal significantly increased the abundance of Vibrio species on day 14, including Vibrio parahemolyticus, Vibrio cholera, and Vibrio vulnif icus, which are naturally occurring bacterial pathogens in marine sediment and are leading causes of foodborne pathogen illness and mortality worldwide. High level treatment with fishmeal altered the pattern of potential HPB in sediment (Figure S12B), as revealed by PcoA, which showed that samples treated with low levels of fishmeal clustered together while late-stage hightreatment samples separated from the others. Illumina highthroughput sequencing accurately and efficiently profiled the diversity and abundance of various pathogens at the species
level; however, whether a strain is pathogenic depends on the expression of several virulence factors. Although 16S rRNA sequences do not determine the pathogenicity of a species, they are efficacious and convenient for the initial screening and monitoring of the potential environment risk of a pathogen. Co-occurrence Patterns among ARGs, MGEs, and Microbial Taxa. The co-occurrence patterns among ARG subtypes and MGEs were explored based on strong (ρ > 0.8) and significant (P < 0.05) correlations. The network contained 25 nodes and 28 edges, and a high modularity index of 0.658 indicated an obvious modular structure.60 The topology was separated into seven modules (Figure 4A). Nodes in modules had greater numbers of correlations with each other compared to other modules. The nodes most densely connected in their modules were defined as network “hubs”. For example, the aadA1 and vanXD genes were the hubs in their module. Hubs serve as indicators to monitor the ARG clusters in the same module. The nodes consisted of major types of ARGs, including TC, β-lactam, MLSb, florfenicol, VAN, multidrug resistance genes, and MGEs. Each module harbored different types of ARGs. For example, the vanXD gene (a VAN resistance gene) was the hub of its module and co-occurred with acrA-05, ceoA, and qacEdelta1-01, which are associated with multidrug resistance. Transposase genes (tnpA-04, tolC-03, tnpA-05 genes) correlated with the blaTEM, ereB, pncA, and cmlA1−01 genes. The connections between theses ARGs indicated that these genes may be located in the same genetic elements or carried by specific bacterial species. The co-occurrence patterns among ARG subtypes, MGEs, and microbial taxa (genus-level) were also investigated using the network approach (Figure 4B). We assumed that nonrandom co-occurrence patterns between ARGs and microbial taxa indicated potential host information for ARGs if ARGs and coexisting microbial taxa had a strong and positive correlation (R2 > 0.8, P < 0.05). For instance, Megasphaera was the host of a multidrug resistance gene (mtrC-02) and a VAN resistance gene (vanSB gene). Fusobacter was potentially the host for the aac (6′)-Ib (akaaacA4)-02 and aadA1 genes, whereas Micrococcus harbored the strB gene. Only a few ARG hosts were verified in previous studies, possibly because most bacteria in marine sediment are not cultivable. Thus, network F
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Figure 5. (A) Canonical correspondence analysis (CCA) illustrating relationships among microbial phyla, ARGs and environmental factors, including total nitrogen, total carbon, and pH. The percentage of variation explained by each axis is shown, and the relationship is significant (P < 0.01) based on 999 permutations. (B) Partial CCA analysis differentiates the effects of bacterial communities, environmental factors, and mobile genetic elements (MGEs) on ARG profile alterations. TN: total nitrogen; TC: total organic carbon; and MGEs: mobile genetic elements.
analysis provided new insight into ARGs and their potential hosts in complex environmental samples.61 Factors Influencing the Dynamics of Sediment Resistomes. The relationships among environmental factors (listed in Table S8), MGEs, microbial communities, and resistomes were explored by performing CCA (Figure 5A). Selected environmental variables (total organic carbon, total organic nitrogen, pH), MGEs, and the bacterial community explained 92.42% of ARG shifts. MGEs, Bacteroidetes, and H.178 positively correlated with the first axis and LM sediment samples. Firmicutes and Proteobacteria positively correlated with the HM50 sample. Partial CCA was carried out to separate the effects of environmental factors, MGEs, and the bacterial community (Figure 5B). The bacterial community contributed to 44.97% of the total ARG variations, which was higher than the total contribution of environmental factors (20.56%) and MGEs (10.05%). The interaction between environmental factors and the bacterial community as well as the interaction between MGEs and the bacterial community accounted for 8.65% and 7.36% of total resistome variation, respectively. According to a Mantel test, there was a significant correlation between bacterial communities (OTUs) and ARG profiles in mariculture microcosm sediment based on Bray−Curtis distance (r = 0.3097, P < 0.005). To investigate the contribution of eDNA and nutrients in fishmeal to sediment resistomes, eDNA within FM (PE1) was degraded using DNase. ARG numbers in FM (PE1) decreased from 51 to 8 after DNase treatment, and the residual ARGs were aadA1, blaSFO, blaTEM, lnuA-01, oprD, qnrA, tet (35), and tetA-02. VAN and MLSb resistance genes were not detected after DNase treatment. Normalized ARG copy numbers in FM (PE1) were reduced from 0.123 to 0.0174, indicating ARGs were present at higher concentrations as eDNA than intracellular DNA (iDNA). 0.5% DNase-treated FM (PE1) significantly increased the number of normalized ARG copies in sediment on Day 50 (P < 0.05), and MLSb, multidrug, and aminoglycoside resistance gene abundances were all higher than that in the initial sediment, as shown in Figure S13. ARGs in fishmeal (blaSFO, qnrA, lnuA-01, qnrA, and tet (35) genes) were not detected in sediment, while others remained at low abundance (Figure S15). The α-diversity of ARGs in the treated group was even lower than that in the
control group on Day 50. The emergence of VAN resistance genes and other ARGs was not observed.
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DISCUSSION Source of ARGs in Fishmeal Products. Our study provided baseline data on ARGs in fishmeal products. ARG levels were high but quite variable, which was poorly linked to corresponding antibiotic concentrations. Our LC/MS/MS results suggested that the total abundance of antibiotics in fishmeal ranged from 16.3 to 54.0 μg/kg. SAs and FQs were the two most dominant types of antibiotics, but target ARGs accounted for less than 2% of total ARG abundance. Despite the highest antibiotic levels, fishmeal from Russia exhibited the lowest ARG abundance among fishmeal samples (SI). Inconsistent patterns between antibiotics and ARGs within fishmeal products were further confirmed by the frequent detection of TC, ML, and VAN resistance in the absence of corresponding antibiotics. In addition, the coselection of ARGs with heavy metals was observed in soil (at least 200 mg/kg for nickel and 400 mg/kg for copper in soil),62−64 but this explanation does not extend to ARGs in fishmeal because the background levels of heavy metals were very low (Table S5). Thus, there was no obvious selective pressure from antibiotics and heavy metals for the presence of ARGs in fishmeal products. The abundant ARGs in fishmeal products were traced back to raw materials. Prophylactic antibiotics are commonly administered to fish through feeding or bathing, thus exerting selective pressure on bacteria in the aquaculture ecosystem.13 Various bacteria in fish develop resistance to TCs, MLs, SAs, streptomycin, β-lactam, and multiple drugs.65−67 Without proper treatment, bacteria carrying ARGs in raw materials survive the process and persist in fishmeal products. In addition, ARB recontamination may also result from unsterilized equipment, packaging, transport, improper storage, and even aerosols68 in the rendering factory. Although thermal treatment (115 to 145 °C) during fishmeal processing kills most microorganisms in raw materials, ARGs may still persist as eDNA even after host bacteria have died. DNase treatment in this study indicated ARGs in fishmeal were present at higher concentrations as eDNA than as iDNA. Propagation of ARGs in Mariculture Sediment. The present study reported that fishmeal application elevated the G
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sediment increased 2-fold over the initial sediment on Day 50 (Figure 2). The normalized copy numbers of MGEs positively correlated (P < 0.05) with those of aminoglycosides, MLSb, VAN, and total ARGs (Table S9). Transposase gene (tnpA-04 gene) abundance was associated with the dissemination of specific ARGs (blaTEM gene) as revealed by our network analysis (Figure 4A). The occurrence of HGT was further confirmed by the microcosm experiment utilizing DNasetreated fishmeal, which increased ARG abundance but decreased ARG diversity compared with the control group (Table S11). The emergence of VAN resistance genes in sediment treated with FM (PE1) was not observed after DNase treatment (Figure S12), which is indirect evidence for the assimilation of eDNA in fishmeal by naturally competent resident bacteria. According to previous studies, marine sediment facilitated the uptake and expression of eDNA by transformable bacteria, and the frequency was even higher with higher organic content.78,79 Thus, eDNA within fishmeal also contributes to the emergence of ARGs in mariculture sediment bacteria. Elevated ARG abundance was observed along with potential HPB in response to fishmeal application, suggesting an increased risk of HPB capturing ARGs to form superbugs. The enrichment of several species of potential HPB via fishmeal application was also observed in fish farm sediment.80 Recently, many studies have reported the isolation of antibiotic-resistant V. vulnificus and V. parahemolyticus from fish farms, seafood, and clinical environments.81 Our study implies that long-term and repeated feeding with fishmeal may accelerate the emergence of antibiotic-resistant bacteria and even pathogens through HGT. In summary, the present study revealed fishmeal itself to be a reservoir of ARGs and demonstrated the increased diversity and elevated abundance of ARGs as well as the potential enrichment of HPB in microcosm sediment after fishmeal application. Due to the significant correlation between the trends for ARGs and bacterial compositions, the nutrient-driven dynamics of the resident sediment bacterial community were identified as the most important factors shaping sediment resistomes during fishmeal application. The emergence of certain fishmeal-specific ARGs in the sediment may also result from the introduction of ARGs in the form of eDNA in fishmeal. Environmental Implications. The findings in this study indicate fishmeal itself is a reservoir of ARGs and exerts a previously underestimated impact on the antibiotic resistome in mariculture sediment. On the basis of data for a broad spectrum of ARGs, fishmeal, which is one of the most globally traded commodities, serves as a vehicle to promote ARG dissemination internationally, highlighting the importance of ARG detection in fishmeal during food safety inspections. In addition to mariculture production, fishmeal is also widely used in livestock, inland aquaculture, or organic fertilizer, and therefore the residual fishmeal in related ecosystems deserves more attention with respect to its impact on the bacteria resistome, even in the absence of prophylactic or therapeutic antibiotic use. As the growing collection of antibiotics is offset by ARGs, our results provided useful guidelines for animal feeding to control ARG dissemination. To mitigate ARG proliferation, appropriate feeding strategies or efficient microbial agents should be designed to eliminate residual fishmeal in the environment. We also recommend the development of improved technologies to remove eDNA in fishmeal during
abundance and diversity of ARGs in mariculture microcosm sediment. FM (PE1) dosing likely resulted in up to 0.016 μg/ kg and 0.080 μg/kg antibiotic (SAs and FQs) contamination in LM and HM sediment, respectively. According to a previous study, there was no observable effect on ARGs or integrons for 100 days in lake sediment microcosm spiked with antibiotics at concentrations up to 200 μg/kg, including SMX, TMP, and CIP.69 Thus, antibiotics in fishmeal at negligible levels had no observable effects on the sediment resistome. Additionally, background antibiotics may be present in mariculture sediment, although the sediment and water samples were collected from a mariculture farm where no antibiotics were used. According to previous findings, ΣSA and ΣTET concentrations in sediment samples from major mariculture sites in China represented 0.19−1.59 μg/kg of dry sediment and 3.45−74.84 μg/kg of dry sediment.70 The combined effects of fishmeal and background antibiotics were excluded because treatment with 100 μg/kg of dry sediment tetracycline combined with 0.1% fishmeal exerted no significant effects on the sediment resistome or bacterial community compared to those spiked with only 0.1% fishmeal (Han et al., submitted for publication; unpublished results). On the basis of these data, potential selective pressure from potential background antibiotics was negated. In addition, coselection of resistance to heavy metals and antibiotics did not explain ARG enrichment, as only low background concentrations of heavy metals were observed in sediment (Table S5). The dynamics of sediment resistomes significantly correlated with the sediment bacterial community according to the results of the Mantel test (P < 0.05). Environmental factors (TN, TC, and pH value), bacterial community (relative abundance at the phylum level), and MGEs explained 92.4% of the total variation in sediment resistomes. MGEs contributed to 10.1% of the total variation, while environmental factors, the bacterial community and their interaction contributed a considerable percentage (74.2%). On the basis of these data, phylogeny was the primary determinant of variation in sediment resistome composition. Similar findings were also observed in a drinking water system,71 sewage sludge composting,36 and long-term sewage sludge-fertilized soil.58 Fishmeal favored the growth of the Fusibacter genera in microcosm sediment compared to the control group, which hosted the aadA1 and aac(6′)-Ib(akaaacA04)-02 genes according to our network analysis (Figure 4). ARB enrichment may explain the elevated ARG levels in the sediment. Fishmeal products normally contain between 60% and 72% crude protein,72 and thus fishmeal decomposition increases organic carbon, nitrogen, and oxygen demand in sediment, resulting in anoxia and changes in the sulfur, nitrogen, and phosphorus cycles.73−76 These effects induced by nutrients in fishmeal are reportedly dominant factors shaping the sediment bacterial community.77 As shown in Figure S10, most bacteria in FM (PE1) failed to compete with indigenous bacteria and tended to decay over time, indicating the bacteria in fishmeal contribute little to the dynamics of the bacterial community and ARGs in sediment. These data supported the hypothesis that nutrients in fishmeal were the determinant factors shaping the bacterial community. The lower percentage of variation explained by MGEs (10.05%) indicated HGT partially explained resistome variations during fishmeal application. HGT via MGEs is often regarded as the underlying mechanism responsible for resistome formation and dissemination in various ecological compartments. The normalized copy numbers of MGEs in HM H
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profiling, Professor Hua Wang and Yusheng Jiang at Dalian Ocean University for water and sediment sampling and Dr. Cai Lin for his kind help with the human pathogenic bacteria 16S rRNA database. We also appreciate Xin He and Hongxia Zhao at Dalian University of Technology, China, for their significant help with antibiotic analysis.
manufacturing. For a better understanding of the potential risks of fishmeal on public health, more work is needed to determine whether the detected ARGs in fishmeal transfer from “farm to fork” and threaten the safety of food animals and consumers.
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ASSOCIATED CONTENT
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S Supporting Information *
The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.est.7b02875. Text S1. Chemicals and reagents for antibiotics analysis; Text S2. sample extraction and cleanup; Text S3. methodology for screening potential pathogens based on 16S rRNA gene sequencing; Text S4. DNase treatment on FM (PE1); Table S1. MRM parameters for antibiotics and internal standards; Table S2. validation of antibiotics detection method; Table S3. normalized copies of ARG; Table S5. heavy metal concentration in FM, CM, and MBM samples; Table S6. chemical compositions of mariculture sediment by XRF; Table S7. fold change value of ARG in mariculure sediment; Table S8. chemical properties of microcosm sediment during the incubation; Table S9. Spearman’s correlation between the abundance of MGEs and ARGs in microcosms; Table S10. fold change value of ARG in mariculure sediment microcosm sediment spiked with DNase treated fishmeal; Table S11. diversity indices of ARGs in microcosm sediment spiked with DNase-treated FM(PE1); Figure S1. concentration and percentage of antibiotics in FM samples; Figures S2−S6. ARGs data; Figure S7. alpha diversity including Chao1, observed species, and Shannon index of the bacterial community of the microcosm sediment; Figure S8. abundance of 16S rRNA sequences of top 10 phyla and classes during the incubation of microcosms; Figure S9. UPGMA clustering of microbial communities in sediment samples based on weighted Unifrac distance; Figure S10. LEfSe analysis between control group and fishmeal treated group; Figure S11. fate of fishmeal bacteria in mariculture microcosm sediment during incubation; Figure S12. dynamic of the potential human pathogenic bacteria in mariculture microcosm sediment; Figure S13. normalized ARGs copies per cell in microcosm sediment spiked with DNase treated fishmeal; and Figure S14. ARGs profiles in the microcosm sediment spiked with DNase treated (PDF)
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AUTHOR INFORMATION
Corresponding Author
*Phone: +86 411 84706250; fax: +86 411 84706252; e-mail:
[email protected] (J.W.). ORCID
Jing Wang: 0000-0003-2395-3157 Jingwen Chen: 0000-0002-5756-3336 Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS This work was supported by the National Basic Research Program of China (2013CB430400). We gratefully acknowledge Dr. Yongguan Zhu at the Institute of Urban Environment, China, for his technical support with high-throughput qPCR I
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