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. Author manuscript; available in PMC: 2022 May 1.
Published in final edited form as: Chemosphere. 2021 Jan 7;271:129563. doi: 10.1016/j.chemosphere.2021.129563

Metagenomic Insights into Dissemination of Antibiotic Resistance across Bacterial Genera in Wastewater Treatment

Xiaoxiao Cheng 1, Jiannong Xu 2, Geoffrey Smith 2, Yanyan Zhang 1,*
PMCID: PMC7969412  NIHMSID: NIHMS1662692  PMID: 33453487

Abstract

The aim of this study was to evaluate the impacts of conventional wastewater treatment processes including secondary treatment and chlorination on the removal of antibiotic resistance genes (ARGs) and antibiotic resistant bacteria (ARB), and to assess the association of ARGs with their potential hosts in each treatment process. The results showed chlorination with subinhibitory concentration (< 8 mg/L) resulted in an increased ARB number in the disinfection effluent. qPCR analysis indicated secondary treatment increased relative abundance of ARGs in remaining bacteria whereas disinfection reduced the relative abundance of those genes effectively. Metagenomic analysis revealed a significant shift of dominating bacterial genera harboring ARGs. Along the treatment train, 48, 95 and 80 genera were identified to be the ARG carriers in primary effluent, secondary effluent, and disinfection effluent, respectively. It was also found that secondary treatment increased the diversity of potential ARG hosts while both secondary treatment and chlorination broadened the host range of some ARGs at the genus level, which may be attributed to the spread of antibiotic resistance across bacterial genera through horizontal transfer. This study highlights the growing concerns that wastewater treatment plants (WWTPs) may disseminate ARGs by associating this effect to specific treatment stages and by correlating ARGs with their bacterial hosts.

Keywords: Antibiotic resistance genes, metagenomic analysis, bacterial community, potential hosts

Graphical Abstract

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1. Introduction

The existence of antibiotic resistance in the environment is receiving increasing attention due to its worldwide health impacts. According to the World Trade Organization (WTO), antibiotic resistance has become one of the major threats to public health as it poses a threat to the lives of animals and humans (Hoffman et al., 2015). The massive use of antibiotics has turned wastewater into an environmental reservoir of antibiotic resistant bacteria (Ibarbalz et al.) and antibiotic resistance genes (ARGs) (Sorinolu et al., 2020). Wastewater treatment plants (WWTPs) are designed to remove organic compounds, nutrients and pathogens in wastewater that pollute the environment and endanger public health. For instance, the secondary treatment process can reduce high concentration of suspended solids, dissolved organics, and nutrients; and chlorine treatment during disinfection destroys bacteria, virus and their genomic materials by strong oxidation. Paradoxically, studies have shown that the ARGs impact of treated wastewater on receiving rivers is higher than that of natural rivers (Barancheshme and Munir, 2018). Thus, WWTPs may ironically play a role in the proliferation of ARB and ARGs. Secondary treatment especially the biological treatment stage, has been identified as a hotspot for the spread of ARB and ARGs due to high microbial density, nutrient richness, and various selective pressures (Rizzo et al., 2013). Moreover, secondary treatment process could promote horizontal gene transfer (HGT) indirectly, which is primarily responsible for the enrichment of ARBs and ARGs. In our previous work, we demonstrated that biological treatment increased the abundance of both ARGs and ARB, as well as increasing HGT via transduction (Cheng et al., 2019).

Chlorination is the most commonly used disinfection process as the final step of wastewater treatment prior to discharge. However, there are reports that chlorination in wastewater treatment plant could also promote the ARGs transfer and ARB concentration, which may increase the potential risk of potential selection of pathogens and regrowth rates of indigenous bacteria (Li et al., 2013). Yuan et al. (2015) studied the fate of ARB and ARGs in the wastewater treatment process and pointed out that though chlorination can make ARB inactive, for certain types of ARGs removal, such as erythromycin and tetracycline resistance genes, chlorination had limited effects. It was reported that chlorination produce disinfection by-products (DPBs) that induce antibiotic resistance through mutations, thereby increasing the risk of increased ARB and ARGs (Li et al., 2016). Xu et al. (2016) reported that the relative abundance of ARGs in final chlorine treated water was higher than they were in the raw water. Shi et al. (2013) used metagenomic methods to reveal that chlorination increase the presence of ampC and tetA genes in effluent water. It is worth noting that the chlorination may inactivate the resistant bacteria by destroying bacterial DNA or cell structure, but the drug resistance genes may still be present in the cell debris. Liu et al. (2018) also found that both extracellular ARGs and intracellular ARGs were increased 3.8 and 7.8-folds during chlorination, and once being released into the environment, these extracellular ARGs can be transferred into other bacteria horizontally.

Although the changes of ARGs after activated sludge system and chlorination have been investigated in previous work, few studies have explored the effects of different treatments on the dynamics of potential ARG host composition through the treatment processes. Association between ARGs and their hosts after different treatments have rarely been investigated. In addition to vertical gene transfer of ARGs to offspring, ARGs are likely horizontally transferred among bacteria of different taxa, which increases the risk of ARG transfer from commensal bacteria to pathogens of both animals and humans (Hu et al., 2016). In this study, to gain a comprehensive insight into the effects of treatment units on ARGs dissemination, we investigate the changing composition of the ARG host community composition and characterize the relationship between ARGs and their potential hosts in each treatment unit for the first time. The data revealed the potential risk of HGT in ARG dissemination in current treatment processes. Specifically, we studied the effects of activated sludge and chlorination process on the removal of culturable ARB, related ARGs and their potential bacterial host to reveal ARG distribution in different bacterial taxon, and thus gained insight into the antibiotic resistance of wastewater treatment train. The objectives of this study are to: 1. Characterize the effects of different treatment processes on bacterial community structure (composition); 2. Identify the bacterial taxon that carrying ARG and establish a network to connect ARGs and their potential bacterial hosts; 3. Reveal the impacts of different treatment processes on the control and spread of antibiotic resistance.

2. Materials and Methods

2.1. Sampling

Wastewater effluent samples were collected from the wastewater treatment plant (Las Cruces, New Mexico) operated in continuous flow mode at an average flow rate of 10 MGD. After Primary settling tank, the treatment system is composed of a trickling filter followed by activated sludge and chlorination with chlorine dose of 7 mg/L for 30 min. Duplicated water samples of primary treatment effluent (P), activated sludge system effluent (S) and disinfection effluent (D) were collected in May 2018 (Batch 1) and July 2018 (Batch 2) during normal operation. All samples were collected in sterile containers and were stored at 4 °C for less than 6 hours before DNA extraction.

2.2. Lab-scale chlorination test and enumeration of ARB by culture method

In order to investigate the effect of disinfectant dosage on ARB removal, different concentrations of sodium hypochlorite (NaClO) were used for laboratory chlorination tests. NaClO with a final concentration of 2 mg/L, 8 mg/L, 16 mg/L, and 32 mg/L were respectively added to the secondary effluent for a contact time of 30 min. The sodium thiosulfate at the final concentration of 1.5% was added to terminate the chlorination.

The ARB in wastewater samples taken from each sampling point in Las Cruces WWTP and the ARB in secondary effluent treated by different concentrations of NaClO in the lab were enumerated using the heterotrophic plate count (HPC) method with 5 antibiotics individually tested (ampicillin: 32 mg/L; tetracycline: 16 mg/L; ciprofloxacin: 4 mg/L; erythromycin: 8 mg/L; sulfamethoxazole: 50.4 mg/L) (Munir et al., 2011). Each of these broad-spectrum antibiotics represents antibiotic classes known as the penicillin, tetracycline, quinolone, macrolide, and sulfonamide classes, respectively. R2A agar (BD, Difco) was used as the culture medium, and the antibiotics mentioned above were added with those clinically relevant concentration, followed by the addition of 200 mg/L cycloheximide to prevent fungal growth. The sample was prepared with ten-fold serial dilutions and spread onto the plates with antibiotics. All analyses were performed in duplicate. Plates were incubated at 37 °C for 48 h and then incubated for 72 h at room temperature, and colony counts between 20 and 300 CFU were recorded for ARB concentrations in the water samples. The concentration of ARB in wastewater samples was calculated by using the number of plate count multiply by dilution ratio, then divided by the volume applied on the plate.

2.3. DNA extraction and quantification for ARGs by quantitative real-time PCR (qPCR)

For batch 1 samples, bacteria in 50 mL of primary treatment effluent (P), 350 mL of secondary treatment effluent (S) and 400 mL of disinfection effluent (D) were collected by membrane filtration with 0.22 μm pore-size membrane (Millipore, Billerica, MA); and for batch 2 samples, bacteria in 100 mL of primary treatment effluent (P), 300 mL of secondary treatment effluent (S) and 300 mL of disinfection effluent (D) were collected in the same way before DNA extraction. DNA was extracted using DNeasy PowerWater Kit (Qiagen, Germany). The DNA extracts were stored at −20°C until being used for real-time PCR and metagenomic sequencing analysis.

Twelve antibiotic resistance genes including 4 quinolone resistance genes (qnrA, qnrB, qnrC, qnrS), 3 tetracycline resistance genes (tetW, tetM, tetO), 2 sulfonamide resistance genes (sul1, sul2), 1 erythromycin resistance gene (ermB), 1 multi-resistance to β-lactam antibiotics gene (blaTEM) and 1 mobile element class 1 integron gene (intI1) were investigated through PCR assay. The eight detected genes (tetW, qnrA, qnrB, qnrS, sul1, ermB, intI1, blaTEM) were quantified by using SYBR-Green based qPCR in a Bio-Rad CFX real-time PCR detection system (Bio-Rad, California). qPCR reactions for ARGs were performed in 96-well plates with a system volume of 20 μL, containing 10 μL of 2× SsoAdanved Universal SYBR Green Supermix (Bio-Rad), 1 μL of each forward and reverse primer (10 μM), 2 μL of diluted DNA sample and 6 μL of DNA/RNA free water. 16S rRNA was used to quantify total bacteria copy number using TaqMan probe based qPCR (Sims et al., 2012). qPCR reaction for 16S rRNA was performed in 20 μL system consisting 10 μL of SsoAdvanced Universal Probes Supermix (Bio-Rad), 1 μL of each forward and reverse primer (10 μM), 2 μL of diluted DNA sample, 1 μL of probe (2.5 μM) and 5 μL of DNA/RNA free water. The amplification procedures, primers and probe for all the qPCR reactions are listed in Table S1. Each reaction was performed in triplicate. Standard plasmids containing target genes were constructed using a TOPO TA cloning® kit (Invitrogen, Thermo Fisher Scientific) for quantification as described in supporting information. Reactions without the DNA template served as negative controls. The absolute abundance of ARGs was calculated by using the concentration of ARGs obtained from the standard curve multiply by the dilution ratio and the total DNA volume extracted from the DNeasy PowerWater Kit, then divided by the total volume of the water sample.

2.4. Metagenomic sequencing and bioinformation analysis

The batch 2 of samples was used for shotgun metagenomic sequencing and analyses by following the flow diagram in Figure S1. Metagenomic sequencing was performed at GENEWIZ (Global Headquarters, NJ) using the Illumina HiSeq platform with 150 bp paired-end reads. The raw reads were trimmed and low-quality nucleotides and any read with ambiguous bases were discarded by GENEWIZ data processing pipeline. Clean reads were de novo assembled into contigs using CLC Genomics workbench (version 12.0, CLC Bio, Qiagen) with the following parameters: -min k: 65, -max k: 145, -step 10, -pre_correction. Assembled contigs longer than 500 bp were then used for annotation. The assembly statistics are listed in Table S2 and S3.

Taxonomic Profiler tool of CLC Genomics workbench was used for taxonomic assignment. Clean reads were mapped against complete genomes in the microbial genome database, and a read would be assigned with a taxon ID if a match was found with a minimum seed length of 30. The relative abundance (percentage) of each bacterial genus for each sample was calculated by using the number of reads belonging to the OTUs divided by the total number of reads in this sample. The ARGs in the assembled contigs were identified by BLASTn search against a local antibiotic resistance gene sequence database from ResFinder (Zankari et al., 2012). The parameters used for BLASTn search were ≥ 60% gene identity and 60% sequence length of the resistance gene with a cut-off E-value ≤ 10−5. In order to identify ARG potential hosts of the ARG-like sequences, those contigs containing ARG-like sequences were further taken for taxonomic annotation against the nucleotide collection (nr) database. The relative abundance of the detected antibiotic resistant classes was calculated by using RPKM (Reads Per Kilo base per Million mapped read) that normalizes the datasets based on the sample sequencing depth and gene length, which makes the abundance comparable between the samples. Cytoscape v_3.7.2, as an open source software platform, was used for visualizing complex networks between ARGs and potential hosts.

3. Result and Discussion

3.1. Occurrence of total bacteria in wastewater treatment processes

The bacterial load in wastewater samples was quantified by qPCR targeting 16S rRNA as Figure S2. The concentration of bacteria in primary effluent is the highest, and the concentration of bacteria in secondary effluent is 1-2 log lower, which is consistent with our previous report (Delanka-Pedige et al., 2019). The flocs of activated sludge could aggregate most bacteria in mixed liquor and be removed by settling in the sedimentation tank. In addition, some protozoa (e.g. ciliates) and metazoa (e.g. rotifers) could be preying on the dispersed bacteria, which leads to low bacterial concentration in the effluent(Mahmood and Elliott, 2006). Surprisingly, chlorination did not result in the reduction of total bacteria substantially (P value=0.069) in both batches although it is the commonly used process for disinfection. It is worth mentioning that the total bacteria results obtained from this study is based on the quantification of 16S rRNA via qPCR., which is not capable of distinguishing signals from live and dead cells. The preserved 16S rRNA genes in dead cells (Burkert et al., 2019) could cause interpretation bias, thereby underestimating the disinfection efficiency of chlorination.

3.2. Removal of ARB in wastewater samples and chlorine-treated water samples in the lab

Similar to the removal of the total bacteria (Figure S2), secondary treatment was effective in ARB removal for all the 5 types of resistant bacteria (Figure 1). There were 1.5 log, 2.1 log, 0.5 log, 1.0 log and 1.2 log removal observed after secondary treatment for ampicillin-, ciprofloxacin-, erythromycin-, tetracycline- and sulfamethoxazole resistant bacteria, respectively. However, contrary to the effective removal of secondary treatment, the disinfection process increased most types of ARB. There were 0.5 log, 1 log, 2 log and 0.1 log increase observed after disinfection for ampicillin-, ciprofloxacin-, tetracycline- and sulfamethoxazole- resistant bacteria, respectively. The results may be caused by increased horizontal gene transfer efficiency of ARGs during chlorine disinfection. Guo et al. pointed out that low chlorine CT value (40 mg free-chlorine·min/L) highly promoted the frequency of conjugative transfer by twofold to fivefold in E. coli. Moreover, the generated chloramine stimulated the transfer by improving the permeability of cell membrane (Guo et al., 2014). Zhang et al. (2017) demonstrated that subinhibitory concentrations which lower than minimum inhibitory concentrations of disinfectant can increase intergenera conjugative frequency by 1.4-2.3 folds. Therefore, chlorine disinfection condition applied in WWTPs may promote ARG transfer and ARB growth although it could reduce the total number of bacteria.

Figure 1.

Figure 1.

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The concentration of ARB (ampicillin-, ciprofloxacin-, tetracycline- and sulfamethoxazole-resistant bacteria) in primary effluent, secondary effluent, and disinfection effluent.

Our lab study further verified the inadequacy of chlorination on ARB reduction under various chlorine concentrations. Overall, the effect of chlorination depended on the dosage of chlorine disinfection, and NaClO concentrations of 2 mg/L and 8 mg/L resulted in higher concentrations of ampicillin-, ciprofloxacin-, tetracycline- and sulfamethoxazole-resistant bacteria compared to those ARB in secondary effluent (Figure 2). For these ARB except tetracycline-resistant bacteria, a downward trend in ARB concentration was only observed at higher doses of chlorine (> 8 mg/L) (P value=3.5 × 10−3, 6.7 × 10−17, 4.32 × 10−17 and 7.2 × 10−17 for ampicillin-, ciprofloxacin-, erythromycin- and sulfamethoxazole-resistant bacteria). Although the concentration of tetracycline-resistant bacteria in secondary effluent was low, they exhibited high tolerance to chlorination. Surprisingly, the concentration of tetracycline-resistant bacteria increased when NaClO dose increased from 2 to 16 mg/L (P value =7.1× 10−17). The removal of tetracycline-resistant bacteria was only observed at a chlorine dose of 32 mg/L. Another study also showed that >1.0 mg Cl2/L of chlorine with a 10 min contact time also increased the tetracycline resistance of E. coli (Huang et al., 2013). In contrast, the number of erythromycin-resistant bacteria decreased with the increase of NaClO concentration, indicating that erythromycin-resistant bacteria are more sensitive to chlorination.

Figure 2.

Figure 2.

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The concentration of ARB (ampicillin-, ciprofloxacin-, tetracycline- and sulfamethoxazole-resistant bacteria) in secondary effluent after disinfection with different concentration of NaClO (2 mg/L, 8 mg/L, 16 mg/L and 32 mg/L) in the lab.

3.3. Abundance of target ARGs measured by qPCR

To compare the antibiotic resistance across treatment units, the absolute abundance and relative abundance of twelve ARGs were quantified by real-time quantification PCR. Eight of twelve genes (blaTEM, intl1, tetW, qnrA, qnrB, qnrS, sul1, ermB) encoding five classes of antibiotic resistance were detected by qPCR in the samples with the absolute abundance from 143 to 2.32 × 107 copies/mL (Figure S3). Consistent with the data above, secondary treatment was responsible for the removal of the majority of ARGs, with little/no further reduction provided by disinfection.

The relative abundance of ARGs in remaining bacteria after treatment processes is presented by the ratio of copies number of ARGs and 16S rRNA (McCann et al., 2019), which can be used as an indicator of ARG transfer (Figure 3). The highest relative abundance of blaTEM, intl1, sul1, qnrA, qnrB, and qnrS gene were observed in secondary effluent for both batches, although secondary treatment has the best performance in reducing the absolute abundance of resistance genes. This result is consistent with previous work, suggesting a positive selection of resistant bacteria by secondary biological process (Voolaid et al., 2017). After disinfection the highest reduction was observed for genes encoding the class of quinolone resistance (qnrA, qnrB and qnrS). For ermB and tetW gene, the highest relative abundances were presented in the primary effluent and decreased with the treatment train. For ermB gene, the relative abundance decreased from 5.75 × 10−3 and 1.55 × 10−2 in primary treatment effluent (batch 1 and batch 2) to 1.99 × 10−3 and 1.46 × 10−3 (batch 1 and batch 2) in disinfection effluent. For tetW gene, the relative abundance decreased from 2.39 × 10−1 and 1.96 × 10−1 in primary treatment effluent (batch 1 and batch 2) to 5.05 × 10−2 and 1.09 × 10−2 (batch 1 and batch 2) in disinfection effluent. A similar pattern was observed in a previous study (Rodriguez-Mozaz et al., 2015), which reported the relative abundance of tetW and ermB gene decreased in secondary effluent while qnrS, blaTEM and sul1 gene had a higher relative abundance in the secondary effluent compared to influent. Several studies indicated that wastewater treatment processes, and in particular secondary treatment could promote the distribution of ARGs through horizontal gene transfer (Kruse and Sørum, 1994; Poté et al., 2003; Davies, 2012). Due to the proliferation and spread of ARGs in biological processes, high resistance even was observed in the final effluent after all treatment units (Munir et al., 2011). Moreover, high concentration of ARGs encoding almost all major classes of antibiotics and MGEs were detected in activated sludge (Su et al., 2015), which is a reservoir of genetic elements that could be dispersed horizontally to bacteria in the effluent.

Figure 3.

Figure 3.

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The relative abundance of ARGs (ARGs/16S rRNA) in primary effluent, secondary effluent, and disinfection effluent.

3.4. Occurrence and diversity of ARGs by metagenomic analysis

qPCR analyses only could focus on a limited number of ARGs and do not provide comprehensive and systematic information on the abundance and diversity of all ARGs in water samples. A sequencing depth of 25 Gb (Giga base pairs) was applied for each sample on Illumina HiSeq platform. The reads that longer than 150 bp and contigs information were listed in Table S2 and Table S3. Based on the HTS (high throughput sequencing) metagenomic analysis, a total of 124 ARGs subtypes were detected in three types of wastewater. Those ARGs can be grouped into 16 antibiotic resistant classes, including four types of resistance mechanisms, namely antibiotic deactivation, efflux pump, cellular protection, and transposase. The occurrences and diversity of antibiotic resistant classes varied across the treatment processes. As shown in Figure S4, the relative abundance of six classes of ARGs (aminoglycoside-, fluoroquinolone-, lincosamide-, macrolide-, beta-lactams-, and tetracycline-resistance) reduced remarkably after secondary treatment which indicates that secondary treatment can reduce the most types of antibiotic resistance in remaining bacteria. Unexpectedly, at least six classes of antibiotic resistance (aminoglycoside-, macrolide-, beta-lactams-, sulfonamide-, phenicol-, and tetracycline-resistance) in the secondary effluent increased with disinfection whereas three classes decreased slightly in abundance by disinfection. This mixed efficacy of disinfection highlights the differential effects that chlorination has on a wide array of antibiotic resistance genes identified in this metagenomics approach, with seven out of ten of the classes actually stimulated by disinfection. This is consistent with our lab results shown above which indicate the selective effects of applied chlorine concentration levels on a wide array of antibiotic resistance.

The dominating resistance classes were plotted in Pie chart (Figure 4) to show the distribution of each class. In primary effluent, beta-lactams resistance accounted for the majority of resistance, representing 32% of total resistance, followed by macrolide resistance (21%) and aminoglycoside resistance (13%). The high incidence of beta-lactams resistance in primary effluent is consistent with penicillin being the most commonly used antibiotics for human treatment or veterinary usage (Li et al., 2015). After secondary treatment, sulfonamide resistance increased dramatically to 22% while bata-lactmas resistance dropped to 6%. Macrolide and tetracycline resistance increased to 25% and 19% while they were 21% and 11% in secondary effluent. This remarkable change of resistance class distribution indicated that activated sludge process could selectively reduce the abundance of certain resistance classes, while the others accumulated after the process. Another study also demonstrated that activated sludge process increased erythromycin resistance under macrolide class (Guo et al., 2015). Unlike the secondary treatment, disinfection did not change the distribution of resistance classes substantially. Disinfection could further decrease the proportion of macrolide resistance and sulfonamide resistance to 19% and 17%, respectively. However, disinfection also promoted the proportion of bata-lactams and aminoglycoside resistance to some extent.

Figure 4.

Figure 4.

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The distribution of antibiotic resistance classes as percentage of reads per kilo base per million mapped reads (RPKM) in primary effluent (a), secondary effluent (b), and disinfection effluent (c); The shared and unique ARGs types in primary effluent, secondary effluent and disinfection effluent (d).

The dominance of macrolide resistance was observed in all three treatment stages, which may result from the presence of macrolide antibiotics in raw wastewater and inadequacy of wastewater treatment process in macrolides removal (Gulkowska et al., 2008). Significant correlations were found between the abundance of ARGs and the antibiotic residual in the effluents of WWTPs (Mao et al., 2015). There are studies reporting that macrolide resistance in certain bacterial strains can be induced by exposure to sub-inhibitory concentrations of erythromycin (Allen, 1977; Michael et al., 2013). Moreover, it has been reported that some macrolide resistance could be spread among bacteria by mobile genetic elements such as plasmids and transposons (Zhang et al., 2009), which may cause the amplification of macrolide resistance in secondary effluent. Tetracycline resistance is also commonly detected in the effluent of WWTPs, causing the spread of resistance into the aquatic environment (Laht et al., 2014). Three resistance mechanisms have been found for tetracycline resistance, including antibiotic efflux pumps, target modification, and antibiotic inactivation (Makowska et al., 2016). Those multiple resistance mechanisms may result in a broader resistance in the environment. Aminoglycoside resistance also had higher abundances in the wastewater according to the Jia et al. (2017)’s study, and their relative abundances increased in the receiving river caused by the treated wastewater discharge. Similarly, Gupta et al. reported that the abundance of aminoglycoside resistance in the effluent from WWTP was higher than influent which is consistent with our result (Gupta et al., 2018).

There were 72, 60 and 55 types of ARGs in primary effluent, secondary effluent, and disinfection effluent, respectively (Figure 4d), indicating that the number of ARGs subtypes decreasing along the treatment train. As shown in Figure4d, each treatment unit harbored its unique ARG types, and all three treatment units shared 20 types of ARGs, revealing some types of ARGs were persistent in the water even after disinfection. Primary effluent and secondary effluent shared 22 types of ARGs, and secondary effluent and disinfection effluent shared 37 types of ARGs, whereas primary effluent and disinfection effluent shared 24 types of ARGs, indicating that ARG composition shifted significantly along the treatment train. The presence of new ARG types after each treatment indicated that both secondary treatment and disinfection promote HGT or proliferation of ARGs.

The relative abundance (as RPKM) of ARGs ranged from 0.48 to 91.8 as shown in Figure S5. Overall, the gene aadA6, ant(3")-Ia (aminoglycoside resistance), tlr(C) (macrolide resistance), blaOXA-490, blaVIM-48 (beta-lactams resistance), srm(B) (pleuromutilin resistance), sul2-16, sul1-26 and sul1-31(sulfonamide resistance), oqxB (tetracycline resistance) were the ten most abundant ARGs across the three samples. Compared with primary effluent, the composition and relative abundance of ARGs in secondary and disinfection effluent varied greatly (Figure S5). Secondary treatment removed 45 ARGs and disinfection removed 24 ARGs. However, secondary treatment enhanced the abundance of ere(B), car(A), mphG genes which belong to macrolide resistance, tet(Q), tet(39) genes which belong to tetracycline resistance, sul2-16 gene which belongs to sulfonamide resistance, ant(3")-Ia, aadA7-1 and aadA2 genes which belong to aminoglycoside resistance. Some ARG subtypes which were not observed in primary effluent emerged after wastewater treatments (Figure S5). The emerging ARG types in the secondary effluent may come from the immigration of microbes accumulated in activated sludge in the aeration tank. Also, bacterial proliferation or ARG acquisition could bring about the emergence of underrepresented ARG subtypes in the primary effluent after treatments, especially from the biological treatment. After disinfection, the relative abundance of gene blaOXA-444 and blaSGM-5 genes which belong to beta-lactams resistance, sul1-2 and sul2-1 genes which belong to sulfonamide resistance, tet(G)-1 and tet(A)-4 genes which belong to tetracycline resistance, ole(C) and mph(G) genes which belong to macrolide resistance increased. This mixed efficacy of secondary treatment and disinfection on a wide array of antibiotic resistance genes were identified by using this metagenomics approach. This is consistent with our qPCR results shown above which indicate the selective effects of treatment processes on different antibiotic resistance genes. Specifically, the relative abundances trend of sul1, tetW, and ermB genes from qPCR and metagenomic analysis are consistent. However, there were discrepancies between qPCR and metagenome sequencing data. Generally, qPCR is more sensitive, but its results are impacted by the intrinsic differences of amplification efficiency between ARGs and 16S rRNA. Metagenome data largely rely on sequencing depth and some underrepresented ARGs may not be detected, resulting in the discrepancy between qPCR results and metagenome data.

3.5. Bacterial community composition

Based on the taxonomic analysis result, shifts in the bacterial community composition, bacterial richness and diversity were observed along with the treatment processes. In line with other reports (Ibarbalz et al., 2013; Kim et al., 2019; Zhang et al., 2019), Proteobacteria, Bacteroidetes, and Firmicutes were the most dominant bacteria across all three samples.

At the class level, 18, 30, and 24 classes could be classified in primary effluent, secondary effluent, and disinfection effluent, respectively (Figure S6). Shannon Wiener Diversity Index of the three samples was 1.72, 1.76, and 1.48, respectively. As the Shannon index decreases, both the richness and the evenness of the community decrease, indicating that disinfection process could reduce the richness and the evenness of microbial community. Furthermore, given that Shannon index is difficult to compare communities that differ greatly in richness, Simpson’s index was also used as the measure of dominance. Simpson’s index of the three samples were 0.23, 0.26 and 0.30 in primary effluent, secondary effluent, and disinfection effluent, respectively, verifying that disinfection effluent contained the lowest diversity. In primary effluent, the most abundant bacterial classes were Gammaproteobacteria (31.07%), Betaproteobacteria (30.40%), Epsilonproteobacteria (18.24%) and Bacteroidia (7.51%). After secondary treatment, Bataproteobacteria (46.04%), Flavobacteria (16.79%), Alphaproteobacteria (9.23%), and Gammaproteobacteria (9.04%) class were the most abundant bacterial classes. Gammaproteobacteria (43.14%), Betaproteobacteria (29.67%), Flavobacteria (12.79%) and Alphaproteobacteria (7.70%) were dominated in disinfection effluent. The result indicates that secondary treatment decreased the abundance of Gammaproteobacteria which is a class containing several medically vital groups of bacteria and even pathogens, such as the family of Enterobacteriaceae, Vibrionaceae and Pseudomonadaceae. Vibrionaceae family was not found in secondary effluent and Enterobacteriaceae and Pseudomonadaceae family remained very little after secondary effluent. Similarly, secondary treatment also significantly reduced Deltaproteobacteria, Bacteroidia, Epsilonproteobacteria which contain many species serving as potential pathogens (for example, Helicobacter spp.), Betaproteobacteria also consist of several groups of aerobic or facultative bacteria and pathogenic genus such as Neisseriaceae which is absent in secondary effluent and Burkholderia which reduced by secondary treatment. However, compared with secondary effluent, disinfection increase the abundance of Alphaproteobacteria, Betaproteobacteria, Gammaproteobacteria and Flavobacteria.

At the genus level, 121, 131, and 120 genera could be classified in primary effluent, secondary effluent, and disinfection effluent, respectively. Figure S7 showed the relative abundance of top 20 genera in the three samples. Shannon-Wiener diversity indices of those three samples at genus level were 3.10, 3.79, and 3.25, respectively. In primary effluent, Arcobacter, Pseudomonas, and Acinetobacter genera were the major genera with the relative abundance of 15.22%, 13.92%, and 10.74%, respectively. (Hrenovic et al., 2017). Some of species in the genus Arcobacter are considered as food and waterborne pathogens such as Arcobacter butzleri (Levican et al., 2016). It has been demonstrated that the presence of Arcobacter in water sources correlates with the presence of fecal pollution (Collado and Figueras, 2011). Due to the adaptability to various environmental stresses, the species of Pseudomonas are considered as one of the most widely distributed genera in the environment and Pseudomonas can acquire a wide range of antibiotics resistance (Luczkiewicz et al., 2015). Acinetobacter genus also has a remarkable ability to develop resistance to antibiotics. The specie of Acinetobacter baumannii accounts for most Acinetobacter infections in humans. In secondary effluent, although the relative abundance of Arcobacter decreased, Flavobacterium became the most dominating bacteria genus with the relative abundance of 11.46%. Several species of Flavobacterium are known to cause disease in freshwater fish. Several bacterial genera such as Nitrospira, Limnohabitans, Porphyrobacter, polynucleobacter and Sediminibacterium were abundant in secondary effluent, whereas they were barely present in primary effluent. Chlorination disinfection process could reduce the relative abundance of most genera. However, some putative pathogenic bacterial genera such as Pseudomonas and Stenotrophomonas have increased relative abundance in disinfection effluent, indicating the resistance of those bacteria to chlorine disinfection. The result indicated that secondary treatment could increase the diversity of bacterial community but decrease some type of potential pathogens, while disinfection could not reduce some potential pathogen genera such as Pseudomonas effectively and remaining bacteria may have higher pathogenicity and antibiotic resistance.

3.6. Correlation of ARGs and their potential hosts

Totally 457 contigs harboring ARGs were detected in all three samples, which belongs to 150 genera of bacteria. 48, 95 and 80 genera were identified as the potential ARG hosts in primary effluent, secondary effluent, and disinfection effluent, respectively (Table S5, Table S6, and Table S7). The corresponding Shannon-Wiener diversity index for the ARG hosts were 3.38, 4.34, and 3.93, respectively, indicating that secondary treatment could increase the bacterial host diversity and disinfection could lessen that slightly.

Figure 5 shows the number of bacterial genera that carrying the dominant ARGs. Among the 10 dominant ARG groups, compared to levels in primary stage treatment, 8 of the groups expanded their host range due to secondary or disinfection treatment. It was found that secondary treatment widened the host range of aadA, blaOXA, car(A), ole(B), ole(C), oqxB, and tlr(C) genes dramatically, and disinfection also expanded the host range of aadA, blaOXA, oqxB and srm(B) genes. Gene mef(B) and msr(D) only existed in 3 genera in primary effluent then were removed by secondary treatment. Disinfection reduced the number of hosts genera for car(A), ole(B), ole(C), and tlr(C) genes. These results indicate that secondary treatment spread ARGs among different bacteria genera probably due to the selective pressure from sub-inhibitory concentrations of antibiotic residues, as well as the high concentrations and diversity of microorganisms stimulated by in a nutrient rich environment as discussed by Pazda et al. (2019). Moreover, activated sludge in the secondary treatment system is a potential source of new bacterial genera carrying ARGs in the secondary effluent. The prevalence of ARGs in the sludge has been demonstrated in previous study and ARB in the sludge may go to the secondary effluent, resulting in the increased number of host genera (Calero-Cáceres et al., 2014). It’s worth noting that the macrolide resistant gene ole(B), ole(C), and tlr(C) harbored high number of bacterial host genera after secondary treatment. The genera Actinobacteria and Burkholderia that are putative pathogenic genera were found as the potential ARG hosts of these macrolide genes in secondary effluent.

Figure 5.

Figure 5.

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The number of dominant bacterial genera carrying ARGs in primary effluent, secondary effluent and disinfection effluent.

The network analysis was conducted to investigate the relationship between ARGs and microbial taxa (Figure S8-S10). Secondary treatment could broaden the range of bacterial hosts for ARGs substantially. Generally, macrolide resistance genes (car(A), ole(B), ole(C), and tlr(C)) are more likely harbored by a wide genera range of bacterial hosts, thereby promoting the ARGs spread between different taxa of bacteria. For instance, the result (Figure S5, S8, S9 and Table S4-S6) revealed that the ole(B) gene distributed in 6 genera of potential bacterial host in primary effluent while it was harbored by 18 genera of potential hosts after secondary treatment. Horizontal ARG transfer among different microbial taxa was also observed during disinfection processes. And aadA gene which belongs to beta-lactams resistance was harbored by 10 bacterial genera after secondary treatment while it co-occurred with 14 bacterial genera after disinfection.

Figure 6 shows the relative abundance (as RPKM) of dominating genera as potential ARG hosts in remaining bacteria across the three samples. The majority of these genera (11 of 21) were amplified in the secondary or disinfected samples compared with the primary effluent. In primary effluent, host Arcobactor and Pseudomonas have RPKM of 103.38 and 53.00 in the remaining bacteria, respectively. These two genera also are the most abundant two genera in the whole microbial community. In secondary effluent, genera Proteus, Shewanella, Laribacter, and Lysobacter were the dominant genera among potential ARG hosts which have RPKM of 28.00, 26.60, 24.32, and 10.94 in remaining bacteria, respectively. However, they were not dominant among all the bacteria, which indicates the change of the whole microbial community is not the main reason causing the changes of ARG-carrying genera. After disinfection, hostAeromonas, Sphingobium, Providencia, and Luteimonas dominated the effluent, with the relative abundance (as RPKM) of 55.65, 32.73, 25.06, and 24.05 in remaining bacteria. These results demonstrated that the significant shift of bacterial host genera capable of harboring ARGs along the treatment train. It was found that secondary treatment decreased the relative abundance of most bacterial hosts in primary effluent. However, the host range of bacteria genera was expanded after secondary treatment as 8 new potential hosts emerged including Shewanella, Laribacter, Lysobacter, Pseudoxanthomonas, Providencia, Sphingobium, Luteimonas and Thauera. Surprisingly, chlorine disinfection process also resulted in the increase of most bacterial host genera abundance (11 out of 20) from secondary treatment. Consistent with our results discussed above, both secondary effluent and chlorination apparently stimulated the spread of antibiotic resistance across bacterial genera possibly through HGT. Due to increased risks of ARG dissemination during conventional treatment processes, alternative treatment processes should be proposed to control antibiotic resistance in wastewater. One such alternate treatment technology is algal-based, in which we have demonstrated significant reductions in ARGs and ARG transmission (Cheng et al., 2019).

Figure 6.

Figure 6.

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The relative abundance as reads per kilo base per million mapped reads (RPKM) of dominating bacterial genera as the potential ARG hosts in primary effluent, secondary effluent and disinfection effluent.

Although HGT potential of ARGs was explored in this study, more work is needed to confirm the occurrence of conjugation, transformation or transduction. The analyses of mobile genetic elements including insertion sequences (IS), integration conjugative elements(ICE), transposons(Tn) and integrons (In) are needed to gain a better understanding of acquisition and spread of ARGs. An advanced bioinformatic tool such as MetaCHIP could be used to identify putative donor and recipient transfer events within a given community based on a combined similarity and phylogenetic incongruency approach (Song et al., 2019).

4. Conclusion

This study revealed the effects of secondary treatment and chlorination process in wastewater treatment plants on dissemination and distribution of ARGs and ARB and assessed the association of ARGs and their potential hosts in each treatment process by the network analysis. It was found that secondary treatment increased the relative abundance of some ARGs in the remaining bacteria, and it also amplified the diversity and range of ARG hosts. Unexpectedly, the effect of disinfection was mixed, with evidence of it reducing relative abundance of ARGs in remaining bacteria but broadening the host range of some ARGs. Moreover, the sub-inhibitory concentration of chlorine increased ARB number in disinfection effluent, which may be caused by enhanced horizontal ARG transfer during chlorination. Due to increased risks of ARG dissemination, more work needs to be done to reassess the existing wastewater treatment processes to reduce the environmental and health risks during wastewater management, reuse and disposal.

Supplementary Material

1

Highlights.

  • Chlorination with subinhibitory concentration increased ARB number

  • Secondary treatment increased the diversity of potential ARGs hosts

  • Secondary treatment spread ARGs among different bacteria genera

  • Chlorine reduced the relative abundance of ARGs in remaining bacteria

  • Chlorination broadened the host range of some ARGs

Acknowledgments

This work was supported by the National Institutes of Health Support of Competitive Research (SCORE) Pilot Project Award program (grant number 1SC2GM130432), College of Engineering at New Mexico State University.

Footnotes

Declaration of interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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