Effects of persulfate treatment on antibiotic resistance genes abundance and the bacterial community in secondary effluent

Accepted Manuscript Effects of persulfate treatment on antibiotic resistance genes abundance and the bacterial community in secondary effluent Jing-Feng Gao, Wan-Jun Duan, Wen-Zhi Zhang, Zhi-Long Wu PII: DOI: Reference:

S1385-8947(19)31245-8 https://doi.org/10.1016/j.cej.2019.05.221 CEJ 21860

To appear in:

Chemical Engineering Journal

Received Date: Revised Date: Accepted Date:

1 March 2019 14 May 2019 30 May 2019

Please cite this article as: J-F. Gao, W-J. Duan, W-Z. Zhang, Z-L. Wu, Effects of persulfate treatment on antibiotic resistance genes abundance and the bacterial community in secondary effluent, Chemical Engineering Journal (2019), doi: https://doi.org/10.1016/j.cej.2019.05.221

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Effects of persulfate treatment on antibiotic resistance genes abundance and the bacterial community in secondary effluent Jing-Feng Gao*, Wan-Jun Duan, Wen-Zhi Zhang, Zhi-Long Wu National Engineering Laboratory for Advanced Municipal Wastewater Treatment and Reuse Technology, Beijing University of Technology, Beijing 100124, China *Corresponding author: Dr. Jing-Feng Gao, E-mail: [email protected], Tel.: 0086-10-67392627; Fax: 0086-10-67391983. Abstract Secondary effluents as one of the main ‘hotspots’ of spreading antibiotic resistance genes (ARGs) into the environment have attracted considerable attentions. However, conventional disinfection methods may be inadequate for the successful removal of ARGs. Therefore, this study investigated the removal efficiency and the regeneration potential of ten ARGs in secondary effluent using sodium persulfate activated by Ginkgo biloba L. modified nanoscale zero-valent iron. Moreover, the role of bacterial community was not yet clear when ARGs were removed by persulfate treatment (PT). Therefore, quantitative PCR and Illumina MiSeq sequencing were applied for further exploration. The results depicted that 98.6% bacterial 16S rRNA gene was removed within 10 min. After 0.5 h PT, the removal efficiency of target ARGs decreased in the following order: Tn916/1545 = aac (below the limit of detection) > int I1 (99.99%) > tet E (99.64%) > mex F (99.10%) > tet W (94.57%) > qnr S (90.18%) > van G (82.21%) > bla-TEM (64.15%) > cat A1 (23.13%).

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Even upon after 48 h storage, ARGs abundances were stable. The influence of pH on ARGs removal efficiency was not significant. The results of heatmap and principal coordinate analysis depicted that the abundance of ARGs did not increase with the changes of community structure. This study revealed PT as an effective method could reduce abundance of ARGs. The ARGs carried by bacteria did not multiply in their hosts and pass on other bacterial populations further.

Keywords: Persulfate treatment; Ginkgo biloba L. modified nanoscale zero-valent iron; Secondary effluent; Antibiotic resistance genes; Bacterial community structure; Regeneration

1. Introduction Antibiotic resistant genes (ARGs) can be encoded in bacterial chromosomes or extrachromosomal plasmids, thereby triggering the biochemical defense mechanism of ARGs [1]. This mechanism allows bacteria to survive in the presence of corresponding antibiotic compounds, which may seriously impair the efficacy of antibiotics, posing threats to public health. ARGs are considered as an emerging environmental contaminant [2]. Additionally, wastewater treatment plants (WWTPs), one of the main ‘hotspots’ of spreading ARGs into the environment, have attracted considerable attentions [3]. Meanwhile, WWTPs are one of the main disinfection places to suppress the spreading of ARGs [3]. Therefore, the control of ARGs in WWTPs is an emerging challenge to deal with drug resistance in the worldwide. However, WWTPs are inadequate for the successful

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removal of ARGs, which are often identified in the WWTPs effluents indicating that these ARGs can be further disseminated in habitats downstream related to human activities [4]. In this regard, WWTPs link human activities and the environment [5]. Common disinfection technologies of WWTPs mainly include chlorination, ultraviolet (UV) processes, ozonation and homogeneous and heterogeneous photocatalysis [6]. Chlorination is a disinfection method to inactivate microorganisms. However, chlorine can form various by-products, which have higher toxicity than their parent compounds [7]. In comparison to chlorination, UV disinfection does not produce disinfection by-products [8]. However, analysis of the effect of UV/H2O2 process on ARGs transfer potential by Ferro et al. [9] showed that bla-TEM gene increase up to 3.7 × 103 copies mL-1 after 240 min treatment, and the removal efficiency of qnr S gene does not significantly change between the initial (5.1 × 104 copies mL-1) and the final sample (4.3 × 104 copies mL-1). For the synergistic effect of photocatalysis and ozonation, Sousa et al. [10] found that after a contact time of 30 min, a remarkable removal of 16S rRNA, int I1, and specific ARGs (bla-TEM, qnr S, van A and sul 1) are achieved, but all reach pre-treatment levels after three days storage except qnr S gene. In summary, these methods may be inadequate for the successful removal of ARGs. Therefore, novel disinfection and sterilization methods need to be established for effective removal of ARGs in WWTPs. Sodium persulfate (PS) has long bond length and low bond dissociation energy, which are 1.497·Å and 140 kJ·mol-1, respectively [11], [12]. PS is easily activated by catalyst to produce free radicals which have strong oxidation potentials for a wide range of pollutants including bacterial cell and DNA [1]. Few studies, however, have investigated the effects of persulfate treatment (PT) on removing ARGs in secondary effluents, except a study

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reported by Zhang et al. [13]. In Zhang’s study, the relative abundance of erythromycin-resistant genes has been investigated after heat-activated persulfate oxidation, however, little information is available about the regeneration capacity of ARGs (the forming again or continue generation of ARGs after an injury or interruption) and the relationships of ARGs with bacterial community and environmental factors. Therefore, using PT to remove ARGs needs to be further studied. PS can be activated by a variety of methods, such as heat, alkaline, activated carbon and metal ions. Comparatively, metal ions activation seems to be more practical. Heat [14] and alkaline [15] are effective ways to activate the PS. However, several issues needed to be addressed. Firstly, the heat activated PS requires a large amount of heat input, which makes it energy-consuming. Secondly, pH has to be adjusted to neutral condition for the treated water. Activated carbon as an adsorbent and catalyst has been widely investigated [16]. The modified methods, such as nitrogen doped carbonaceous materials and transition metal load onto carbon, have attracted more and more attention recently. Nevertheless, the energy consumption in the modification process is unavoidable. In recent years, the use of PS activation by nanoscale zero-valent iron (NZVI) to generate reactive oxidants was applied in the treatment of organic pollutants. The process can be described in the following reactions Eqs. (1-3) [17]. e0

-

S

e0 S

e

S

e

S

(1)

-

e -

-

-

S

-

(2) e

(3)

4

NZVI has large surface area, small particle size and high reactivity [18]. However, large surface area also brings defects to the application of NZVI. For example, NZVI particles are easily passivated and agglomerated. To overcome these weaknesses, particle stabilization materials with properties of safety, low-cost, prevention aggregation and enhancement the reactivity of NZVI are particularly attractive. Ginkgo biloba L. is one of the most ancient plants on earth [19] and has been widely used as a traditional medicine. The main components of Ginkgo biloba L. contain a wide variety of flavonoids (free flavonoids and flavonoid glycosides), terpene lactones, phenolic acids and proanthocyanidins [20]. According to previously published report that Ginkgo biloba L. can be selected as a stabilizer [21] to enhance the reactivity and dispersity of NZVI. It is noteworthy that no study has been reported to systematically study the PS activation by NZVI to remove ARGs, especially activation by modified NZVI. There are studies regarding bacterial community evolution and fate of ARGs. But previous studies mainly focused on the fate of ARGs in aerobic composting [22], freshwater aquaculture environment [23] and urban wastewater treatment plant [24]. Therefore, the relevant studies about effects of disinfection on ARGs abundance and the bacterial community have been generally overlooked. Until now, there has been not yet a study that investigates PT for the removal of ARGs, particularly for impacts on the bacterial community. Additionally, the relationships of ARGs with bacterial community and environmental factors are also not clear yet. In recent years, Illumina MiSeq sequencing as a next-generation sequencing technology was introduced, which could serve as an ideal technique to solve these problems.

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In this study, NZVI and nanoscale zero valent iron modified by Ginkgo biloba L. (G-NZVI) were synthesized as catalysts to activate PS. Bacterial 16S rRNA gene, eight ARGs and two mobile genetic elements were selected as representative target pollutants. As taxonomic markers of bacteria, 16S rRNA genes reveal the abundance of background bacteria [25]. Selected ARGs involve four resistance mechanisms, including efflux (tet E [26] and mex F [27]), deactivate (bla-TEM [26], cat A1 [28] and aac [29]), protection (tet W [26] and van G [29]) and unknown (qnr S [29]). Class 1 integron-integrase gene (int I1) and the conjugative transposon Tn916-Tn1545 family (Tn 916/1545) contribute exactly multiple antibiotic resistance' horizontal transfer among bacterial species due to their ability to capture and spread gene cassettes carrying ARGs [30]. The aims of this study were (1) to ascertain the potential of ARGs repair and regeneration after PT; (2) to investigate the effect of pH on removal efficiency of ARGs during the PT; (3) to explore shifts of bacterial community structure during PT; (4) to elucidate the relationships of ARGs with bacterial community and environmental factors. 2. Materials and methods 2.1. Chemicals Potassium borohydride (KBH4) was purchased from Sinopharm Chemical Reagent Co., Ltd. (Beijing, China). Ferrous sulphate (FeSO4·7H2O), PS (Na2S2O8, powder >98%) and sodium thiosulfate (Na2S2O3) were purchased from Macklin Biochemical Co., Ltd. (Shanghai, China). All the reagents were analytical grade and used without further purification. The Ginkgo biloba L. was collected from Beijing University of Technology. The pore membrane (0.

μm, Ø 50 mm) was purchased from Pall Corporation Co., Ltd.

(New York, America).

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2.2. Secondary effluent samples Gaobeidian WWTP, located in Beijing, primarily treats domestic wastewater with a mean flow of 1,000,000 m3/day and services a population of approximately 2,400,000 people [31]. Anaerobic/anoxic/aerobic treatment process is adopted. Overall, hydraulic retention time is 8-1  h, and sludge retention is 8-13 days. Secondary effluent samples were collected from the secondary clarifier, which were filled in 5 L sterilized polyethylene bottles and immediately transported to the laboratory. The concentrations of chemical oxygen demand (COD), ammonia nitrogen (NH3-N), and nitrate nitrogen (NO3--N) and secondary effluent pH were found to be 27.09 mg L-1, 0.17 mg L-1,16.11 mg L-1 and 7.25, respectively. 2.3. Preparation of NZVI and G-NZVI Deionized water (18.2·MΩ·cm) was used for solution preparation. To prepare 3 g NZVI, 108 mL KBH4 aqueous solution (1.5 M) was added dropwise to 108 mL FeSO4·7H2O (0.5 M) aqueous solution at ambient temperature. In order to ensure complete reduction of ferrous ions, KBH4 added was excessive according to Eq. (4). The synthetic NZVI was collected by a magnet, which was firstly cleaned with acetone, then cleaned with deionized water three times. e

-

e0

(4)

The preparation for G-NZVI has been reported in previous study [21]. Briefly, first, the extract of Ginkgo biloba L. was prepared as follows. 30 g Ginkgo biloba L. powder (0.3mm) was heated in 500 mL aqueous solution at 80 ℃ for 40 min with a constant

7

stirring. Second, 108 mL extract of Ginkgo biloba L. was added to 108 mL FeSO4·7H2O (0.5 M) aqueous solution. The mixed solution was continuously stirred at 250 revolution per minute (rpm) for 5 min with magnetic agitator (IKA, RCT basicS025, Germany). The precursor solution was obtained. Finally, 108 mL KBH4 solution (1.5 M) was added dropwise to precursor solution to form 3 g G-NZVI particles. The G-NZVI particles that formed in the solution were separated by a magnet, and cleaned with acetone, then cleaned with deionized water three times. 2.4. Experimental procedure 2.4.1. Bacterial 16S rRNA gene removal by PT under NZVI and G-NZVI catalysis Catalytic efficiencies of NZVI and G-NZVI for PS were investigated via the variation of bacterial 16S rRNA gene abundance. All the experiments were carried out at ambient temperature (22 ± 2 ℃). 0.25 g NZVI were transferred into two 250 mL beakers containing 100 mL secondary effluent, respectively, which were stirred 1 min at 400 rpm using a magnetic agitator to ensure full contact between material and bacterial 16S rRNA gene. Then 2 g PS were added to trigger oxidation, whose dosage was determined according to the proportion of iron and PS in the literatures [32], [33], the solutions were stopped at different predetermined time (1 and 2 h) by adding 4g Na2S2O3. After termination of reaction, 100 mL samples were immediately filtered with 0.

μm membranes and analyzed

for catalytic efficiency via real-time quantitative PCR (qPCR). At the same time, 100 mL secondary effluents were used to repeat the above experiments under G-NZVI as catalyst. The experimental condition was similar with NZVI catalysis. The bacterial 16S rRNA gene abundances were tested under different predetermined time (1 and 2 h). 2.4.2. Removal and regeneration of ARGs in the secondary effluent with PT

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The removal and regeneration experiments were conducted simultaneously to evaluate the effect of PT on ARGs. Therefore, compared with the Section 2.4.1, the reaction system and the dosage at this Section were doubled. 0.5 g G-NZVI was firstly introduced into the 200 mL secondary effluent solution, which was stirred at 400 rpm for 1 min. Subsequently, 4 g PS was transferred into the solution to start the reaction. In order to observe the variation of ARGs abundance along time, the reactions were quenched with 8 g Na2S2O3 at predetermined time (0.5 h, 1 h, 1.5 h, 2 h, 2.5 h and 3 h). After the reaction completed, half of the aqueous samples were used for the analysis of ARGs removal, which were filtrated through 0.

μm membranes immediately; the aqueous samples left were used for

regeneration analysis, which were filtrated through 0.

μm membranes after incubated 48

h. These membranes were stored in 5 mL centrifuge tube at -20 ℃ before DNA extraction for qPCR and Illumina MiSeq sequencing. To study the effect of pH, the solution pH was adjusted to desired values (3.41, 7.25 and 11.12) using 2 mol L-1 H2SO4 and NaOH solutions. 2.5. DNA extraction DNA was extracted using the Fast DNA® SPIN Kit for soil (Qiagen, CA, USA). The membranes of the filtered secondary effluent samples were cut into Lysing Matrix E tube directly for mechanical cell disruption and used for DNA extraction. The extraction procedure of DNA was carried out in compliance with manufacturer's protocol. The concentration of the extracted DNA was measured by using the Nanodrop-1000 (Thermo Fisher Scientific, USA). 2.6. qPCR analysis of ARGs

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qPCR assays were applied to quantify the abundances of bacterial 16S rRNA gene and ARGs in the samples. The abundance of bacterial 16S rRNA gene and ARGs in the samples were measured on the MX3005P Thermocycler (Agilent Technologies, USA) with BeyoFastTM SYBR Green qPCR Mix and specific primers (2X, Low ROX). All the samples were conducted in triplicates. The 0 μL reaction mixture consisted of µL template DNA, 7.2 µL aseptic water, 0.4 µL of each primer and 10 µL mix. The thermocycling protocol consisted of an initial phase 10 min at 95 ℃, followed by 40 cycles of denaturation (95 ℃, 30 s), primer annealing (temperatures are listed in Table 1) and extention (72 ℃, 30 s). Target genes classification, and operating parameters are listed in Table 1. Calibration curves were generated for qPCR by 10-fold serial dilutions of the plasmids carrying target genes. qPCR efficiencies were between 85.6 and 106.2% with correlation coefficients of 0.990-0.999, which were determined by comparing signals from serial dilutions of samples with the DNA standards. Table 1 2.7. Illumina MiSeq sequencing of bacterial 16S rRNA gene In order to study the evolution of bacterial community, samples DNA (raw water (RAW), PT for 0.5 h (T0.5), PT for 1 h (T1.0), PT for 1.5 h (T1.5), PT for 2 h (T2.0), PT for 2.5 h (T2.5) and PT for 3 h (T3.0)) were chosen to conduct Illumina MiSeq sequencing targeting hypervariable regions V3-V4 of bacterial 16S rRNA gene. In addition, it is important to understand the effect of incubation and pH on the evolution of the bacterial community, thus regenerated water without PT (RT0.0), regenerated water after PT for 3 h (RT3.0) and PT for 2 h (AT2.0) under acid conditions samples were selected simultaneously (PT for 2 h under alkaline conditions sample can not be amplified). The

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PCR products were purified with AxyPrep DNA Gel Extraction Kit (Axgen, USA), quantified using a QuantiFluorTM-ST (Promega, USA) and mixed to achieve equal mass concentrations in the final mixture. Finally, samples were sent out to Allwegene Tech Co., Ltd. in Beijing for DNA library constructed and run on Illumina MiSeq PE300 sequencer. The raw data obtained have been deposited in Sequence Read Archive (SRA) database (PRJNA524754). In order to obtain high quality sequences, the barcodes, primers, adapters and chimera were removed. Then, the sequences were further processed with Quantitative Insights Into Microbial Ecology (QIIME) pipeline package. Next, reads by normalization with the minimum number were clustered into operational taxonomic units (OTUs) based on 97% similarity. Then, diversity was calculated, and correlation matrix among bacterial genera was constructed. Finally, all the correlations were visualized. 2.8. Statistical analysis The correlation matrix of ARGs and the top 20 genera in each sample were analyzed using Pearson correlation methods. Differences at the p < 0.05 and p < 0.01 level (95% and 99% confidence interval) were considered slight and significant correlation. All the correlations were further visualized using Cytoscape software (version 3.0.2). Furthermore, variation partitioning analysis (VPA) was elucidated to quantify the contributions of operation parameters and bacterial community to removal of ARGs using R software 2.15. 3. Results and discussion 3.1. Effect of PT under different catalysts on bacterial 16S rRNA gene removal capacity and degradation kinetics

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To verify whether NZVI and G-NZVI were different to activate PS, bacterial 16S rRNA gene was used as a probe contaminant to explore the catalytic efficiency. The results are shown in Fig. 1(a), the abundance of bacterial 16S rRNA gene in the RAW was 3.868×105±2.647×104 copies·mL-1; 1 h later, the abundance decreased to 1.946×104±4.497×102 copies·mL-1 and 2.166×102±1.128×101 copies·mL-1 with NZVI and G-NZVI as catalyst, respectively. The removal efficiencies were 94.97% and 99.94%, respectively. At 2 h, the removal efficiency of bacterial 16S rRNA gene using NZVI as catalyst was 95.69%, which was higher than that in 1 h. By contrast, the variety of removal efficiency was not significant using G-NZVI as catalyst from 1 h to 2 h. However, bacterial 16S rRNA gene abundance using NZVI as catalyst was two orders of magnitude higher than that of G-NZVI as catalyst. The degradation results suggest that the catalyst efficiency of G-NZVI in PT was higher than that of NZVI. Fig. 1 When NZVI reacts with PS, NZVI is thought to be a slowrelease source of Fe2+ according to Eqs

e0

e

-

and

e0

e

-

[34],

[35]. Due to the large surface area and small size, NZVI has superior reactivity, which leads to easy passivation and agglomeration [36]. Moreover, the NZVI is easily reacted with oxygen. These oxide layers inhibit the oxidation sites activity on the NZVI surface and prevent the transfer of electrons from the surface of NZVI to PS. However, Ginkgo. biloba L. could prevent the formation of NZVI oxide and prolong the effective release time of Fe2+ [21]. The better performance of G-NZVI in comparison to bare NZVI could be attributed to interfacial coupling between NZVI and Ginkgo. biloba L., resulting in more Fe2+. The

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components of Ginkgo. biloba L, flavonoids and other phenolic compounds, contribute to the stabilization of the metal ion, which act as reducing and capping agents [21]. As shown in Fig. 1(b), high value of R2 (0.99) revealed that bacterial 16S rRNA gene removal by the PT was consistent with pseudo-second-order kinetic. The pseudo-second-order rate constant (k) was 3.5×10-5·mL·copies-1·min-1. PT using G-NZVI as catalyst quickly removed about 98.6% bacterial 16S rRNA gene in the first 10 min. The higher oxidative removal efficiency of bacterial 16S rRNA gene was achieved because more reactive oxygen species (ROS) were generated. The results indicated that G-NZVI exhibited good catalytic activity for PT, so it was selected for subsequent experiments. 3.2. ARGs removal by the PT with G-NZVI as catalyst Rizzo has raised a question that whether ARGs were resistant to disinfection methods [37]. Therefore 3 h was chosen as the reaction time to identify whether long-term effect of PS on the ARGs produced resistance risk. There was direct response of ARGs to PT, and the evolutions of the normalized concentration of ARGs are shown in Fig. 2. The removal equilibrium could be reached during 0.5 h. At this moment, about 23.13%, 64.15%, 82.21%, 90.18%, 94.57%, 99.10%, 99.64%, 99.99%, > 99.99% and > 99.99% (>99.99% means the abundances were below the detection limit) of cat A1, bla-TEM, van G, qnr S, tet W, mex F, tet E, int I1, Tn916/1545 and aac were removed by PT, respectively. The removal efficiency of tetracycline (tet E, tet W), integron-integrase (int I1), transposon (Tn916/1545) and aminoglycosides (aac) genes appeared to be higher than the others at 0.5 h. It indicated that the five ARGs might be attacked easily by the radical species than the others. During the next 2.5 h, it was gratifying to note that the abundances of all ARGs remained basically unchanged. Compared to 0.5 h, the removal efficiency of van G and cat A1 decreased 10.21%

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and 0.16% at 3 h, respectively. Meanwhile, the removal efficiency of the remaining eight ARGs increased less than 3%. These results revealed that the horizontal transfer capability of ARGs was eliminated successfully after the PT. The removal efficiency of target ARGs decreased in the following order Tn916/1545 = aac > int I1 > tet E > mex F > tet W > qnr S > van G > bla-TEM > cat A1. The rapid removal rate might be attributed to the following two reasons. The first reason is that PS is a strong oxidant, which could produce radicals under the activation of G-NZVI. The second reason is that radicals could induce cells lysis and oxidize ARGs [38]. Therefore, the full contact between radicals and ARGs made the high removal efficiency. The results depicted that PS activated by G-NZVI had inhibitory effect on the ARGs. Therefore, the removal efficiencies of ARGs were high, and using G-NZVI to activate PS to degrade ARGs in secondary effluent was promising. Fig. 2 3.3. Regenerative potential of ARGs after the PT It should be noted that all ARGs have regenerative potential. Therefore, it was essential to monitor the regeneration of ARGs incubated for 48 h after PT. Fig. 3 depicted the abundance after incubation at some points was higher than that without incubation. Regeneration led to Log (Ct´/C0) increased, while the abundances were still low (C0 representing the initial ARGs abundances (copies·mL-1), Ct´ representing the ARGs abundances after incubated 48 h (copies·mL-1)), indicating that the regeneration of ARGs could be neglected. For example, the increase of int I1 abundance (sample at PT for 1 h) was more obvious than others; the changes of Log (Ct/C0 (Ct'/C0)) of int I1 before and after incubation were -4.62 and -3.74, the removal efficiency decreased from 99.29% to 98.86%,

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however, the abundance of int I1 was still below the limit of detection. The results suggested that the regeneration of ARGs was suppressed by PT. Fig. 3 The influences of damage of microbial cells on the abundance of ARGs had been investigated. For instance, Zheng et al. [39] reported that mechanism for ARG-removal by sludge conditioning treatments is likely that the microbial cells are severely damaged. Bacterial hosts carrying ARGs are effectively damaged to attenuate most ARGs. Therefore, destruction of bacterial cell structures is a premise to reduce ARGs. The mechanism of bacterial damage and ARGs degradation may be attributed to the following points: disruption of amino acid biosynthesis, enzyme inactivation, as well as cellular dehydration. Radicals interact with cell surface and penetrate into the cell, then radicals act on specific target sites of ARGs [6], which could disrupt amino acid biosynthesis pathways and lead to enzyme inactivation [40] and ARGs removal; Other study reported [41] that hydrolysis of persulfate could yield hydroperoxide anion (HO2-) based on Eq. S

-

Eq. S

-

-

-

S

S

-

-

S

, which was further oxidized to superoxide (O2-·) based on -

-

. O2- could oxidize and release iron atoms in

mononuclear enzymes, which leads to misinstallation of the enzyme with zinc, additionally, zinc is not as efficient catalytically as iron [42], therefore PT led to bacterial damage and ARGs removal; Moreover, ROS (SO4-·, OH· and O2- ) destroy the catalytic [4Fe–4S] cluster of the dihydroxy-acid dehydratase enzymes, leading to cellular dehydration [43]. These results suggested that the PT may produce a more severe and permanent damage on

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ARGs, therefore ARGs abundances were stability after incubation 48 h. It is thought that the removal of ARGs were irreversible and resistance of ARGs to PT were not observed. 3.4. Effects of pH on the removal of ARGs As is known to all that pH plays a very important role in the degradation of ARGs [44]. To gain further insight into the effects of pH on the ARGs degradation, three pH (pH=3.41, pH=7.25, pH=11.12) were investigated over a reaction time of 1 h and 2 h, respectively. Ten ARGs were used as probe contaminants, among them Tn916/1545 and aac content were below the detection limit at three different pH. The results are shown in Fig. 4. At pH=3.41, with increasing reaction time from 1 h to 2 h, the removal efficiency (Log) of two ARGs decreased (cat A1 (-0.26 to -0.24) and van G (-0.23 to -0.18)), while that of five ARGs and int I1 increased (bla–TEM (-0.64 to -0.69), qnr S (-0.96 to -1.09), tet W (-1.20 to -1.39), mex F (-3.07 to -3.35), tet E (-3.02 to -3.32) and int I1 (-3.02 to ~)). At pH=7.25, the changes were described in Section 3.2. At pH=11.12, the range of Log (Ct/C0) of cat A1, bla–TEM, van G, qnr S, tet W, mex F and tet E (abundance of int I1 was below the limit of detection) varied from -0.36 to -0.24, -0.72 to -0.78, -0.11 to -0.13, -1.05 to -1.20, -1.61 to -1.70, -4.14 to -3.11 and -3.59 to-3.33, respectively. At different pH, the removal efficiencies of most ARGs were higher than 70% and the removal efficiency fluctuated slightly with the extension of reaction time, indicating that effect of pH on ARGs removal by PT was not significant. Fig. 4 At the same reaction time (1 h or 2 h), with pH increasing from 3.41 to 7.25, the removal efficiency of ARGs decreased slightly, while the removal efficiency increased slightly at pH 11.12 compared to that at pH 3.41. The range of removal efficiencies varied

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from 40.53% to >99.99% (pH=3.41), 22.51% to >99.99% (pH=7.25) and 55.76% to >99.99% (pH=11.12). These results confirmed that removal processes were independent on pH, but removal efficiency of ARGs at pH=7.25 was slightly lower than others. A bit higher removal efficiency at pH=3.41 than that at pH=7.25 was probably because that a lower pH could increase the production of sulfate radicals based on Eqs. (5,6) [45], [46], [47]. At pH=11.12, the highest removal efficiency was achieved. On the one hand a higher pH could inhibit side effects for the consumption of sulfate radicals based on Eq. (7) [48], [49], [50], on the other hand a higher pH would generate hydroxyl radical (OH·) with stronger oxidation capacity based on Eq. (8) [51]. However, the removal efficiencies of ARGs did not significantly change in the three pH, indicating that effect of pH on ARGs removal by PT was not significant. -

S S

S

-

-

S -

S

S

-

-

-

-

S -

S S

(5) (6) -

(7)

-

(8)

The results depicted that the ARGs were effectively decomposed at a broad pH range of 3.41-11.12. The effects of pH on removal of ARGs were ranked in the following order pH=11.12 > pH=3.41 > pH=7.25. The differences were less than 20%, except the van G (50.80%) and cat A1 (34.52%) gene. Therefore, in order to save cost, reduce environmental pollution and ensure high removal efficiency, practical application of PT to remove ARGs was recommended in neutral condition. 3.5. Relationship between bacterial community and ARGs during the process of PT

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3.5.1. Bacterial community diversity and composition In order to study the changes of bacterial community diversity, 10 samples (RAW, T0.5, T1.0, T1.5, T2.0, T2.5, T3.0, RT0.0, RT3.0 and AT2.0) were selected to Illumina MiSeq sequencing of bacterial 16S rRNA gene. The results are shown in Table 2. After the chimeras were removed, the number of effective sequences varied from 49412 (T2.0) to 124094 (RAW). The Good’s coverage values (0.9 -0.99) indicated that the microbial sequencing met the requirements of analysis. A total of 13403 OTUs were clustered at 97% similarity level. The 4294, 3344, 3963, 3436, 3490, 2591, 3004, 3113, 1357, 2838 OTUs number were obtained from the RAW, RT0.0, T0.5, T1.0, T1.5, T2.0, T2.5, T3.0, RT3.0, AT2.0 samples, respectively. Table 2 The Shannon index used to estimate the community diversity ranged from 3.77 to 8.56 in different samples. Compared with other samples, RAW and RT0 had the higher diversity (7.99 and 8.56), indicating that oxidation process decreased the biodiversity. The Shannon index at AT2.0 (4.55) was comparable with that at T2.0 (4.36), suggesting the slight effect of acid condition on bacterial diversity. In order to understand the shifts in bacterial community structure with extension of reaction time at different pH, principal coordinate analysis (PCoA) at genus level was conducted. Principal component 1 (PCo1) and 2 (PCo2) explained 83.46% and 9.59% of the variances in the bacterial community, respectively. The results as depicted in Fig. 5(a). The ten points were divided into three clusters. The first cluster was RAW and RT0.0, the second cluster was T0.5 and T1.0, the remaining points fell into the third group, which demonstrated that the points in each cluster had similar bacterial community composition.

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The degree of dispersion on the PCo1 or PCo2 between the RAW and RT0.0 were higher than T3.0 and RT3.0. The results indicated that after 48 h incubation, the bacterial community of the untreated samples were more variance than that of the treated samples. This may explain that free radicals may be a driving force to reduce bacterial community variance. Although the bacterial community had different location under different pH (AT2.0 and T2.0), the bacterial community at AT2.0 and T2.0 clustered together, which indicated that initial pH had no obvious effect on the bacterial community. The ARGs degradation curve (Fig. 2) showed that the abundances of ARGs were removed within 0.5 h, but it was known from the PCoA map that the community structure was basically unchanged after reaction time of 1.5 h. This indicated that bacterial community in the second and third cluster didn’t carry ARGs as potential hosts, and removal of the ARGs were irreversible. Fig. 5 Venn diagram at genus level was further applied to investigate the differences in the bacterial community composition during PT. As shown in Fig. 5(b), the unique OTUs in RAW, T1.0, T2.0, and T3.0 were 2449, 1088, 760 and 1172; accounting for 57.06%, 31.68%, 29.35%, 37.67% of the total OTUs, respectively. The mechanism related to the ferrous/persulfate oxidation had controlled the survival ability of unique OTUs [52]. 1389, 923 and 1045 OTUs were shared between the RAW and T1.0, T2.0, T3.0, respectively. During 0-2h, numbers of shared OTUs depicted a downward trend, after 2 h, that OTUs depicted an upward trend. This can be explained as bacteria had a direct action to radicals, but some bacteria could recover, even if there was external selective pressure. The regeneration of bacteria after inactivation was also reported in literatures [53], [54]. In the

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later stage, numbers of shared OTUs increased, whereas abundance of ARGs did not increase, indicating that the ARGs carried by bacteria did not multiply in their hosts and pass on other bacterial populations further. Samples from different reaction time shared a small number of OTUs (532 OTUs) in common, which accounted for 6.2% of the total OTUs, indicating that most of the OTUs were different among samples. During the PT, the bacterial community structure changed significantly. The relative abundance of bacterial phyla in all samples are shown in Fig. 6(a). In RAW, Proteobacteria, Bacteroidetes and Firmicutes were the three most dominant phylum, and the relative abundance of them was 62.27%, 10.01% and 8.82%, respectively. With extension of reaction time, abundance of Proteobacteria and Bacteroidetes decreased to 14.03% and 3.09% at the T3.0, respectively. The abundance of Firmicutes in the RAW was less than 10%, but increased significantly to 76.50% at the T3.0. Previous study [55] showed that after soil drying, gram-negative species that are terrestrial decrease in abundance, whereas gram-positives increase, which is attributable to a peptidoglycan layer of gram-positive species possess with resistance to environmental hazard. Most of Firmicutes is the gram-positive, which might resist oxidation of radicals and relative abundance increased, but most of Proteobacteria and Bacteroidetes are the gram-negative, so the relative abundance decreased. However, there was no significant difference in community composition between T2.0 and AT2.0. The above experimental results depicted that PT changed bacterial community composition, but influence of pH on it was not significant. Fig. 6

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In order to investigate the effect of PT on bacterial community structure, heatmap analysis was carried out. The similarity of bacterial community structure could be determined by the hierarchically clustered heatmap analysis at genus level. If the clustering occurred among the different samples, there were no substantial variations among the dominant bacteria during PT, indicating bacterial communities exhibited the similarity. Distribution heatmap of genus-level bacterial communities at initial stage, removal stage and regeneration stage are shown in Fig. 6(b), respectively. The microbial community structure in RAW and RT0.0 had similarity, which were much different from those in other stages. The microbial community structure was dramatically affected by PT. The bacterial community mainly shifted from Nitrospira, Dechloromonas, Terrimonas, Ferruginibacter, Rhodobacter, Haliangium, Hydrogenophaga, Fodinicola, Thauera, Trichococcus, Arcobacter and Methylophilus to Lactobacillus, Brevibacillus, Enterobacter, Escherichia-Shigella, Mucispirillum, acinetobacter and odoribacter. It is worth noting that Arcobacter genus appeared in RAW and accounted for a high proportion (0.91%), which is considered a foodborne emergent pathogen [56]. But it was gratifying that the proportion of Arcobacter genus was decreased to 0.01% at T2.0. This phenomenon may be due to the PT. The abundance of bacteria with bioremediation function increased after PT. Literatures reported that Lactobacillus, Enterobacter and Methylobacterium could degrade dye [57], chromate [58] and trihalomethanes [59]. Overall, relative abundance of potential pathogen decreased, and the functional bacteria were found as the dominant microorganisms. Meanwhile, bacteria community structure did not differ when comparing between T2.0 and AT2.0, which demonstrated pH had a slight influence on bacteria community structure.

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In order to study the relationship between ARGs and bacteria, the Pearson analysis was applied to investigate the correlation between abundance of the ARGs and the top 20 genera in each sample during the reaction process. The genera associated with ARGs were obtained and the results were marked in a heatmap (Fig. 6(b)). Here, the significantly positive correlations were focused. In RAW and RT0.0, Dechloromonas, Zoogloea, Flavobacterium, Hydrogenophaga and Thauera were positively correlated with ARGs. These bacteria have been reported as hosts of antibiotics and ARGs. For example, Dechloromonas is a potential host of multiple antibiotic resistance types [60]; after the sulfamethoxazole was introduced into wastewater, Hydrogenophaga, Acinetobacter and Zoogloea become the three most abundant genera [61]; Flavobacterium could produce extended-spectrum β-lactamases, which makes it have multidrug resistance [62]; Hydrogenophaga and Prevotella belong to Proteobacteria and Bacteroidetes, respectively, which are positively correlated with all ARGs [63]; Thauera could be host bacteria of bacitracin resistance genes and might cause prevalence [64]. But abundances of genera that had significant positive correlations with ARGs decreased during PT. Additionally, in T2.5 and T3.0, genera with high abundance had negative correlations with ARGs. Therefore, it could be concluded that PT was beneficial to the removal of ARGs and the ARGs carried by bacteria did not multiply in their hosts and pass on other bacterial populations further. 3.5.2. Relationships between environmental factors, genera and ARGs To reveal whether environmental factors and genera were potential influence factors of ARGs existence, the co-occurrence patterns between them were investigated though the network analysis. As shown in Fig. 7(a), there were many strong correlations between the genera and ARGs. For example, Hyphomicrobium, Nitrosomonas, Dokdonella, Haliangium,

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Rhodobacter, Sphaerotilus, Arcobacter, Aeromonas and Nannocystis were positive correlated with ARGs; on the contrary, Bacillus, Mycobacterium and Intestinibacter were negative correlated with ARGs. The heatmap depicted that genera positively related to ARGs were dominant in RAW and RT0.0, genera negatively related to ARGs were enriched at the removal stage. Additionally, from RAW to T3.0, abundances of ARGs were reduced. These results depicted that PT could reduce most of ARGs. The ARGs did not multiply in tolerant genera after PT. Fig. 7 In this study, genes might carry the five resistance mechanisms, including efflux (tet E and mex F), deactivate (aac, bla-TEM and cat A1), protection (tet W and van G), unknown (qnr S) and mobile genetic elements (int I1 and Tn916/1545). Positive correlations were observed between the most of bacterial genera and ARGs of various mechanisms, indicating that ARGs-genera co-occurring event was influenced by a hosting relationship and existed across ARGs mechanisms. Moreover, the environmental factor, reaction time, was positively correlated with ARGs. Therefore, the increase in reaction time may be beneficial for the removal of ARGs. However, the other environmental factors, incubation time and pH, were not significant correlated with ARGs. These results mean that PT did not result in ARGs regeneration even incubated 48 h, and no significant effects on ARGs removal were observed at the tested pH levels. These results were in accordance with section 3.3 and 3.4. To differentiate the effects of the bacterial community and operation parameters on the removal of ARGs, VPA was applied. The total variations were explained by 14 genera with obvious proportion changes, time, pH, interactions between them and the unexplained

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portion (Fig. 7(b)). The results depicted that a total of 43.12% of the removal of ARGs could be attributed to that no horizontal gene transfer or other process made number of ARGs-carrying bacteria increases. Moreover, contribution of genera was much higher than that of the time (1.09%) and pH (1.05%), suggesting that genera was the crucial factor affecting the removal of ARGs during the PT. Noticeably, except contribution of genera, genera and time together contributed 47.44% of the total variance, indicating that there were considerable interactions between the two parameters. The effect of time on the ARG removal was reflected in the joint effects with genera. This is understandable because genera responded to radicals, and genera carrying ARGs were removed with the continued action of selective pressure. Whereas, the explanation data of the interactions between the time and pH were low (0.25%), showing less influence than the individual component. Additionally, the explanation data of the interactions among three major components were low (1.65%). However, 5.40% of ARGs removal could not be explained by the above components. These results suggest that some additional factors may shape removal of ARGs in the process of PT. 4. Conclusions Different catalyst performance (NZVI and G-NZVI) were evaluated by bacterial 16S rRNA gene abundance variation. The results depicted that the catalyst efficiency of G-NZVI in PT was higher than those of NZVI. The removal of bacterial 16S rRNA gene using G-NZVI as catalyst was found to obey a pseudo-second-order kinetic and k value reached 3.5×10-5·mL·copies-1·min-1. The removal efficiency of target ARGs decreased in the following order: Tn916/1545 (>99.99%) = aac (>99.99%) > int I1 (99.99%) > tet E (99.64%) > mex F (99.10%) > tet W (94.57%) > qnr S (90.18%) > van G (82.21%) >

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bla-TEM (64.15%) > cat A1 (23.13%). The activation of persulfate by G-NZVI could effectively remove the bacterial 16S rRNA gene and ARGs in the secondary effluent. The removal of ARGs were irreversible and ARGs did not produce resistance to PT. A new insight into the response of ARGs of resistance to PT was provided. During the PT, the bacterial community structure changed significantly even though the ARGs abundance remained stable. The Firmicutes as the gram-positive could resist oxidation of radicals and increase the relative abundance. Overall, relative abundances of potential pathogens decreased and the functional bacteria showed a dominant microorganism after PT. Additionally, the ARGs-genera co-occurring event was influenced by a hosting relationship and observed regardless of the mechanisms. Overall, useful information of ARGs removal by PT was supplied. Acknowledgements We would like to thank the NSFC (51578015), National Science and Technology Major Project (2017ZX07103-003) and Beijing Municipal Science and Technology Project (Z181100005518002) for the financial supports of this study.

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Figure captions: Fig. 1

Catalytic efficiency and degradation kinetics on the bacterial 16S rRNA gene

removal. (a) Degradation of bacterial 16S rRNA gene by NZVI and G-NZVI as catalyst in the persulfate system. (b) Abundance changes of bacteria 16S rRNA gene by PT under G-NZVI catalysis and fitting of the experimental data to the pseudo-second-order kinetic model, where, C0 and Ct representing the initial and ARGs abundances after treatment (copies·mL-1); t representing the reaction time. Fig. 2

Removal of ARGs with PT.

Fig. 3

Comparison of normalized flux of ARGs abundances after incubation 48 h (line

and scatter) and no incubation (bar). The normalized flux Log (Ct/C0(Ct´/C0)) as a function of time was used to determine the inactivation performance, with Ct´ representing the ARGs abundances after incubated 48 h (copies·mL-1). Fig. 4

ARGs degradation trend by PT under different pH.

Fig. 5

Bacterial community similarity of structure. (a) A principal coordinate analysis

(PCoA) based on the Weighted UniFrac of 16S rRNA gene-amplicon sequences of the samples under different degradation time. (b) Venn diagram showing the number of shared bacterial OTUs among samples. Fig. 6

Bacterial community composition. (a) Relative abundance of bacterial

communities at the phylum level. (b) Distribution profiles for the top 20 genera in each sample during the degradation process. Genera that were significantly positive (p < 0.01) associated with ARGs were marked with yellow rectangle frame in heatmap. Genera that were positive (p < 0.05) associated with ARGs were marked with blue rectangle frame in

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heatmap. Genera that were negative associated with ARGs were marked with red rectangle frame in heatmap. Fig. 7

(a) Network analysis showing the relationships among ARGs, bacteria and

environmental factors. Solid and dotted lines represent the positive and negative correlations, respectively. (b) Variation partitioning analysis.

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40

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Table 1

Specific primers used for qPCR analysis Annealin

Resistan Efficie

Gene

g Primers

Sequences (5´-3´)

ce ncy

Classificati

Tempera

Mechan (%)

on

ture

ism

Uni1055ATGGCTGTCGTCAGCT F 1392-R

ACGGGCGGTGTGTAC

int I1-F

CCTCCCGCACGATGATC

55

106.2

55

99.5

16S rRNA

Class 1

int I1-R

TCCACGCATCGTCAGGC

integron

Tn916/15

TCCTACAGCGACAGCCAGT

45-F

GA

Transposon 55

Tn916/15

TGCGTTGCTTTGGTCTGCT

45-R

GGT

Bla-TEM-

AGCATCTTACGGATGGCAT

F

GA

Bla-TEM-

TCCTCCGATCGTTGTCAGA

R

AGT

85.7

deactiva 60

100.4

Beta-lactam te

GGGTGAGTTTCACCAGTTT

Florfenicol,

cat A1-F TGATT

chloramphe

deactiva

CACCTTGTCGCCTTGCGTA

nicol and

te

TA

amphenicol

60

87.7

cat A1-R

45

(FCA) TTGGCGCTGTATGCAATGA tet E-F T 60

85.9

Tetracycline efflux

55

86.2

Tetracycline

CGACGACCTATGCGATCTG tet E-R A ATGAACATTCCCACCGTTA tet W-F TCTTT

protecti

ATATCGGCGGAGAGCTTAT

on

tet W-R CC ATTTGAATTGGCAGGTATA van G-F CAGGTTA 53

Vancomyci

protecti

n

on

efflux

97.8

TGATTTGTCTTTGTCCATA van G-R CATAA TGC mexF1-F

CCGCGAGAAGGCCAAGA

mexF1-R

TTGAGTTCGGCGGTGATGA

58

91.3

FCA

55

85.6

Quinolone

AAACACCTCGACTTAAGTC qnr S-F T

unknow

GTGAGTAATCGTATGTACT

n

qnr S-R TTTGC aac-F

CCCTGCGTTGTGGCTATGT 59

aac-R

TTGGCCACGCCAATCC

46

Aminoglyc

deactiva

oside

te

88.8

Table 2

α-diversity indices of 10 samples (RAW, T0.5, T1.0, T1.5, T2.0, T2.5, T3.0,

RT0.0, RT3.0 and AT2.0) that were selected from the PT

Sequence Samples

OTUs

Chao1

Shannon

Coverage

number RAW

124094

4294

7531.19

7.99

0.96

RT0

100295

3344

5518.09

8.56

0.97

T0.5

83219

3964

7597.08

6.17

0.96

T1

74012

3436

6814.01

5.52

0.96

T1.5

76090

3490

6460.44

5.81

0.96

T2

49412

2591

4895.68

4.36

0.97

T2.5

91547

3004

6017.29

4.88

0.96

T3

75513

3113

6038.85

4.59

0.96

RT3

69867

1357

2187.32

3.77

0.99

AT2

71052

2838

5657.10

4.55

0.97

47

Highlights 

Most of ARGs removal efficiency by PT exceeded 82.21% at 30 min under inherent pH.



Regeneration of ARGs was negligible and resistance of ARGs to PT was not observed.



The difference of ARGs removal efficiency under different pH were less than 20%.



The community structure changed even if ARGs abundance remained stable during PT.



ARGs-genera co-occurring patterns were observed regardless of the mechanisms.

48

Graphical abstract

49