Fitness and Recovery of Bacterial Communities and Antibiotic

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Fitness and recovery of bacterial communities and antibiotic resistance genes in urban wastewaters exposed to classical disinfection treatments Andrea Di Cesare, Diego Fontaneto, Julia Doppelbauer, and Gianluca Corno Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b02268 • Publication Date (Web): 22 Aug 2016 Downloaded from http://pubs.acs.org on August 23, 2016

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Environmental Science & Technology

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Fitness and recovery of bacterial communities and antibiotic resistance genes

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in urban wastewaters exposed to classical disinfection treatments

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Andrea Di Cesare, Diego Fontaneto, Julia Doppelbauer, Gianluca Corno*

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Microbial Ecology Group, National Research Council of Italy, Institute of Ecosystem

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Study - Largo Tonolli 50, 28922 Verbania (Italy)

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* Corresponding author:

Gianluca Corno

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email: [email protected]

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tel: +390323518321 fax: +390323556513

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ACS Paragon Plus Environment


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Abstract

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Antibiotic resistance genes (ARGs) are increasingly appreciated to be important as

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micro-pollutants. Indirectly produced by human activities, they are released into the

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environment, as they are untargeted by conventional wastewater treatments. In

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order to understand the fate of ARGs and of other resistant forms (e.g. phenotypical

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adaptations) in urban wastewater treatment plants (WWTPs), we monitored three

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WWTPs with different disinfection processes (chlorine, peracetic acid (PAA), and

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ultraviolet light (UV)). We monitored WWTPs influx and pre- and post-disinfection

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effluent over 24 hours, followed by incubation experiments lasting for 96 hours. We

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measured bacterial abundance, size distribution and aggregational behavior, the

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proportion of intact (active) cells, and the abundances of four ARGs and of the

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mobile element integron1. While all the pre-disinfection treatments of all WWTPs

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removed the majority of bacteria and of associated ARGs, of the disinfection

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processes only PAA efficiently removed bacterial cells. However, the stress imposed

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by PAA selected for bacterial aggregates and, similarly to chlorine, stimulated the

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selection of ARGs during the incubation experiment. This suggests disinfections

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based on chemically aggressive destruction of bacterial cell structures can promote

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a residual microbial community that is more resistant to antibiotics and, given the

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altered aggregational behavior, to competitive stress in nature.

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Introduction

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Wastewater treatment plants (WWTPs) are designed to remove nutrients (e.g. C, P,

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and N) and to reduce pathogen loads, but while they can eliminate some

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pharmaceuticals (e.g. paracetamol) and some antimicrobials (e.g. zwitterionic

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fluoroquinolones), they do not target the majority of antibiotics, which are thus not

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removed1. Furthermore, the generally applied technology in wastewater treatments

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does not target emergent contaminants, such as antibiotic resistant bacteria (ARB)

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or antibiotic resistance genes (ARGs)2.

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Bacterial communities are active and highly dense in activated sludge and in water

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during the biological treatment, where spatial proximity between bacterial cells is

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enhanced and nutrients and antibiotics concentrated, leading to ideal conditions for

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the spread of ARB and ARGs3,4. Concomitancy between reduced removal and

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favorable ecological conditions makes WWTPs potential reservoirs of ARB3,5,6 and

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hot-spots for the spread of ARGs to rivers and lakes7-9. Urban WWTPs generally

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comprise successive treatments (mechanical, biological, chemical), and each of

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them can indirectly influence the fate of ARB and ARGs3.

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Because the emergence of antibiotic resistance is a compelling health problem and

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because of the increasing reuse of treated wastewaters (e.g. in agriculture) due to

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water scarcity, the presence of ARB and ARGs in WWTP effluents has become a

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major public concern10, 11.

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Currently, a final disinfection is generally applied to reduce the overall indicator

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bacterial numbers (i.e. Escherichia coli) in WWTP effluents. Between the different

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technologies implemented for this treatment phase, chlorination is the most

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commonly used, but also has the highest secondary impact on the environment12.

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Chlorine is introduced in wastewater and it persists mainly as HOCl and OCl- (ratio



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depending on the pH, summarized by Sharma and coworkers13). HOCl reacts with

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superoxide anions, producing ‧OH that destroys bacterial cell structures by

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oxidation14. The process of chlorination generates harmful disinfection byproducts

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released into the environment with the effluents15, and HOCl that can react with NH3

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producing monochloramine, affecting the overall removal of bacteria and, indirectly,

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of ARGs2. Less severe peracetic acid (PAA) disinfection treatment was developed in

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the early ‘80s, and it is now considered an efficient substitute for chlorination16. The

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mechanism of action of PAA is based on the production of “active” oxygen or

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reactive oxygen species (summarized by Gehr and colleagues16) damaging the

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bacterial cell structures. Another alternative disinfection treatment is irradiation of

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wastewaters with UV, which damages dsDNA and, in turn, bacterial cells11. Unlike

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chemical disinfections, UV treatment leaves no residues in the effluent.

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A very limited number of studies have evaluated the efficiency of disinfection

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treatments on removal or deactivation of ARGs, and none of them considers the

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multi-species bacterial community13. The scarce data available suggest a potential

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relationship between the abundance of introduced disinfectant (i.e. chlorine), the

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contact time, and ARG removal efficiency2. Furthermore, Guo and colleagues5

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demonstrated that low doses of chlorine promote bacterial horizontal gene transfer

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(HGT) and thus the potential spread of antibiotic resistance. Conversely, high

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chlorine concentrations or high UV radiations extensively suppress HGT.

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In this study we present the first, to our knowledge, comprehensive research on the

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response of the resident microbial community in terms of number, fitness, and

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antibiotic resistance spread in relation to different disinfection technologies that are

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commonly applied worldwide (chlorine, PAA, and UV disinfection). The experimental

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setup has been designed to reproduce conditions as close as possible to those of


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the environments in which the WWTP effluents are released. The residual effect of

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the disinfections on the released bacterial communities was evaluated by performing

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96h-long batch experiments in which the fate of four ARGs of particular importance

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for the evaluation of the communities resistome, and of the class 1 integrase gene

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(int1), was determined. The four selected ARGs each represent either abundant

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genes detected in the long-term ARGs monitoring of Lake Maggiore, where the

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effluents of two of the three WWTPs used for this study are released17, or genes

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found to be particularly important in terms of absolute abundance in a previous study

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on the same WWTPs18. In detail, we assessed the relative proportion of the gene

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tetA (against tetracycline), that can be transferred by HGT and it is commonly found

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in environmental and clinical bacterial communities19. The gene ermB (against

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macrolides), one of the most common erythromycin resistance genes in the

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environment, that has been recently found on a conjugative plasmid and co-

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transferred together with a toxin-antitoxin system from an environmental strain to

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Enterococcus faecalis JH22 by in vitro conjugation20. The gene blaTEM, against β-

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lactams is abundant in many metagenomes from human and environmental

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microbiomes21, and it can be co-transferred with integrons in multi-drug resistant

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enterobacteriaceae22. The gene qnrS, one the most common quinolone plasmid

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mediated resistance genes23, found on conjugative plasmids in strains isolated from

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animals24 and from waters25. The gene int1 has been also quantified and tested as

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potential proxy of antibiotic resistance in the three WWTPs.

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Materials and methods

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WWTPs sampling



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Samples (1L in DNA free bottles) were collected from the urban WWTPs of Verbania

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(VB; 45°55’54” N, 8°33’57” E; 51000 Population Equivalent, PE), Novara (NO;

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45°25’30” N, 8°35’28” E; 110000 PE), and Cannobio (CN; 46°04’00” N, 8°42’11” E;

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15000 PE) in Piedmont (Italy). The three WWTPs are characterized by different

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treatment designs (detailed description of the WWTPs is available in Di Cesare et

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al., 2016). Moreover, each WWTP applies a particular disinfection technology:

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chlorination in VB, PAA treatment in CN, and UV radiation in NO.

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In VB, chlorination is carried out by a solution at 14% of sodium hypochlorite. The

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dose is 0.05 mg L-1 of residual chlorine, and the average contact time is 45 minutes

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with a flow of 550 m3 h-1.

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The disinfection by PAA in CN is performed by a 15% PAA solution, the dose is

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0.010 mg L-1, and the average contact time is 55 minutes with a flow of 125 m3 h-1.

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The UV mediated disinfection in NO is carried out by a modular system composed

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by 80 UV-C lamps, whit a dose of 25.8 mJ cm-2, and a contact time of 2 seconds

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considering the suspension solid < 10mg L-1, a transmittance > 65%, and a flow of

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2400 m3h-1. However, these theoretical parameters could not be reliably achieved

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because of an exceptional algal bloom in the treatment during the experimental

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monitoring, resulting in a slight reduction of the UV radiation disinfection efficiency.

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We sampled water from the influx (A), the pre-disinfection effluent (B), and the

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effluent after the disinfection treatment (C), for each WWTP. The samples were

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collected every 3h for 24h, starting at 12:00 am: in VB on November 26, 2015; in NO

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on December 12, 2015; in CN on January 21, 2016. The weather conditions for each

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sampling campaign were optimal, without rain events during the sampling or in the

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previous week and with air temperatures between -3.1 and 7.6°C. Once collected,


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samples were kept at 4°C in the dark and processed for further analyses within 8h

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from the last sampling time point.

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Sample processing for pre- and post-incubation analyses

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Aliquots of 10 mL of each sample from each WWTP were used to measure the

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microbiological variables by flow cytometry and epifluorescence microscopy:

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bacterial cell number, bacterial size distribution, and intact/damaged cell proportions.

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Aliquots of 200 mL from each 3h sample were combined to generate the 24h-

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integrated samples for A, B, and C from each WWTP.

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From the integrated samples, 50-250 mL were firstly filtered over a 126 µm pore-size

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net (Sartorius, Germany), then concentrated on a 0.22 µm pore-size filter (47mm

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diameter, Isopore polycarbonate, Merck Millipore Co., Germany), and finally stored

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at -20°C until DNA extraction. The remaining integrated water samples were

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organized in triplicate batches (200 mL each) for A, B, and C, and incubated (in a

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climate controlled room, temp. 10±3°C) for 96 hours in the dark (incubation

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experiment), gently shaken twice a day. At time (T) 0, 24, 48, 72, and 96, a 2 mL

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sample from each replicate was collected to assess the microbiological variables

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(described below) by flow cytometry and epifluorescence microscopy. At the end of

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the experiment (T96), an aliquot of 50-190 mL was again collected from each

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replicate and processed for DNA extraction as described above.

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Microbiological variables (abundance, size distribution, cell wall conditions)

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Bacterial abundances, size-class distribution (single, dividing cells and small

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microcolonies; larger aggregates), and relative proportion of intact/damaged cells

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were assessed for the 24h monitoring and (daily) for the incubation experiment, by



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flow cytometry (Accuri C6, BD Biosciences, USA) and confirmed by epifluorescence

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microscopy. For the measurement of bacterial abundance and size distribution, an

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aliquot of 1 mL for each liquid sample was stained with SYBR Green I (Life

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Technologies, USA) solution (1%) for 15 minutes in the dark. Counts (on triplicate

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samples) were set to a minimum of 2 × 106 events within the gate designed for

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bacterial cells, and 5 × 102 events in the gates designed for larger bacterial

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aggregates26. The correct assessment of single cells and aggregates in the

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cytograms was confirmed by preliminary check of 3 selected samples per treatment

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and per WWTP in epifluorescence microscopy (Axioplan; Zeiss, Germany) on DAPI

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stained bacteria (for the complete protocol, see27).

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The Nucleic Acid Double-Staining procedure to assess the proportion of damaged

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cells was performed according to Amalfitano and coworkers28 by the concomitant

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staining by membrane-permeable nucleic acid dye SYBR Green I (1:10000 final

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concentration; Life Technologies, USA) and membrane-impermeable propidium

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iodide (PI, 10 µg mL−1 final concentration). Cells with intact membranes appeared

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then differentiated from those with damaged ones (red-marked since permeable to

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PI29).

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Gates design and calculation of events per mL for each measurement were carried

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out with the Accuri C6 resident analysis software (BD Biosciences, USA).

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DNA extraction, antibiotic resistance genes and int1quantification

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The filters collected for DNA extraction from the integrated and from the post-

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incubation samples were processed in triplicates by using the Ultraclean Microbial

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DNA Isolation Kit (MoBio Laboratories, USA). Each extracted DNA was then ten-fold

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diluted and tested for the abundance of the 16SrDNA gene (proxy of the overall


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bacterial abundance), of four antibiotic resistance genes (tetA, blaTEM, qnrS, and

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ermB), and of the mobile element integron1 (int1 gene) by qPCR (performed by a

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CFX Connect™ Real-Time PCR Detection System, Bio-Rad, USA). The primers and

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the protocols used to quantify the 16SrDNA gene and the ARGs are described in18.

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The

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GATCGGTCGAATGCGTGT-3’) and intI1LC1 (5’-GCCTTGATGTTACCCGAGAG-

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3’)30 following the same used for the quantification of the ARGs. The positive controls

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are reported in Table S1. The standard curves were carried out as introduced by Di

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Cesare et al.31. According to the methodology proposed by Bustin and colleagues32,

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the limits of detection (LOD) for 16SrDNA, tetA, ermB, blaTEM, qnrS, and int1 were

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58, 11, 54, 24, 6, and 40 copies µL-1, respectively. The specificity of the reactions

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has been verified by melting curve analysis and by electrophoresis run as previously

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described17. The mean value and standard deviation of the efficiencies for all tested

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genes were 110.26±10.80%, the coefficient of regression was 0.99 for all tested

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genes. The conversion of the gene concentrations from ng reaction-1 to copy µL-1

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were carried out following the procedures indicated in Di Cesare and co-workers31.

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qPCR results are presented as copies of ARG or int1 per copy of 16SrDNA gene

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and per ng of extracted DNA. The ARGs overall relative abundance for each sample

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is given as sum of the abundances of the single genes (with the exception of the

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samples B and C in CN, where ermB was not quantified because of specificity

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problems and thus excluded by further analyses). The interpretation of gene

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abundances lower than the LOD but positive was made accordingly to Di Cesare et

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al.18.

quantification

of

int1

was

carried

out

using

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Statistical Analysis


primers

intI1LC5

(5’-


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A two-way RM-ANOVA was applied to test for significant differences between A, B,

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and C in both the 24h monitoring of the three WWTPs and the 96h experimental

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incubation. The tested microbiological variables were: (i) bacterial absolute

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abundances (log transformed, and tested for normality of the model residuals), (ii)

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aggregate and intact cell proportions (arcsin of the square root transformed,

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normality of model residuals tested).

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Relative gene abundances (arcsin of the square root transformed, again after testing

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model residuals for normality) for single time points were compared by one-way

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ANOVA, with means compared by Tukey’s post hoc test. Given the reduced number

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of samples and the repeated analyses on the same experiments, for these analyses

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we considered significant only p-values that were lower than 0.01. All ANOVAs were

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carried out using the software JMP10 (SAS Institute Inc.).In order to address the

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potential use of int1 as a proxy for ARGs, we performed two sets of analyses. First,

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we explored whether the abundance of int1 was related to the abundance of ARGs,

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regardless of WWTP of origin (CN, NO, VB), position in the disinfection steps (influx,

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pre-disinfection, effluent), and post-incubation or not. For this step of the analyses,

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we used a graphical representation of the relationship, considering all the dataset,

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and each WWTP independently. We performed linear models to quantify the

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strength (R2) and the significance (p-value) of the correlation between int1 and

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ARGs, using log transformed data for both int1 and ARGs, in order to improve

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normality fit of residuals in the statistical models.

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Second, we explicitly addressed the question of how int1 could be used as a proxy

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for ARGs, analysing the importance of different variables that could affect

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abundance of ARGs: int1, WWTP of origin, position in the disinfection steps, and

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regrowth. The rationale is that, if int1 were to be a good proxy for ARGs, it should


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have a very high partial R2 in comparison to the other variables included in the

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models. We used linear models with log-transformed data for int1 and ARGs, and

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obtained p-value and partial R2 for each of the analysed variables.

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This part of the analyses was performed in R v3.1.233, with package asbio v1.1-534 to

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obtain partial R2 from linear models. In the case when abundances of ARGs were in

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principle quantifiable but for technical problems we could not obtain a reliable

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measurement, we did not use ‘not available’ in the analyses, but we filled the empty

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cell with the average values of that ARG across all the samples. This procedure will

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allow us to include all the data in the analyses and will not affect type I error (i.e. the

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incorrect rejection of a true null hypothesis); the bias will make it more difficult for the

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analyses to reject the null hypothesis and thus to support significant differences.

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Results

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Removal of prokaryotic cells, aggregates and cells integrity before and after

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incubation

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The overall percentage of prokaryotic cell removal between A and C was 98%

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(A=48.89±10.56 × 106 cell mL-1; C=0.90±0.25 × 106 cell mL-1), 86% (A=30.51±12.91

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× 106 cell mL-1; C=4.32±0.32 × 106 cell mL-1), and 99% (A=62.23±16.72 × 106 cell

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mL-1; C=0.38±0.09 × 106 cell mL-1) in VB, NO, and CN, respectively.

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In VB and NO, the bacterial cell abundance was significantly reduced already

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between A and B (pC, p92%) and

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remained constant in VB (64%). A significant increase of damaged cells after the

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disinfection was detected in CN where they rose from 8% (in B) to 68% (p=0.002)

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(Figure1). In NO, the proportion of intact cells was significantly lower in A than in B

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and C (p≤0.016), and no difference was observed between B and C (Figure1). The

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evaluation of intact/damaged cells was not performed in VB step C because of the

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presence of residual chlorine preventing the collection of the data.

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The overall bacterial number throughout the incubation experiment was largely

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comparable to the abundances detected at the corresponding step in the WWTPs

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(Figures 1 and 2). In addition, growth trends in batch culture were similar: a small

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increase over the first 24 hours followed by 36-48 hours of stationary growth and a

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decrease after 72 hours. The total number of bacteria in A was, however, at least

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one order of magnitude higher than that of B and C (p

Fitness and Recovery of Bacterial Communities and Antibiotic

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