Antibiotic resistance, antimicrobial residues and bacterial community composition in urban wastewater

w a t e r r e s e a r c h 4 7 ( 2 0 1 3 ) 1 8 7 5 e1 8 8 7

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Antibiotic resistance, antimicrobial residues and bacterial community composition in urban wastewater Ana Novo a, Sandra Andre´ b, Paula Viana b, Olga C. Nunes c, Ce´lia M. Manaia a,* a

CBQF, Escola Superior de Biotecnologia, Universidade Cato´lica Portuguesa, 4200-072 Porto, Portugal Ageˆncia Portuguesa do Ambiente, I.P., 2610-124 Amadora, Portugal c LEPAE, Dpto Engenharia Quı´mica, Faculdade de Engenharia, Universidade do Porto, 4200-465 Porto, Portugal b

article info

abstract

Article history:

This study was based on the hypothesis that the occurrence of antimicrobial residues and

Received 12 July 2012

antibiotic resistant bacteria in the sewage could be correlated with the structure and

Received in revised form

composition of the bacterial community and the antibiotic resistance loads of the final

3 January 2013

effluent. Raw and treated wastewater composite samples were collected from an urban

Accepted 6 January 2013

treatment plant over 14 sampling dates. Samples were characterized for the i) occurrence

Available online 17 January 2013

of tetracyclines, penicillins, sulfonamides, quinolones, triclosan, arsenic, cadmium, lead, chromium and mercury; ii) antibiotic resistance percentages for tetracycline, sulfameth-

Keywords:

oxazole, ciprofloxacin and amoxicillin and iii) 16S rRNA gene-DGGE patterns. The data of

Wastewater

corresponding samples, taking into account the hydraulic residence time, was analyzed

Bacterial community

using multivariate analysis.

Antibiotic residues Antibiotic resistance

Variations on the bacterial community structure of the final effluent were significantly correlated with the occurrence of tetracyclines, penicillins, sulfonamides, quinolones and triclosan in the raw inflow. Members of the class Epsilonproteobacteria presented positive correlations with those antimicrobials, whereas negative correlations were observed with Beta and Gammaproteobacteria and Firmicutes. Antibiotic resistance percentages presented different trends of variation in heterotrophs/enterobacteria and in enterococci, varied over time and after wastewater treatment. Antibiotic resistance was positively correlated with the occurrence of tetracyclines residues and high temperature. A relationship between antibiotic residues, bacterial community structure and composition and antibiotic resistance is demonstrated. Further studies, involving more wastewater treatment plants may help to elucidate this complex relationship. ª 2013 Elsevier Ltd. All rights reserved.

1.

Introduction

Wastewater treatment plants are considered important hotspots for antibiotic resistance spreading (Baquero et al., 2008; Martinez, 2009; Manaia et al., 2012). Three major arguments

are often used to sustain this idea. The first is that antibiotic residues and other substances with potential selective pressure, antibiotic resistant bacteria and resistance genes are heavily discharged into the municipal sewage system (Kim and Aga, 2007; Segura et al., 2009; Novo and Manaia, 2010;

* Corresponding author. Tel.: þ351 22 5580059; fax: þ351 22 5090351. E-mail address: [email protected] (C.M. Manaia). 0043-1354/$ e see front matter ª 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.watres.2013.01.010

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Luczkiewicz et al., 2010; Ku¨mmerer, 2009; Szczepanowski et al., 2009). The second is that conditions offered to microorganisms during the wastewater treatment process may favor either the selection or the horizontal gene transfer of antibiotic resistance determinants (Szczepanowski et al., 2009; Zhang et al., 2011). The third, is the observation that, worldwide, wastewater treatment, regardless of its efficiency or operational conditions, leads to the production of final effluents containing antibiotic resistant bacteria, sometimes at higher percentages that in the raw inflow (Ferreira da Silva et al., 2006, 2007; Watkinson et al., 2007; Novo and Manaia, 2010; Luczkiewicz et al., 2010; Galvin et al., 2010). The factors that explain these evidences are at the epicenter of the concerns of microbiologists and wastewater engineers interested in controlling antibiotic resistance increase and spreading into the natural environment. Nevertheless, unfortunately, the knowledge in this area is still limited. Further insights into this topic would contribute to establish adequate control measures and to achieve significant reductions on the prevalence of antibiotic resistant bacteria in treated wastewaters. In general, it is possible to assume that three major categories of factors may influence the fate of antibiotic resistant bacteria during wastewater treatment, the abiotic conditions, the composition and structure of the bacterial community and the presence of possible selective pressure factors. Among the abiotic conditions, factors such as the organic matter load, the temperature or the water flow would be hypothesized as those more relevant. Wastewater bacterial communities comprise mainly members of the phyla Proteobacteria, Bacteroidetes, Firmicutes and Actinobacteria, most of which are not easily cultivated (Zhang et al., 2012). The dynamics of the different bacterial lineages thriving in wastewater systems may be influenced by a myriad of biotic and abiotic variables (Zhang et al., 2012) and to vary during wastewater treatment. In this respect, selective pressures, exerted by antibiotic residues and heavy metals, even at low concentrations, may be very important (Martinez, 2009; Andersson and Hughes, 2011). Nevertheless, the knowledge in this area is still scant. Indeed, the relationship between bacterial community structure and composition variations and measurable indicators of antibiotic resistance in wastewaters are, to the best of our knowledge, unknown. Further insights into this relationship may contribute to elucidate the role of the socalled uncultivable bacteria on the maintenance and spreading of antibiotic resistance in wastewaters. A factor widely referred to influence the bacterial communities and promote the proliferation of antibiotic resistant bacteria is the presence of micropollutants, in particular antibiotic residues and heavy metals (Baquero et al., 2008; Martinez, 2009; Skurnik et al., 2010; Graham et al., 2011). Indeed, the occurrence of antibiotic residues in wastewaters is documented (e.g. Segura et al., 2009). Although in literature the potential selective pressure of sub-inhibitory concentrations of antibiotic residues found in wastewaters is often assumed, experimental evidences are rare and difficult to obtain (Graham et al., 2011). Nevertheless, based on in silico modeling evidences using the European Committee on Antimicrobial Susceptibility Testing (EUCAST) database, Tello et al. (2012) demonstrated that even at low concentrations antibiotics may exert a selective pressure effect.

In summary, it is possible to conclude that although many publications describe the occurrence of antibiotic residues in wastewaters, and many others explore the occurrence of antibiotic resistance in wastewaters, studies on the influence of one on the extent of the other are, to the best of our knowledge, inexistent. In spite of this apparent gap, over the last decade it became evident that assessments on antibiotic resistance maintenance and spreading in the environment will benefit from multi-parametric analyses, as those often used in ecology studies (e.g. Lopes et al. 2011; Wang et al., 2012). The current study was designed to assess the influence of abiotic factors (e.g. temperature, water flow, antibiotic residues) on the levels of antibiotic resistance and bacterial community structure of the final effluent. Possible correlations between cultivable populations of antibiotic resistant bacteria and the 16S rRNA-DGGE based bacterial community composition were also explored.

2.

Materials and methods

2.1.

Sampling

This study was conducted in a municipal wastewater treatment plant characterized in previous studies (Ferreira da Silva et al., 2006, 2007; Novo and Manaia, 2010). Briefly, this plant serves about 100 000 inhabitants equivalent and beside the municipal sewage, receives about 30% of pre-treated industrial wastewaters (mainly from food-industry and animal farming). Over a period of about 12 h of hydraulic residence time, treatment includes a primary settling tank to remove the settleable solids, an activated sludge biological process, and a secondary settling tank to remove the biomass and other suspended particles. The resultant final treated effluent (annual average of 18 000 m3 day1) is discharged into a natural water stream. A total of 14 24 h composite samples of raw (after the primary settling tank) and treated wastewater was collected in glass sterile bottles and in polypropylene flasks, refrigerated transported to the lab and analyzed within 12 h. Sampling dates comprised three periods, in 2008 and 2009, of two or four consecutive days (from Tuesday to Friday). In order to assess possible seasonal variations, samples were collected in the periods 25e28th November, and 4e5th December (Autumn, P1), in 31st Marche3rd April (early Spring, P2) and 21ste24th April (Spring, P3) (Table 1).

2.2.

Enumeration of cultivable bacteria

The enumeration of total and antibiotic resistant bacteria was made based on the membrane filtration method as described by Novo and Manaia (2010). Briefly, membranes through which were filtered 1e10 mL of adequate decimal dilutions of wastewater samples were incubated on different culture media for enumeration of bacteria: plate count agar (PCA, Pronadisa), 24 h at 30  C for heterotrophs; m-Faecal Coliforms agar (m-FC, Difco), 24 h at 37  C for enterobacteria and m-Enterococcus agar (m-Ent, Difco) 48 h at 37  C for enterococci. The corresponding antibiotic resistant subpopulations were enumerated on the same media supplemented with the

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Table 1 e Climate conditions, physicochemical parameters and sampling schedule. Autumn

Sampling dates (dd/m/y) Average values of mean temperature ( C)a Range values of precipitation (mm)a Range values of relative humiditya Range of daily flow (m3 d1) CODb (mg O2 L1) BODb (mg O2 L1) Range of concentrations (mg L1)

Hg As Quinolones Tetracyclines Penicillins Sulfonamides Triclosan

RWW TWW RWW TWW RWW TWW RWW TWW RWW TWW RWW TWW RWW TWW RWW TWW RWW TWW

Spring

P1

P2

25e28/11/08 04e05/12/08 7.0e12.0 0e7 53.7e96.3 14,747e25,605 566 82 273 21 <0.1e0.18 <0.1e0.17 1.9e2.9 1.5e3.7 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D.

31/03/09 01e03/04/09 9.0e12.0 0 49.7e79.0 18,289e19,556 589 100 293 25 <0.1e0.46 <0.1 1.8e2.5 1.5e1.9 2.2e15.5 0.3e4.4 21.1e22.2 0.2e4.2 1.5e9.5 0.7e13.9 3.6e13.1 0.8e4.0 0.5e0.6 0.6e0.8

P3 21e24/04/09 13.0e17.1 0 55.3e78.3 17,779e18,983 580 61 306 19 <0.1e0.15 <0.1 2.0e2.2 1.8e2.1 6.2e18.0 1.6e3.5 27.6e37.4 6.1e7.0 6.4e12.4 0.4e4.8 7.2e10.0 2.6e2.8 0.5e0.6 0.5e0.6

RWW, raw wastewater; TWW, treated wastewater; N.D., not determined. a Detailed information is available in Supplementary Table S1, Source: Instituto Portugueˆs do Mar e da Atmosfera. b Average values for those sampling dates, Source: wastewater treatment plant; Cd, Cr and Pb were below the limit of quantification (0.05 mg L1).

following antibiotic concentrations: 32 mg L1 amoxicillin (AML, Sigma); 16 mg L1 tetracycline (TET, Sigma), 4 mg L1 ciprofloxacin (CIP, Sigma) and 350 mg L1 sulfamethoxazole (SUL, Sigma). These antibiotic concentrations were based on previous studies (Watkinson et al., 2007). All the procedures were done in triplicate. Dilutions with 10e80 colony forming units (CFU) were the basis for further data processing and analyses. The percentage of resistance for each antibiotic and bacterial group corresponded to the ratio of CFU mL1 on the culture medium with and without antibiotic (Novo and Manaia, 2010).

2.3.

Bacterial community characterization

The bacterial community was characterized using the cultureindependent method denaturing gradient gel electrophoresis (DGGE). Total DNA extracts were prepared after filtration of 25 mL of raw wastewater and 150 mL of treated effluent through polycarbonate membranes (0.2 mm porosity, Whatman). DNA extraction was made with the aid of a commercial kit (PowerSoil DNA Isolation kit, MO BIO), according to the manufacturer instructions. DNA quantification was made by fluorimetry (Qubit Fluorometer, Invitrogen) as described before (Lopes et al., 2011). In order to control variations due to DNA extraction, PCR reactions and DGGE analysis, three total DNA extracts were obtained for each sampling site and date being further processed independently. DGGE relied on the analysis of the region V3 of the 16S rRNA gene flanked by the primers 338F-GC-clamp (50 -GACTCCTACGGGAGGCAGCAG-30

with a GC-clamp attached) and 518R (50 -ATTACCGCGGC TGCTGG-30 ), corresponding to a 200 bp fragment as described by Muyzer et al. (1993). A reaction mixture of 50 mL comprised 0.5 KCl and 0.5 (NH4)2SO4 buffer, 3 mM MgCl2, 0.4 mM dNTP’s mix, 5% DMSO, 0.6 mM each primer, 1.5 U of Taq polymerase (Stabvida) and 4 mL of template DNA (or water in the negative control) and was subjected to amplification (Biometra) with the following conditions: 5 min at 94  C, 35 cycles of 30 s at 92  C, 30 s at 55  C, 30 s at 72  C, final extension of 20 min at 72  C. A volume of PCR product containing 1.8 mg of DNA was loaded onto a vertical polyacrylamide gel (8% w/v) with a denaturing gradient ranging from 30 to 52% (in which 100% denaturing gradient is 7 M urea and 40% deionized formamide). For inter-gel comparison and normalization, each gel included two lanes of a reference sample, comprising a mixture of cultures which profile spanned the whole denaturing gradient. Electrophoresis (DCode universal mutation detection system, Bio-Rad Laboratories), gel staining and image acquisition (Molecular Imager Gel Doc XR system, BioRad Laboratories) were performed as described before (Barreiros et al., 2008). DGGE profiles were examined visually and further compared using the Bionumerics software (version 6.1, Applied Maths). Pattern normalization, bandmatching and inter-gel comparisons were based on the principle of the equality of reference lanes. According to band position, band classes were defined, and their respective intensity values were registered. A two-entry table with band intensity value per band class in each DGGE profile supported the further comparison of samples.

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Bands corresponding to resident bacteria (found in raw and in treated wastewater) or characteristic of a variation pattern (temporal, type of water, etc) were excised, amplified with the same primers and cloned with a commercial system according to the manufacturer’s instructions (InsTAclone PCR cloning kit, MBI Fermentas). Clone inserts with 200 bp were amplified with the same primers, analyzed by DGGE to confirm the authenticity of the band of interest and sequenced with the primer M13F-pUC (50 -GTTTTCCCAGTCACGAC-30 ). Up to five randomly selected clones were sequenced per band, in order to assess the occurrence of multiple sequence DGGE bands. The phylogenetic affiliation of each nucleotide sequence was inferred after GenBank database querying using the BLAST software (http://www.ncbi.nlm.nih.gov/).

2.4.

Quantification of micropollutants

P2 and P3 samples (but not P1, due to technical problems) were characterized for the content of the metals cadmium, lead, chromium arsenic and mercury, the antimicrobials oxytetracycline, doxycycline tetracycline, chlortetracycline, penicillin G, penicillin V, sulfathiazole, sulfamethazine, enrofloxacin, ciprofloxacin, ofloxacin and triclosan. Cadmium, chromium and lead were determined by inductively coupled plasma atomic emission spectroscopy (ICPOES) and arsenic by hydride generation atomic absorption spectroscopy (HGAAS), after humid acid digestion by micro-waves. Mercury was determined by cold vapor atomic absorption spectrometer (CVAAS). Antibiotics were quantified in water samples by liquid chromatography (LC) coupled with electrospray ionization mass spectrometry (ESI-MS), after solid-phase extraction (SPE). The reference compounds referred to above and the standards sulfathiazole-d4, flumequine and 13C-phenacetin (>98%) were purchased from Dr. Ehrenstorfer GmbH. Water samples were filtered through 1 mm glass fiber filters followed by 0.45 mm nylon membrane filters (from Whatman). A Na2EDTA solution (5%, w/v) was added prior to micropollutants extraction, performed with the aid of an automated sampler processor (ASPEC XL, Automated Sample Preparation with Extraction Columns, Gilson). For SPE, 200 mL of wastewater samples were loaded at 3 mL min1 onto Oasis HLB cartridges (200 mg, 6 mL, Waters) pre-conditioned with 5 mL of methanol, followed by 5 mL of HPLC grade water. Cartridges were rinsed with 5 mL of HPLC grade water and were dried under vacuum for 15e20 min, to remove excess of water. Antibiotics were eluted with 2  4 mL of methanol. Extracts were evaporated to 0.5 mL under a gentle nitrogen stream and reconstituted to 1 mL of methanol/water (25:75, v/v). A standard mixture containing the internal standards sulfathiazoled4, flumequine and 13C-phenacetin was used as internal standard calibration. LC-MS analysis was performed using an Agilent HP 1100 HPLC equipped with an autosampler and connected in series with a mass selective detector quadrupole. The quadrupole mass spectrometer was operated with an atmospheric pressure electrospray ionization (API-ESI) source in positive ion mode. Chromatographic separation was achieved with a Purospher Star RP-18 endcapped column (125 mm  2.0 mm, particle size 5 mm) preceded by a C18 guard column (4  4.5 mm), both supplied by Merck. The analysis was performed using acetonitrile as eluent A and HPLC grade

water with 0.1% formic acid as eluent B. The elution gradient started with 5% eluent A, increasing to 95% in 35 min. Initial conditions and re-equilibration time were 25 min. Chromatographic analyses lasted in 60 min. The injection volume was set at 25 mL and the flow rate was 0.3 mL min1. Quantitative analysis was performed in a selected ion monitoring (SIM) mode. Identification of target analytes was accomplished by comparing the LC retention time and m/z ions monitored of the target compounds in the samples with those of standards analyzed under identical conditions. The diagnostic m/z ions (positive ionization mode) were respectively: sulfathiazole (256/257); sulfamethazine (279/280); ciprofloxacin (332/333); enrofloxacin (360/361); ofloxacin (362/363); oxytetracycline (461/462); chlortetracycline (479/480); tetracycline (445/446); doxycycline (445/446); penicillin V (383/384); penicillin G (367/ 368). For internal standard based quantification, eight-point calibration curves, based on peak areas, were constructed using a least-square regression analysis from the injection of standard mixtures of the analytes at concentrations ranging between 25 mg L1 and 600 mg L1. For estimation of the method recoveries (% R) samples of wastewaters were fortified, prior to the extraction, with appropriate concentrations of standard mixtures containing target analytes, and were subject to the analytical procedure mentioned before. Blank samples of the matrix were also evaluated to avoid overestimations in the calculation of the recoveries. The recoveries varied between 63% and 97%. Quantification limits (LOQs) were within the standard of the calibration curve. Triclosan was detected and quantified by gas chromatography-mass spectrometry (GC/MS), equipped with a column (Varian, VF5-MS com 30 m; 0.25  0.25), after SPE extraction (Lichrolut RP-18, 500 mg, 40e60 mm, 6 mL, Merck). Cartridges were conditioned with water and methanol and elution was made twice with 4 mL methanol. The extract was dried under a smooth nitrogen flow and reconstituted in dichloromethane. A volume of 2 mL of sample was injected (splitless injector at 250  C) and the GC/MS operated in positive mode (EIþ) at 70 eV. Triclosan mass fragments corresponded to m1/Z1 ¼ 288 and m2/Z2 ¼ 290 (SIM mode).

2.5.

Diversity indices and statistical analyses

P The bacterial diversity [H0 ¼  pi ln( pi)] and evenness [J ¼ H0 / ln(Hmax)] indices were calculated based on the DGGE profiles using the Shannon and Weaver’s (1963) and Pielou’s (1966) indices, respectively. In this analysis it was considered that the abundance of each operational taxonomic unit (OTU) corresponded to the band intensity, assuming the principle one band-one taxon. One-way Anova and post-hoc Tukey test (SPSS 19.0 for Windows) were used to assess statistically significant differences ( p < 0.05) among the values of antibiotic resistance percentage and diversity indices. Variation of the bacterial community structure or of the log counts of total and antibiotic resistant bacteria in function of time, type of water, micropollutants concentration or the log counts of antibiotic resistant bacteria (environmental variables, Table 1) were assessed based on Detrended or Canonical Correspondence Analyses (DCA and CCA, software package CANOCO version 4.5). The influence of raw

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wastewater conditions in the final effluent properties was assessed based on the comparison of corresponding samples, with a lapse of 24 h between the raw (day n) and the final effluent (day n þ 1) samples. The raw data for multivariate analyses comprised tables of sampling day versus percentages of antibiotic resistance, concentrations of micropollutants and other physicochemical data, and DGGE band abundance (band intensity). For these analyses DGGE profiles were organized as a Bionumerics (software version 6.1, Applied Maths) output table, containing band position versus band intensity for each of the triplicates. In the CCA analysis, all variables (abiotic conditions, antimicrobials and metal concentrations, log(CFU) of culturable bacteria) for which the null hypothesis was excluded ( p < 0.05) were included in the ordination. The significance of the relationship between community data (DGGE patterns or log(CFU) of culturable bacteria) and the environmental data (abiotic condition, antimicrobials and metal concentrations or log(CFU) of culturable bacteria) was tested by Monte Carlo permutations test (n ¼ 499). Explanatory variables included in CCA analyses were selected by manual forward selection including the permutation test (Monte Carlo permutations test). Whenever only one explanatory variable was included in the ordination, no biplot was produced.

3.2. Abiotic factors, operating parameters and bacterial community structure Factors potentially associated with the observed variations on the structure of the bacterial communities were assessed using multivariate analysis (CCA). Of the variables water flow, maximum and minimal temperature, chemical oxygen demand (COD) and biological oxygen demand (BOD) in the raw wastewater only maximal temperature, water flow and the COD were observed to significantly ( p < 0.05) affect the bacterial community structure of treated wastewater (Fig. 2). Among these, COD, strongly correlated with axis 1, had the most notorious effect on the structure of the bacterial community of the final effluent, affecting mainly bands B1 (negatively) and B4 (positively). These bands, respectively affiliated to Epsilon and Gammaproteobacteria (Table 2), contributed to separate mainly the December samples (P1) from the others. Interestingly, the maximal temperature, COD and water flow were also significantly correlated with the bacterial community composition of the raw wastewater. But, in this case, the maximal temperature exerted the strongest influence, contributing to separate the Spring from the Autumn samples, due to the variance of bands B9, B18 and B19, more intense in P3 than in P1 DGGE profiles (Figs. S1 and S2, Table 2).

3.3.

3.

Results

3.1. 16S rRNA-DGGE patterning of the bacterial community The characterization of the bacterial community through 16S rRNA-DGGE analyses allowed the assignment of a total of 20 bands, 16 in the raw and 17 in the treated wastewater samples, 13 of which were common to both types of water (Fig. S1, Table 2). The excision of DGGE bands and subsequent nucleotide sequence analysis showed that Proteobacteria (divisions Beta > Gamma ¼ Epsilon) prevailed among the analyzed populations. The phyla Bacteroidetes, Firmicutes and Fusobacteria were represented by two or a single band, respectively. A detrended correspondence analysis (DCA) of the raw and treated wastewater DGGE profiles registered for the three sampling periods, evidenced temporal variations of the bacterial community structure in both raw and treated wastewater (Fig. 1). Nevertheless, such variations did not coincide in raw and in treated wastewater. Over the period P1 the structure of the treated wastewater bacterial communities differed most from those of raw wastewater. Moreover, the treated wastewater samples collected during P1 were divided into two groups, with the December samples (Numbers 10e12, Fig. 1), in days of heavy rain, separated from the others (Numbers 1e9, Fig. 1). The comparison of richness, diversity, evenness and dominance indices showed also a time course variation, not coincident in raw and treated wastewater (Table 3). Whereas in raw wastewater, richness, diversity and evenness significantly increased from the Autumn (P1) to the Spring periods (P2 and P3), in the final affluent these indices were significantly lower in the early Spring (P2) than in the other periods (Table 3).

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Antibiotic resistant cultivable populations

The levels of bacteria exhibiting antibiotic resistance phenotypes were assessed based on culture-dependent methods. Over the sampling dates, the raw inflow contained around 1011 CFU per inhabitant equivalent (CFU IE1) of cultivable heterotrophs and enterobacteria and 109 CFU IE1 of enterococci. In the final effluent the CFU IE1 counts were ten times lower for the three bacterial groups than in the raw inflow. Over the three sampled periods, the counts of total cultivable bacteria did not differ significantly for any of the analyzed bacterial groups. The percentage of bacteria that could grow in the presence of 32 mg L1 amoxicillin, 16 mg L1 tetracycline, 4 mg L1 ciprofloxacin or 350 mg L1 sulfamethoxazole, herein designated as antibiotic resistant, varied for the different bacterial groups, sometimes between the sampling periods and also between raw and treated wastewater (Table 4). For total heterotrophs and enterobacteria the highest resistance percentages were observed for amoxicillin while for enterococci the highest resistance percentages were observed for tetracycline and mainly for sulfamethoxazole (Table 4). Temporal variations were observed mainly in Spring with the highest percentages of tetracycline resistant heterotrophs and amoxicillin resistant enterobacteria observed in the period P3. Among the antibiotic resistance phenotypes examined, sulfamethoxazole exhibited the sharpest variations, with significant increases in the periods P2 and/or P3 in comparison to P1. This effect was particularly evident for heterotrophs. The temporal variations observed in the resistance percentages of the raw inflow did not coincide with those observed in the final effluent (tetracycline resistant heterotrophs, amoxicillin and ciprofloxacin resistant enterobacteria). Significant differences of resistance percentage between raw and treated water corresponded, most of the times, to increases after treatment and were more frequent in P1 than in P2 or P3. Examples are

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Table 2 e Correlations between treated wastewater bacterial community members, assessed based on 16S rRNA geneDGGE bands, and the occurrence of antimicrobials, antibiotic resistant bacteria or COD in the raw inflow. Band

Affiliation (NCBI accession number)

Raw

Treated

B1

Epsilonproteobacteria, related with Arcobacter (JF915357.1)

p(P1,P2,P3)

p(P1,P2,P3)

B2

Epsilonproteobacteria, related with Arcobacter (AP012048.1)

Not detected

p(P1,P3)

B3

Gammaproteobacteria, related with Acinetobacter (JQ795854.1) Gammaproteobacteria, related with Pseudomonadales (AB362308.1)

p(P1,P2,P3)

p(P1,P2,P3)

Not detected

oP1; w(P2,P3)

N.D. Fusobacteria, related with Streptobacillus (AB330760.1) Betaproteobacteria, related with Comamonas (EU869280.1) Epsilonproteobacteria, related with Sulfurimonas (CP002205.1)

pP1; owP2; wP3 p(P1,P2,P3) o(P1, P2); pP3 Not detected

owP1; wP3 owP1; p(P2, P3) p(P1,P2,P3)

B9 B 10

Bacteroidetes (AB623230.1) Betaproteobacteria, related with Acidovorax (JQ689194.1)

ow(P1, P2); pP3 p(P1,P2,P3)

owP1; p(P2, P3) oP1; pP2; wP3

B 11

Firmicutes, related with Clostridium (EU728741.1)

p(P1,P2,P3)

p(P1,P2,P3)

B 12

Gammaproteobacteria, related with ‘Rheinheimera (JQ361154.1) Betaproteobacteria, related with Simplicispira (AB680538.1) Bacteroidetes, related with Leadbetterella (JF700250.1)

ow(P1, P2); pP3

B4

B B B B

5 6 7 8

B 13 B 14

Not detected

p(P1,P2,P3)

ow(P1,P2,P3)

pP1; oP3

B 15

Betaproteobacteria, related with Comamonas (HE575934.1)

p(P1,P2,P3)

oP1; p(P2, P3)

B 16

Firmicutes, related with the genus Phascolarctobacterium (X72866.1)

p(P1,P2,P3)

p(P1,P2)

B 17

Betaproteobacteria, related with Comamonas (AB277850.1)

p(P1,P2,P3)

p(P1,P2)

B 18 B 19 B 20

N.D. Betaproteobacteria, related with Curvibacter (AB696860.1) N.D.

o(P1, P2); pP3 owP1; p(P2, P3) wP1; p(P2, P3)

Not detected p(P1,P2,P3) pP1; wP2; pP3

Correlated environmental factors COD () Quinolones (þ) Enterobacteria (þ) TET resistant enterobacteria (þ) Tetracyclines (þ) Penicillins (þ) Sulfonamides (þ) Triclosan (þ)

COD (þ) Tetracyclines () Penicillins () Sulfonamides () Triclosan () CIP resistant enterococci (þ) Enterobacteria () TET resistant enterobacteria ()

Tetracyclines (þ) Penicillins (þ) Sulfonamides (þ) Triclosan (þ) Heterotrophs (þ) CIP resistant heterotrophs (þ) Tetracyclines () Penicillins () Sulfonamides () Triclosan () Heterotrophs () SUL resistant enterobacteria (þ) CIP resistant heterotrophs () CIP resistant enterococci (þ) Heterotrophs (þ) CIP resistant heterotrophs (þ)

Enterobacteria () TET resistant enterobacteria () Tetracyclines () Penicillins () Sulfonamides () Triclosan () Tetracyclines () Penicillins () Sulfonamides () Triclosan () Enterobacteria () TET resistant enterobacteria () Tetracyclines () Penicillins () Sulfonamides () Triclosan ()

, positive/negative correlations; N.D., not determined; p, present; o, occasional ¼ present in 50% of the samples of that period; w, weak ¼ with intensity lower than 10% of the maximum.

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2.0

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Axis 2 (7.5 %)

Raw P3 Raw P2 27 28 25 30 29 24

15 26

22

14 21 14

23

19 21 17 16 20 18 6 5 6 10 7

13

9

17

13 16

3 6

24 28 29

23 22 27 26 25

11 12 10

1 5 2

4

7 8

30

Treated P3

9

8 4 2

-0.1

Treated P1

15

18

19 20

Raw P1

Treated P1

Treated P2

3

12 11

1

-0.2

3.0

Axis 1 (34.2 %)

Fig. 1 e Detrended correspondence analysis of the 16S rRNA gene-DGGE profiles of raw (dark circles) and treated (white circles) wastewater over the periods P1eP3. Samples numbering: 1e3, 26 Nov 2008; 4e6, 27 Nov 2008; 7e9, 28 Nov 2008; 10e12, 05 December 2008; 13e15, 01 April 2009; 16e18; 02 April 2009; 19e21, 03 April 2009; 22e24, 22 April 2009; 25e27, 23 April 2009; 28e30, 24 April 2009. The DGGE band scores variation was of 0.969, 34.2% of which could be explained by axis 1 and 7.5% by axis 2.

the tetracycline resistant heterotrophs, and the amoxicillin and ciprofloxacin resistant enterobacteria. Curiously, these were the same categories, for which significantly higher resistance rates were observed in raw inflow in the periods P2 and/or P3 than in P1. The only significant decrease of resistance percentage after wastewater treatment was observed for amoxicillin resistant enterobacteria in P3 (Table 4).

3.4. Abiotic factors, operating parameters, antimicrobial residues and antibiotic resistant cultivable populations According to the multivariate analysis (CCA), none of the environmental factors tested (temperature, COD, BOD, water flow) could explain the variation observed on the loads of antibiotic resistance bacteria (log CFU IE1) in raw water. In contrast, in the treated effluent, in spite of the low variation of antibiotic resistant bacteria loads, it was possible to correlate these variations with the maximal temperature ( p < 0.05; inter-set correlation with axis 1 ¼ 0.92; specieseenvironment correlations with axes 1 and 2 of 0.92 and 0.81, respectively). The variations on the abundance of sulfonamide resistant

heterotrophs and enterobacteria were those most correlated with the maximal temperature. This finding is supported by the data presented in Table 4, with significantly higher resistance percentages for those bacteria in P2 and P3 than in P1, which coincide with the values of maximal temperature (Table 1, Table S1). Among the antimicrobial residues analyzed, tetracyclines concentration (the sum of the three tetracyclines determined) in the raw inflow was the only antimicrobial agent observed to have a significant association ( p < 0.05; inter-set correlation with axis 1 ¼ 0.88) with the loads of antibiotic resistant bacteria in the final effluent (specieseenvironment correlations with axes 1 and 2 of 0.88 and 0.00, respectively). However, it was not possible to identify a specific correlation between the tetracycline resistance in the final effluent and the presence of this class of antibiotics in the raw inflow.

3.5. Antibiotic resistance, antimicrobial residues and bacterial community structure The correlation between the loads of antibiotic resistant bacteria (log CFU IE1) in the wastewater treatment plant inflow on the composition and structure of the bacterial community composition of raw water was assessed. The loads of amoxicillin ( p ¼ 0.004), ciprofloxacin ( p ¼ 0.002) and tetracycline ( p ¼ 0.004) resistant enterococci, sulfamethoxazole ( p ¼ 0.002) and tetracycline resistant heterotrophs ( p ¼ 0.006) and sulfamethoxazole resistant enterobacteria ( p ¼ 0.002) could explain the variations observed in DGGE profiles over time. Among the antibiotic resistant populations referred to above, only sulfamethoxazole resistant enterobacteria and heterotrophs could explain the DGGE bands variation over axis 1. The populations presenting the strongest positive correlations with those antibiotic resistant populations were B18 (not identified), B9, B11 and B19, related with the phyla Bacteroidetes and Firmicutes and the class Betaproteobacteria, respectively (Fig. S3, Table 2), more intense in samples collected in Spring (mainly P3) than in Autumn. In contrast band B1, related to the class Epsilonproteobacteria and less intense in P3, correlated negatively with sulfamethoxazole resistant enterobacteria and heterotrophs. The correlations between the concentrations of antimicrobial residues or abundance of cultivable bacteria in the raw inflow and the composition and structure of the bacterial community in the final effluent were also analyzed. Different cultivable bacterial populations present in the raw inflow

Table 3 e Richness and diversity and evenness indices determined over the sampling period based on the DGGE patterns. Index

Type water

Indices values P1 (n ¼ 6)

Richness (number of bands) Shannon (H, Diversity) Pielou’s (E, Evenness)

Raw Treated Raw Treated Raw Treated

14.1  17.8  2.12  2.48  0.34  0.37 

a

1.30 1.38a 0.16a 0.13a 0.02a 0.02a

P2 (n ¼ 4) 15.7 14.5 2.38 2.24 0.38 0.35

b

 1.30  1.24b  0.14b  0.11b  0.03b  0.01b

Significantly different values ( p < 0.05) are indicated by a or b for distinct periods, or underlined for different types of water.

P3 (n ¼ 4) 16.0 19.3 2.46 2.53 0.36 0.38

 0.60b  1.91a  0.07b  0.18a  0.01b  0.02a

1882

1.0

A

w a t e r r e s e a r c h 4 7 ( 2 0 1 3 ) 1 8 7 5 e1 8 8 7

B17

Axis 2 (21.1 %)

B2

B8 B11 B13 B19 B20

B5

(ax1, 0.61)

B15 B6

B3 B10 B4

Temperature (max.) (ax1, - 0.60; ax2, -0.62)

-1.0

B14

COD

B1

B9

Water Flow (ax1, 0.60; ax2, -0.68)

-1.0

1.0

Axis 1 (40.4 %)

1.0

B

B16

P1 (November) 2

Axis 2 (21.1 %)

974 5 1 8 6

P2

15

3

13 14

COD

16 17

P3 29

28

18 26 27

30 25

21 19 20

23 22

P1 (December) 11 12

24

10

were significantly correlated with variations of the DGGE profiles (Fig. 3). Total and tetracycline resistant enterobacteria were those with the strongest correlations with the community structure distribution over axis 1, associated with variations on the intensity of bands B1, B4, B14 and B16. Among these, bands B1 (related to Epsilonproteobacteria) and B4 (related to Gammaproteobacteria), presented the strongest variations and were, respectively, faint and most intense in the DGGE profiles of December. Bands B14 (related to Bacteroidetes) and B16 (related to Firmicutes) with a lower contribution for species variation, were most intense in Autumn samples. The abundance of total and ciprofloxacin resistant heterotrophs and ciprofloxacin resistant enterococci was also correlated with the variations of the bacterial community, over axis 2 (Fig. 3, Table 2). In particular, bands B8 and B11 were correlated with the abundance of total and ciprofloxacin resistant heterotrophs and band B10 with ciprofloxacin resistant enterococci. In general, antibiotic resistant cultivable enterococci and antibiotic resistant cultivable heterotrophs or enterobacteria presented distinct patterns of correlations. In addition, the abundance of antibiotic resistant bacteria was most of the times positively correlated with Epsilonproteobacteria, and negatively correlated with Gammaproteobacteria and Firmicutes (Table 2). The concentrations of antibiotics and triclosan residues in the raw wastewater were also correlated with the bacterial community structure and composition of the final effluent (Fig. 4). Populations represented by bands B2 and B8, affiliated to Epsilonproteobacteria and most intense in P3 samples, were correlated with the concentration of antibiotics such as tetracyclines, penicillins and sulfonamides. Band B1, also related with the Epsilonproteobacteria, correlated positively with the presence of quinolones. Other populations represented by bands B4, B10, B15, B17 and B16, related with Gamma or Betaproteobacteria or Firmicutes, most intense or more frequent in the P2 or P3 samples, were negatively correlated with the presence of those residues (Fig. 4, Table 2).

Temperature (max.)

-1.0

Water Flow

4. -1.0

Axis 1 (40.4 %)

Discussion

1.0

Fig. 2 e A, B. Correspondence Canonical Analysis (CCA) of the variation of 16S rRNA gene-DGGE patterns of the treated wastewater (scores variation of 0.653, of which 40.4% could be explained by axis 1 and 21.1% by axis 2) in function of the environmental variables (minimal and maximum temperature (Tmax), p > 0.05 and p [ 0.002, respectively; biological and chemical (COD) oxygen demand in the raw inflow, p > 0.05 and p [ 0.038, respectively and water flow, p [ 0.002). The specieseenvironmental correlations for axes 1 and 2 were, respectively, 0.934 and 0.891. Only the variables significantly ( p < 0.05) explaining the observed community variation are shown and their inter-set correlation values are indicated. 16S rRNA gene-DGGE bands with a fraction of variance over axis 1 higher than 0.7/1 are indicated by circles. A. Representation of DGGE bands distribution; B. Representation of samples distribution. Samples numbering: P1, 1e12; P2, 13e21; P3, 22e30, according to Fig. 1 legend.

Wastewater treatment plants, the main receptors of antibiotic resistant bacteria and of soluble chemical contaminants, such as antibiotics, biocides and heavy metals, are the major reactors in urban areas for antibiotic resistance emergence and development (Martinez, 2009; Manaia et al., 2012; Michael et al., in press). It is frequently assumed that the presence of antibiotics in an environment is the major driving force for resistance selection and spreading (Baquero et al., 2008; Martinez, 2009; Graham et al., 2011). However, evidences demonstrating that antibiotic residues disturb the microbial communities and promote antibiotic resistance selection are scarce. In part, such limitation is due to the fact that microbial communities in wastewater habitats are influenced by numerous factors, such as the type of biotreatment and operation conditions (e.g. suspended solids, solids and hydraulic residence time), the geographical region, abundance and type of organic substrates, dissolved oxygen concentration, temperature or salinity (Zhang et al., 2012; Wang et al., 2012). Therefore, it is difficult to dissociate the effect of

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Table 4 e Antibiotic resistance prevalence over the sampled period. Group

Heterotrophs

Antibiotic

AML TET SUL CIP

Enterobacteria

AML TET SUL CIP

Enterococci

AML TET SUL CIP

Type water

Raw Treated Raw Treated Raw Treated Raw Treated Raw Treated Raw Treated Raw Treated Raw Treated Raw Treated Raw Treated Raw Treated Raw Treated

Mean values of antibiotic resistance prevalence  standard deviation (%) P1 (n ¼ 6)

P2 (n ¼ 4)

P3 (n ¼ 4)

38.3  8.6 35.7  12.2 1.1  0.3a 2.2  1.3 0.6  0.3a 0.6  0.4a 2.5  1.5 2.0  1.0 35.9  12.4a 49.1  15.6a 2.5  1.0 2.7  0.8 3.3  3.2a 5.7  2.0a 1.2  0.3a 2.7  0.8a 0.3  0.3 0.8  0.4 15.2  4.7 19.0  4.1 66.5  15.2 75.9  12.0a 2.0  1.3 2.9  0.8

37.4 42.0 1.7 1.9 10.0 11.7 2.6 1.8 38.7 22.6 3.9 3.7 6.9 7.3 2.4 2.0 0.8 0.6 17.8 20.4 78.0 85.9 2.5 1.9

 12.5  16.0  0.4a  0.6  6.8b  6.0b  0.9  0.6  19.6a  4.3b  1.8  2.3  3.0a  3.5a  1.2b  1.2a  0.5  0.4  5.6  6.7  12.3  8.7b  0.4  0.6

39.7  11.5 20.0  7.7 2.4  1.3b 2.0  1.3 14.9  5.9b 12.9  3.5b 2.3  1.1 1.3  0.4 60.9  26.0b 38.3  20.1a 3.2  1.2 4.0  1.5 12.3  2.7b 13.6  8.4b 1.2  0.3a 1.0  0.8b 1.2  1.2 0.8  0.5 17.5  5.3 22.7  5.9 75.6  5.4 64.8  20.1a 2.2  1.4 2.0  0.7

AML, amoxicillin; TET, tetracycline; SUL, sulfamethoxazole; CIP, ciprofloxacin. Significantly different values ( p < 0.05) are indicated by a or b for distinct periods, or underlined for different types of water.

antibiotic residues from other environmental variables. On the other hand, once acquired, antibiotic resistance may be rather stable in the environment. The major reason for such stability is the absence of antibiotic resistance fitness costs, i.e. the fact that the acquisition of resistance determinants may not reduce the survival and proliferation of a bacterium, even in the absence of selective pressures. For these reasons, prudence is recommended on the identification and evaluation of possible stressors capable of promoting antibiotic resistance spreading (Andersson and Hughes, 2010). The structure of the microbial community is one of the factors that may determine the potential of the antibiotic resistant bacteria or antimicrobial residues inputs to promote the enhancement or spread of resistance determinants. To improve the knowledge in this area, microbial community ecology approaches are needed to infer the relationships of species with the environment. Such studies, which rely on the use of two types of data, the occurrence and abundance of biological populations and environmental variables measured in the same time and site, may bring a holistic perspective of the antibiotic resistance evolution in the environment. Integrating data of culture-dependent and culture-independent methods, analytical determinations of heavy metal and antimicrobial residues and environmental and operational parameters, the current study endeavored to correlate the dynamics of the bacterial communities with antimicrobials concentrations or antibiotic resistant bacteria abundance. As expected, temperature and COD (a measure of the organic load) were, among the factors tested, those that most affected the bacterial community structure. Different bacterial

populations were most correlated with COD or temperature in raw and in treated wastewater, which can be explained by the fact that in the raw wastewater variations may be due to the inflow quality whereas in the treated wastewater to the treatment conditions and efficiency. The strong correlation between COD and final effluent community structure can be explained because the organic load is a source of nutrients and, thus, can influence the growth and dynamics of some populations. In raw wastewater, among the five bacterial populations most correlated with antibiotic resistant bacteria, three, represented by bands B9, B18 and B19 (Bacteroidetes and Betaproteobacteria), were also correlated with environmental conditions. It is thus suggested that the same populations are correlated with both, the loads of antibiotic resistant bacteria reaching the urban wastewater treatment plant and the maximal temperature. One of the major aims of this study was to infer if the presence of antibiotic residues could be responsible for increased resistance percentages. Indeed, it was concluded that tetracyclines were significantly correlated with higher resistance percentages, although not specifically with tetracycline resistance. This apparently intriguing result is in agreement with previous studies (e.g. Oberle´ et al., 2012; Looft et al., 2012) and emphasizes the complexity and multitude of factors that may influence antibiotic resistance selection. It was not possible to find significant correlations between the concentrations of the other antimicrobials and metals analyzed in the raw inflow and the variations of antibiotic resistance percentage values in the final effluent. According to modeling studies performed by Singer et al. (2011), the sum of

1884

A

HetTOT

1.0

1.0

A

w a t e r r e s e a r c h 4 7 ( 2 0 1 3 ) 1 8 7 5 e1 8 8 7

HetCIP

(ax1, 0.12; ax2, 0.65) (ax1, -0.25 ; ax2, 0.61)

HetTET (ax1, -0.39; ax2, 0.29)

QUI

B2

B11

Axis 2 (20.5 %)

B8

B5

B20

(ax1, 0.15; ax2, 0.36)

B17 B13

B14

B19

B1

B3 B9 B6

EntTET

B1 B10 B16 B17

B15

B19 B6 B13

B4

SUL (ax1, 0.67; ax2, 0.11)

B11

B20

TET

Triclosan (ax1, 0.20)

B9

(ax1, 0.89)

B8 B3

B2 B5

B16

(ax1, -0.50; ax2, -0.29)

B10 EcoCIP

EntSUL

B4

(ax1, -0.46; ax2, -0.43)

-1.0

-1.0

(ax1, -0.32; ax2, -0.46)

1.0

B

1 HetTOT

HetCIP

-1.0

1.0

Axis 1 (39.2 %)

1.0

Axis 1 (75.2 %)

1.0

-1.0

B

PEN (ax1, 0.71; ax2, 0.25)

B16

Axis 2 (8.4 %)

EntTOT (ax1, -0.51; ax2, 0.34)

29

5

30 28

P3

P1 (November) 3

6

QUI HetTET 2

4 7

26 25

8

P3 29

27

EntTET

24

28 23 30 14 22 16 17 13 15 19

P1 (December) 18 20

21

11 EcoCIP

20 21

19

SUL TET

17 16 14

18

15

13

27 25 26

Triclosan

P3 22

12

23 24

10

-1.0

-1.0

EntSUL

P2

PEN

P2

9

Axis 2 (8.4 %)

Axis 2 (20.5 %)

EntTOT

-1.0

-1.0

Axis 1 (39.2 %)

1.0

Fig. 3 e A, B. Correspondence Canonical Analysis (CCA) of the variation of 16S rRNA gene-DGGE patterns of the treated wastewater (scores variation of 0.653, of which 39.2% could be explained by axis 1 and 20.5% by axis 2) in function of the loads of cultivable (Total, TOT; Heterotrophs, Het; Enterobacteria, Ent; Enterococci, Eco; and those resistant to amoxicillin; ciprofloxacin, CIP; sulfamethoxazole, SUL; tetracycline, TET). Only the variables significantly ( p < 0.05) explaining the observed community variation are shown (EntTOT, p [ 0.002; EntTET and HetTET, p [ 0.008; HetTOT and HetCIP, p [ 0.006; EntSUL and EcoCIP, p [ 0.03). The specieseenvironmental correlations for axes 1 and 2 were, respectively, 0.901 and 0.865. 16S rRNA gene-DGGE bands with a fraction of variance over axes 1 and 2 higher than 0.5/1 are indicated by circles and squares, respectively. A. Representation DGGE bands distribution; B. Representation of samples distribution. Samples numbering: P1, 1e12; P2, 13e21; P3, 22e30, according to Fig. 1 legend.

Axis 1 (75.2 %)

1.0

Fig. 4 e A, B. Correspondence Canonical Analysis (CCA) of the variation of 16S rRNA gene-DGGE patterns of the treated wastewater (scores variation of 0.210 of which 75.2% could be explained by axis 1 and 8.4% by axis 2) in function of the concentrations of antimicrobial agents (Arsenic, p > 0.05; sum of all penicillins, PEN, p [ 0.002; sum of all quinolones, QUI, p [ 0.036; sum of all sulfonamides, SUL, p [ 0.016; sum of all tetracyclines, TET, p [ 0.002; triclosan, p [ 0.002). The specieseenvironmental correlations for axes 1 and 2 were, respectively, 0.992 and 0.921. Only the variables significantly explaining the observed community variation are shown and their inter-set correlation values are indicated. 16S rRNA gene-DGGE bands with a fraction of variance over axes 1 and 2 higher than 0.7/1 are indicated by circles and squares, respectively. A. Representation of DGGE bands distribution; B. Representation of samples distribution. Samples numbering: P2, 13e21; P3, 22e30, according to Fig. 1 legend.

w a t e r r e s e a r c h 4 7 ( 2 0 1 3 ) 1 8 7 5 e1 8 8 7

antibiotic residues present in wastewaters in this study would be sufficient to produce a measurable effect. Thus, it is possible that the failure to detect significant correlations between antimicrobial residues concentrations and the percentages of cultivable antibiotic resistant bacteria was due to the low sensitivity of the cultivation method, which produced very low variation scores. However, Graham et al. (2011), using qPCR, also did not find as many positive significant correlations between antibiotic resistance genes abundance and antimicrobials or metals concentrations as could be expected. The absence of direct associations between antimicrobials concentrations and antibiotic resistant bacteria in waste or contaminated waters may be due to the high stability of antibiotic resistant bacterial populations in the environment. Indeed, the success of antibiotic resistance in the environment may be due, in part, to the fact that it is not eliminated in the absence of known selective pressure factors (Andersson and Hughes, 2010). Nevertheless, environmental conditions are probably a major driving force on antibiotic resistance spread. For instance, in this study, maximal temperature was positively correlated with sulfamethoxazole resistance, and in the Spring periods (with higher temperatures) resistance was frequently more prevalent than in the Autumn. Also the increase of antibiotic resistance percentages after wastewater treatment was only observed in the Autumn period, which may result from the operational and/or climate conditions, in particular high precipitation. The influence of climate conditions on antibiotic resistance maintenance or proliferation in the environment is an issue which deserves major attention, since it may be related with differences observed in distinct geographic regions (e.g. Northern e Southern Europe; ECDC). Antibiotics may exert two types of effect on wastewater bacterial communities, the selection of antibiotic resistant bacteria and the failure of physiological functions important for the treatment process, such as ammonium and phosphorus metabolism and removal (Alighardashi et al., 2009; Louvet et al., 2010a,b; Singer et al., 2008). Only eight, out of the 20 bacterial populations (DGGE bands) identified, were observed to be correlated with the concentration of antimicrobials tetracyclines, penicillins, sulfonamides and triclosan or quinolones. Gamma, Betaproteobacteria and Firmicutes belong to the most represented groups in wastewater habitats (Zhang et al., 2012) and were residents in wastewater, detected in both the raw and treated wastewater over the three different periods. These groups (Gamma, Betaproteobacteria and Firmicutes) were negatively correlated with tetracyclines, penicillins, sulfonamides and triclosan. In contrast, positive correlations were found only for three bacterial populations, curiously all related with Epsilonproteobacteria (Table 2). It is even more curious to note that these bacteria are minor representatives of wastewater habitats (Zhang et al., 2012). These results suggest that Epsilonproteobacteria may represent an important target for antibiotics selection in wastewaters. Indeed, a positive selection could be speculated from the fact that two of these Epsilonproteobacteria populations (B2 and B8) were never detected in the wastewater inflow but only in the final effluent (Table 2). This class of bacteria, in particular members of the genus Arcobacter, often referred to as faecal contamination indicators, has gained visibility over the last years as opportunistic pathogens (Collado and Figueiras, 2011).

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Quinolones, known for their strong capacity to adsorb and poor partition in water (Golet et al., 2002), presented a pattern of correlation distinct from the other antimicrobials. Such a difference was noticeable in the biplots (Fig. 4) and confirmed by the fact that the only population that was correlated with the quinolones concentration (B1) was not correlated with the presence of tetracyclines, penicillins, sulfonamides and triclosan. Nevertheless, quinolone concentrations were correlated also with Epsilonproteobacteria (B1). Curiously, five, out of the eight treated wastewater bacterial populations correlated with antibiotic concentrations, were also correlated with the loads of antibiotic resistant bacteria. Antibiotic resistant heterotrophs or enterobacteria presented positive correlations with B1 and B8 (Epsilonproteobacteria), while negative correlations were observed for B4, B14 and B16 (Gammaproteobacteria, Bacteroidetes and Firmicutes). The fact that heterotrophs and enterobacteria have a different biology and ecology than enterococci may be the explanation for the intriguing result that bacterial populations positively correlated with ciprofloxacin resistant enterococci (B4, Gammaproteobacteria and B10, Betaproteobacteria), were negatively correlated with the ciprofloxacin resistant heterotrophs or tetracycline resistant enterobacteria. This is consistent with the different patterns of antibiotic resistance observed for enterobacteria, heterotrophs and enterococci (Ferreira da Silva et al., 2006, 2007; Novo and Manaia, 2010, Table 4), and puts in evidence how complex is the control of antibiotic resistance in wastewater habitats. The results obtained in this study suggest that the relationship between antimicrobial residues, bacterial community structure and composition and antibiotic resistance exists and is rather complex. Further studies, involving other wastewater treatment plants may help to elucidate this complex relationship.

5.

Conclusions

 The bacterial community structure was distinct in raw and in treated wastewater and varied over time;  Temperature and COD were correlated with the variation of the bacterial community structure of raw and treated wastewater, respectively;  For AML, TET and SUL, the antibiotic resistance percentages differed between Autumn and Spring periods;  In Autumn, but not in Spring, AML or CIP resistance prevalence increased significantly after wastewater treatment;  Temperature was positively correlated with the prevalence of sulfonamide resistant heterotrophs and enterobacteria in treated wastewater;  Tetracycline concentration in the raw wastewater was positively correlated with the antibiotic resistance prevalence in treated wastewater, although not specifically with tetracycline resistance;  The concentration of tetracyclines, penicillins, sulfamides and quinolones and the abundance of antibiotic resistant cultivable bacteria in the raw wastewater were positively correlated with the abundance of Epsilonproteobacteria in treated wastewater and negatively with Gamma, Betaproteobacteria and Firmicutes.

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Acknowledgments We gratefully acknowledge the staff wastewater treatment plant for their kind allowance and collaboration in sample collection. We also acknowledge Luisa Barreiros (LEPAE) for the help in the DGGE analysis and Sandra Fernandes (APA) for heavy metals determination, and the Instituto Portugueˆs do Mar e da Atmosfera for climate conditions data. This study was financed by Fundac¸a˜o para a Cieˆncia e a Tecnologia (project PTDC/AMB/71236/2006, Associate Laboratory LA50016).

Appendix A. Supplementary data Supplementary data related to this article can be found at http://dx.doi.org/10.1016/j.watres.2013.01.010.

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