FEMS Microbiology Ecology, 92, 2016, fiw060 doi: 10.1093/femsec/fiw060 Advance Access Publication Date: 11 April 2016 Research Article RESEARCH ARTICLE Serena Caucci1 , Antti Karkman2 , Damiano Cacace1 , Marcus Rybicki1 , Patrick Timpel3 , Veiko Voolaid1 , Robert Gurke4 , Marko Virta2 and Thomas U. Berendonk1,∗ 1 Institute for Hydrobiology, Technische Universität Dresden, 01217 Dresden, Germany, 2 Department of Food and Environmental Sciences, University of Helsinki, 00014 Helsinki, Finland, 3 Forschungsverbund Public Health Sachsen und Sachsen-Anhalt, Technische Universität Dresden, 01307 Dresden, Germany and 4 Institute of Clinical Pharmacology, Technische Universität Dresden, 01307 Dresden, Germany ∗ Corresponding author: Institute for Hydrobiology, TU Dresden, Drudebau Zellescher Weg, 40, 01062 Dresden, Germany. Tel: 351-463-4-2379; Fax: 351-463-3-7108; E-mail: thomas.berendonk@tu-dresden.de One sentence summary: Seasonal relationship between antibiotic prescriptions to outpatients and antibiotic resistance genes in sewers, treated and non-treated wastewater. Editor: Kornelia Smalla ABSTRACT To test the hypothesis of a seasonal relationship of antibiotic prescriptions for outpatients and the abundance of antibiotic resistance genes (ARGs) in the wastewater, we investigated the distribution of prescriptions and different ARGs in the Dresden sewer system and wastewater treatment plant during a two-year sampling campaign. Based on quantitative PCR (qPCR), our results show a clear seasonal pattern for relative ARGs abundances. The higher ARGs levels in autumn and winter coincide with the higher rates of overall antibiotic prescriptions. While no significant differences of relative abundances were observed before and after the wastewater treatment for most of the relative ARGs, the treatment clearly influenced the microbial community composition and abundance. This indicates that the ARGs are probably not part of the dominant bacterial taxa, which are mainly influenced by the wastewater treatment processes, or that plasmid carrying bacteria remain constant, while plasmid free bacteria decrease. An exception was vancomycin (vanA), showing higher relative abundance in treated wastewater. It is likely that a positive selection or community changes during wastewater treatment lead to an enrichment of vanA. Our results demonstrate that in a medium-term study the combination of qPCR and next generation sequencing corroborated by drug-related health data is a suitable approach to characterize seasonal changes of ARGs in wastewater and treated wastewater. Keywords: antibiotic resistance genes; seasonal; wastewater; antibiotic prescriptions; aquatic environment; WWTP INTRODUCTION Antibiotic resistance is a growing global problem (Levy and Marshall 2004; McKenna 2013) that scientists are still trying to fully understand. What is certain is that antimicrobial drug consumption is the major driver of antibiotic resistance in humanpathogens. The correlation between the antibiotic use and the Received: 31 August 2015; Accepted: 11 March 2016  C FEMS 2016. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com 1 Downloaded from https://academic.oup.com/femsec/article/92/5/fiw060/2470064 by guest on 30 November 2022 Seasonality of antibiotic prescriptions for outpatients and resistance genes in sewers and wastewater treatment plant outflow 2 FEMS Microbiology Ecology, 2016, Vol. 92, No. 5 MATERIALS AND METHODS Sewage and wastewater sampling The Dresden Kaditz WWTP processes wastewater for a loading capacity of 650 000 population equivalents, of which around 80% are living in Dresden. The catchment area has no known industries producing or applying significant amounts of antibiotics (e.g. industrial production or animal husbandry). The sewer system consists of about 800 km of combined sewers, around 450 km of a separate sewer system and 350 km for storm water runoff. In Dresden, the river Elbe divides the municipality into two; the sewer system of the city is therefore built accordingly. The sewage of the two areas reaches the Kaditz WWTP through separate channels (SEWER1, SEWER2). They join into one common sewer, which is the inflow (MIXED INFLOW) of the wastewater treatment. Wastewater is processed via activated sludge technology and the treated wastewater (OUTFLOW) is released into the river Elbe. The monitoring campaign started in April 2012 and ended in December 2013 (Fig. S0, Supporting Information). During this time, samples of wastewater (SEWER1, SEWER2, MIXED INFLOW) and treated wastewater (OUTFLOW) were taken at least once per season. In 2013, the sampling was intensified and more samples were collected in the seasons with higher temperature variability (spring and summer). Technical challenges encountered during the sampling and the flooding of the river Elbe in 2013 led to an uneven sampling over the two years. One litre of composite water samples were taken and stored at +4◦ C pending filtration within a few hours. All samples were taken in triplicates. Ambulatory and stationary antibiotic consumption The ambulatory prescription data were provided by the AOK PLUS, the largest statutory health insurance company in the State of Saxony, Germany. About 41% of the population in Dresden is insured with the AOK PLUS while the remaining part is being held by other statutory and private health insurance companies. Ambulant data for 2012 and 2013 were provided weekly. Data from three in-house hospital pharmacies were also available and estimated to cover about 65% of the hospital beds in the catchment area of the Dresden-Kaditz WWTP. Previous comparisons of the prescriptions for in- and out-patients showed a predominance of antibiotics prescribed in the ambulant (out-patient) sector with the exception of some antibiotics which were prescribed in both sectors (e.g. cephalosporins like cefuroxime and fluoroquinolones, like ciprofloxacin and levo-/ofloxacin) or predominantly prescribed in hospitals (e.g. cephalosporines, like cefotaxim and penicillins, like penicillin V). The data provided by the AOK PLUS health insurance refers to the principal thirteen substances (antibiotics) prescribed in Dresden and covering 80% of the total ambulatory prescriptions of the city. The pseudonymized data provided by the AOK PLUS include information about prescriptions and socio-demographic parameters (year of birth, place of residence). Information was available for the defined daily dose (DDD) indicating the assumed average maintenance dose per day for a drug used for its main indication in adults (Merlo, Wessling and Melander 1996). The number of DDD of a pharmaceutical is a unit of measurement providing an estimation of consumption. The total number of DDD was obtained by multiplying the prescribed amount of each antibiotic (active substance in grams) with the DDD. Special attention was paid to the exact quantification of mono and fixed dose combinations. An analysis of the pharmaceutical Downloaded from https://academic.oup.com/femsec/article/92/5/fiw060/2470064 by guest on 30 November 2022 levels of resistance has been demonstrated worldwide (Achermann et al. 2011; Hicks et al. 2011; Vernaz et al. 2011; Hutka and Bernard 2014; Suda et al. 2014) and used as an efficient proxy to estimate the threat of new resistant infections (Niederman 2001; Davies and Davies 2010). Within clinical studies, antibiotic resistance levels highly correlated with the corresponding prescription patterns (Lopez-Lozano et al. 2000; Lepper et al. 2002). Correlations between antibiotic prescriptions for outpatients and resistance profiles of hospital isolates have also been described (Hay et al. 2005; Gottesman et al. 2009). These findings lead researchers to the conclusion that strong seasonal variations in antibiotic use by outpatients might lead to seasonal changes in antibiotic resistance levels (Sun, Klein and Laxminarayan 2012). Selective pressure from antibiotics primarily takes place in human gut and after antibiotic administration the excreted resistant bacteria, together with non-metabolized antibiotics (Sun, Klein and Laxminarayan 2012; Marx et al. 2015), reach the wastewater treatment plant (WWTP). Sewers and WWTPs are the principal collectors of household and hospital wastes where mixtures of commensal and pathogenic bacteria together with the presence of selective agents create the optimal conditions for the selection of antibiotic resistance and spread of antibiotic resistance genes (ARGs), e.g. through horizontal gene transfer (Zhang, Zhang and Ye 2011). Moreover, WWTPs bridge the gap between anthropogenic and natural environments, thus allowing commensal and pathogenic bacteria, resistant and nonresistant, to reach the freshwater ecosystem. Because of the seasonality in antibiotic prescriptions and consumption, the concentration of antibiotic residues found in wastewater differ substantially within a year (Coutu et al. 2013; Marx et al. 2015) and can potentially influence the abundance of antibiotic resistant bacteria and genes present in the wastewater. Therefore, WWTPs represent a critical node to study the spread of antibiotic resistance, especially if treated wastewater is used as reclaimed water (Pruden et al. 2013). The role of antibiotics as a selective agent in a municipal WWTP has been shown before, (Gao, Munir and Xagoraraki 2012; McKinney and Pruden 2012) but to date, it is not known whether the level of resistance genes in wastewater has a similar seasonal pattern as does the prescription and consumption of antibiotics. Wastewater treatment technology is known to reduce the number of bacteria in the wastewater, (Schwartz et al. 2006; Laht et al. 2014; Alexander et al. 2015) but if and how the composition of microbial communities changes during treatment has not been extensively studied, especially in the context of antibiotic resistance. In this study, samples of wastewater and treated wastewater from Dresden municipality were taken seasonally over a two-year period and the levels of 11 resistance genes were measured via quantitative PCR (qPCR). In addition, we used Illumina Miseq sequencing to reveal the microbial community structure of the wastewater in sewers and the treated wastewater in the effluent of the WWTP. We also determined the community changes in relation to wastewater treatment and seasons. We tested the hypothesis that resistance gene level will undergo seasonal changes correlated with the consumption of antibiotics among outpatients. Therefore, higher antibiotic prescription rate should result in higher levels of resistance genes. We also hypothesize that the environmental seasonal changes will influence the microbial community composition of both the wastewater and the treated wastewater and therefore influence the antibiotic resistance gene copy numbers carried by the bacteria in the treated wastewater. In general, we expect that the treatment processes would reduce bacterial and antibiotic resistance gene numbers, absolute and relative. Caucci et al. registration number in combination with the total number of sold packages was used to validate the data (Timpel et al. 2016). Sample filtering and community DNA extraction mental settings. The ARGs levels in the sample were calculated using the standard curve equation and measured Ct value. The quality control of raw Ct values for standard curve and unknown samples was done before further analysis. The limit of quantification (LOQ) was defined as the lowest point on the linear part of the standard curve: for ARGs 10 and for 16S rRNA 100 gene copies per reaction. As negative control, the reaction mix with nuclease-free water as the template was used. Three biological and two technical replicates were incorporated in the statistical analysis. Total copy numbers of all target genes were normalized to ng of DNA extracted from the samples (ngDNA−1 ). Additionally, ARGs copy numbers were normalized to 16S rRNA gene copy numbers and to volume of water (gene copies mL−1 ). From here on, we refer to the concentrations of the normalized data (16S rRNA) as ‘relative ARGs levels’ and to the copy number in a 1 ml water as ‘absolute ARGs levels’. Detection and quantification of ARGs copy numbers A total of 11 resistance genes were surveyed: sul1, sul2, tetM, qnrB, blashv-34 , blactx-m-32 , blaoxa-58, vanA, blakpc-3 , MecA and dfrA1. The qPCR was performed using a Dynamo Flash SYBR Green qPCR kit (Thermo Scientific Neuendorfstraße 25, 16761 Hennigsdorf Germany) using an EPPENDORF realplex machine. The thermal cycling conditions were as follows: 95◦ C for 7 min, 40 cycles at 95◦ C for 10 s and Tm for 30 s. A melting curve was obtained to confirm amplification specificity. Reactions were conducted in 10 μL volumes on 96-well plates containing 1× Dynamo Flash SYBR Green master mix, 0.3 μM of each primer and 1 × ROX passive reference dye. Template DNA was used in qPCR reactions in the range of 2–12 ng; a fixed dilution of raw DNA extract was used. In parallel with the ARGs, the 16S rRNA gene copy numbers were quantified. Primers and annealing temperature, Tm (◦ C) are shown in Table S1 (Supporting Information). The number of technical replicates in the qPCR assay was two. Standards used for quantification A plasmid vector and fragments of ARGs were used as standards for quantifying the raw qPCR results (Laht et al. 2014; Muziasari et al. 2014 and this study). The 16S rRNA gene quantification standard was a genomic DNA from Escherichia coli K12 (genome size 4.6 Mbp with seven copies of the rRNA operon). Standard curves (Ct per log copy number) for quantification of 16S rRNA gene and ARGs were obtained for each run using the plasmid constructs (or genomic E. coli DNA for 16S rRNA in 10-fold serial dilutions). The gene copy number in the standard was determined via the plasmid/genomic DNA concentration [measured using VICTOR X3 Multilabel Plate Readers Perkin-Elmer; (Muziasari et al. 2014)]. Selection, quantification and normalization of ARGs All investigated genes were selected according to their clinical relevance and previous detection in preliminary screenings from the WWTPs in the region (data not shown). Furthermore, ambulatory prescriptions for antibiotics and the likelihood of the genes to be encountered on mobile elements were taken into account. Attention was given to genes conferring resistance to vancomycin (vanA) and high-levels of methicillin (MecA) as an indicator of contamination of hospital-related microorganisms or ARGs in aquatic environmental bacteria. In addition, Berendonk et al. (2015) have suggested many of these genes as possible indicators to assess the antibiotic resistance status in environ- Effect of seasons on ARGs levels and gene variability To determine the relationship between relative ARGs levels and seasons, years and plant treatment, the relative ARGs levels were standardized, square root transformed and assembled into a matrix. A non-metric multidimensional scaling analysis (NMDS) based on Bray–Curtis dissimilarity was calculated using R (version 3.0.1) and the vegan package (Oksanen et al. 2015). Furthermore, linear mixed models (LMM) were fitted to the ARGs data using the lme4 package in R (Bates et al. 2014). We predicted the relative level of ARGs using the genes, sampling location (SEWER1, SEWER2, MIXED INFLOW, OUTFLOW), season, sampling position (INFLOW, OUTFLOW) and sampling dates as predictors. Each predictor was used as fixed or as random effect depending on the applied model for the best prediction of the respective fixed effect. Detailed information on the LMM is contained in Supporting Information (S5a). The model selection was performed using the maximum likelihood estimator. Parameter estimation was performed using the restricted maximum likelihood estimator. Model quality was assessed by model comparison using likelihood ratio tests and the Akaike Information Criterion (AIC). Values below LOQ were set to a standardized gene amount of 5e−8 , which is an order of magnitude below LOQ. Microbial community sequencing and data analysis The analysis of microbial community of the sewage and wastewater samples was performed on the previously extracted DNA and sequenced by MiSeq Illumina technology (300 bp paired end) on the variable region V1–V3 of 16S rRNA gene at the GATC Biotech AG (GATC Biotech AG Constance, Germany) (www.gatc-biotech.com). In detail, 43 DNA samples were used as a template in PCR reactions. This resulted in amplicons of the size of 471 bp; the used primers were 27FAGAGTTTGATCCTGGCTCAG and 534R-ATTACCGCGGCTGCTGG (Methe et al. 2012). The paired end sequences were merged based on overlapping bases using FLASh (Magoc and Salzberg 2011) with maximum mismatch density of 0.25. Sequences were quality trimmed using the MOTHUR software package (Schloss et al. 2009). To minimize the effect of random sequencing errors, both the low quality fragments and the sequences shorter than 280 bp were removed. The PCR chimeras were checked and filtered out by UCHIME (Edgar et al. 2011). Each sample was randomly re-sampled and normalized at 25 230 sequences. The taxonomic assignment of operational taxonomic units (OTUs) was Downloaded from https://academic.oup.com/femsec/article/92/5/fiw060/2470064 by guest on 30 November 2022 Water samples were stored at +4◦ C pending filtration (within a couple of hours). A total of 20 mL of wastewater (SEWER1, SEWER2 and MIXED INFLOW) and 50 mL of treated wastewater (OUTFLOW) were filtered through polycarbonate filters (pore size 0.22 μm, diameter 47 mm, GE Water and Process Technologies). R DNA isolation kit DNA was extracted using MoBio PowerWater (MoBio Laboratories, Inc., CA, USA). Total DNA was recovered in 50 μL of elution buffer. The concentration of extracted DNA was measured using a NanoDrop Spectrophotometer ND-1000 (absorption readings at 260 nm). The extracted DNA was stored at −20◦ C pending further analysis. 3 4 FEMS Microbiology Ecology, 2016, Vol. 92, No. 5 Canonical correspondence analysis (CCA) was calculated with R (package vegan) using the taxonomic data as well as the ARGs to identify correlations between species, genes and the respective sampling area (MIXED INFLOW, OUTFLOW). The CCA was calculated using the Taxonomic data as the species matrix and the absolute ARGs levels as environmental matrix. RESULTS AND DISCUSSION Selection of ARGs and aim of the study The central goal of our study was to assess the presence and levels of multiple ARGs in the Dresden sewer system and municipal WWTP inflow and outflow during a two-year monitoring campaign (2012–2013). For this, samples were taken at least once per season. In 2013, the sampling was intensified and more samples were collected in the seasons with higher temperature variability (spring and summer; Fig. S0, Supporting Information). The samples were filtered and the DNA extracted for a microbial community analysis using MiSeq and for the quantification of ARGs with qPCR (more detailed description in Materials and Methods). To correctly assess the ARGs variability of a complex environment such as the urban wastewater system, we considered: (i) the seasonal differences of ARGs levels, (ii) the spatial variations analysed as the relationships between ARGs levels in the wastewater and treated wastewater, (iii) the composition of the microbial community in the water in time and space. Eight out of eleven genes (blactx-m-32 blaoxa-58 , blashv-34 , dfrA1, sul1, sul2, tetM, vanA), (Fig. 1) were detected in all sampling locations. blakpc-3 , qnrB and mecA were not quantified because they were either not present or their level was under the detection limit. Sul1, sul2 and tetM were more abundant both in wastewater and treated wastewater, with the relative level of sul1 Figure 1. Antibiotic resistance genes (ARG) copy numbers normalized to 16S rRNA gene copy numbers in wastewater (SEWER1, SEWER2, MIXED INFLOW) and treated wastewater (OUTFLOW) of Dresden WWTP. The line in each box marks the median; boxes: 25th and 75th percentiles; whiskers: 5th and 95th percentiles; and q: outlier ±1.5. The gene concentration below the detection limit is indicated with ‘∗ ’. Sampling points in spring 2012 and autumn 2012 are missing because of logistical problems (e.g. flooding of the sewer, see also Supporting Information S0). Downloaded from https://academic.oup.com/femsec/article/92/5/fiw060/2470064 by guest on 30 November 2022 performed by RDP classifier at 80% threshold in MOTHUR (Wang et al. 2007). The sequences have been deposited in the NCBI Short Read Archive under accession number SRP067506. Qualified sequences were assigned to OTUs based on a 97% sequence similarity. Taxonomic classification was based on NCBI Taxonomy http://www.ncbi.nlm.nih.gov/taxonomy. The Shannon diversity index (H’) was calculated to estimate the diversity in the different samples. The Bray–Curtis dissimilarity index, which takes into account diversity and taxa differences between locations, was used to compare beta diversity among samples. The parsimony test, as implemented by MOTHUR, was used to assess whether two or more communities have the same composition. A Bonferroni correction was applied to adjust the significance level for multiple pairwise comparisons (P ≤ 0.05). Permutational multivariate analysis of variance (Adonis), and analysis of similarity, were performed with Bray–Curtis dissimilarity index using the R environment (version 3.1.0; http://www.r-project.org/). Genera abundances were standardized, square root transformed and assembled into a matrix to generate a multidimensional scaling (NMDS) plot to assess similarities among samples in a two-dimensional space and determine the relationship between genera according to seasons, years or plant treatment. Because of the community similarity between the wastewater (SEWER1, SEWER2 and MIXED INFLOW), the differentially most abundant genera between untreated and treated wastewater (OUTFLOW) were calculated only between INFLOW MIX and OUTFLOW samples. Calculations have been performed for all samples of the MIXED INFLOW + OUTFLOW dataset. The differentially most abundant taxa on seven different taxonomic levels (OTU, species, genus, family, order, class and phylum) were instead calculated in R version 3.2.1. with the phyloseq DESeq2 package (Anders and Huber 2010). The difference in features between MIXED INFLOW and OUTFLOW were calculated taking the time into consideration [∼Area + Time (season + year)]. Caucci et al. Table 1. Gene copy numbers of 16S rRNA normalized to DNA content (per ng of DNA) for wastewater (SEWER1, SEWER2, MIXED INFLOW (M. INFLOW)) and treated wastewater (OUTFLOW). The here represented concentrations are averaged over biological triplicates and duplicate measurements. 5 autumn with 6.90E + 05 copies ng−1 for wastewater and 1.51E + 04 copies ng−1 for treated wastewater (Table 1). Antibiotic prescription analysis 2012 2013 16S rRNA copies nr ng-1 Season SEWER1 SEWER2 M.INFLOW OUTFLOW SPRING SPRING SPRING SPRING 1.28E + 07 1.24E + 07 – 2.98E + 06 6.93E + 07 1.77E + 07 5.64E + 07 6.38E + 06 SEWER1 SEWER2 M.INFLOW OUTFLOW SUMMER SUMMER SUMMER SUMMER 9.60E + 06 2.13E + 05 5.43E + 05 1.89E + 04 1.27E + 07 1.73E + 07 1.38E + 07 1.27E + 06 SEWER1 SEWER2 M.INFLOW OUTFLOW AUTUMN AUTUMN AUTUMN AUTUMN – – 6.90E + 05 1.51E + 04 6.37E + 05 7.96E + 05 1.06E + 06 5.44E + 04 SEWER1 SEWER2 M.INFLOW OUTFLOW WINTER WINTER WINTER WINTER 8.01E + 05 4.22E + 06 5.32E + 05 3.75E + 04 5.59E + 06 7.75E + 06 3.51E + 06 5.24E + 05 being always the highest (Figs S2 and 3, Supporting Information). The higher relative ARGs levels and absolute amount of sul1, sul2 and tetM can be explained with a longer history of usage of sulphonamide and tetracycline antibiotics (Pruden et al. 2006; Heuer, Schmitt and Smalla 2011) while plasmid-mediated quinolone resistance encoded by qnrB was first reported only in 2006 (Jacoby et al. 2006). The bacterial numbers fluctuated between seasons (Table 1; Fig. S4, Supporting Information) and between the wastewater and treated wastewater (Table 1). The highest level of 16S rRNA copies was found in spring; for wastewater 6.93E + 07 copies ng−1 and for treated wastewater 6.38E + 06 copies ng−1 . The lowest level of 16S rRNA copies was found in The antibiotic prescription levels were similar for the two investigated years (data not shown). As expected, the total antibiotic prescriptions followed the known seasonal trend (Sun, Klein and Laxminarayan 2012; Hutka and Bernard 2014). The amount of all prescribed antibiotics by the city´s outpatients was always higher in the cold months compared to the warm ones (Fig. 3) and the total consumption of antibiotics extrapolated by the correspondent antibiotic prescriptions ranged between 26 kg (August 2012) and 47 kg (January 2013) per month. While some antibiotic substances confirmed the general seasonal pattern (macrolides), others were either evenly prescribed over the year (quinolones, ß-lactams) or had no specific pattern, like for cephalosporins, penicillin, sulfamethoxazole-trimethoprim and lincosamides. Examples of antibiotics showing different prescription patterns are presented in Fig. 3. Seasonal trends of ARGs levels The relative ARGs levels were significantly different between seasons (Fig. 2; analysis of variance function adonis: R2 = 15% P < 0.0001, Fig. S5b, Supporting Information, panel 1) and the seasonal differences for the overall relative ARGs levels were independent from the location and year (P < 0.0001). Therefore, seasonality strongly influenced the relative level of ARGs in wastewater. Seasonality is also a robust phenomenon in the antibiotic prescriptions for outpatients (Suda et al. 2014). A higher number of antibiotics are prescribed by physicians in the cold months of the year (Sun, Klein and Laxminarayan 2012) when low temperatures contribute to the increase of infections from disease causing bacteria. It is known that selection of resistant bacteria can occur during or after antimicrobial treatment and the quantity of antibiotic consumption contributes to the release of ARB into sewage water by individual excretions (Rizzo et al. 2013). Figure 2. Spatiotemporal changes in relative antibiotic resistance gene (ARG) levels before and after wastewater treatment. Non-metric multidimensional scaling (NMDS) plot based on Bray–Curtis index of ARG in wastewater (SEWER1, SEWER2, MIXED INFLOW) and treated wastewater (OUTFLOW) of Dresden municipality. Symbols indicate samples of wastewater and treated wastewater for the year 2012 and 2013. Downloaded from https://academic.oup.com/femsec/article/92/5/fiw060/2470064 by guest on 30 November 2022 Area 6 FEMS Microbiology Ecology, 2016, Vol. 92, No. 5 In our study, the relative ARGs levels encountered in the Dresden wastewater showed a corresponding pattern with the temporal analysis of the antibiotic prescriptions for outpatients (Figs 1 and 3). ARGs levels were indeed higher in autumn and winter (Fig. 1), in correspondence with the yearly peak of antibiotic uptake. Similarly, the low level of antibiotic prescriptions in spring compared to autumn and winter corresponded to low levels of ARGs in the wastewater in the same season. Partial disagreements were shown in the summer where the low rate of antibiotic prescriptions corresponded to variable ARGs levels. The partial discordance between the rate of prescription and the ARGs levels in the wastewater could be explained by the different seasonal dynamics of prescriptions of individual antibiotic substances (Suda et al. 2014). Indeed in Dresden antibiotics like cephalosporins, penicillin, sulfamethoxazole–trimethoprim and lincosamides have no marked patterns of prescription (data not shown) and therefore their contribution to the levels of certain ARGs (e.g. shv34, sul1) in the wastewater may vary according to the year. The overall amount of prescribed antibiotics influences the amount of antibiotics and antibiotic residues in the wastewater and thus may contribute to the overall selective pressure on the resistant bacterial fraction over the year. The seasonal proportions of prescribed antibiotics were mostly comparable to the ones analysed by Hutka and Bernard (2014) for the years 2005–2011, thus revealing a similar uptake of antibiotic classes by the outpatients over the years. Marx et al. (2015) performed at the same WWTP a measurement of a variety of antibiotics in wastewater and detected them in therapeutic and subtherapeutic concentrations (Marx et al. 2015). This suggests that the selective pressure by antibiotics and antibiotic residues could lead to the selection and co-selection of ARGs (Michael et al. 2013) in the wastewater. As a consequence, ARGs levels might not always correspond, in proportion, to the outpatient antibiotic prescription rates. Therefore, even if ARGs levels in wastewater are significantly different between seasons, the variation of ARGs might not only be limited by the practitioner’s prescriptions but it is likely that other processes are involved (e.g. ecological processes) in the sewers or within the WWTP (Berendonk et al. 2015). In fact, season-dependent environmental factors, like temperature or precipitation of suspended solids in the sewers, have been shown to interfere with the proliferation or survival of microbial taxa that mainly harbour ARGs or promote the growth of microorganisms which do not contain any of the tested resistance genes (Biggs et al. 2011; VandeWalle et al. 2012; LaPara et al. 2015). It is therefore conceivable that even small environmental alterations affecting relevant taxa in a microbial community (carrying ARGs) may lead to differences in ARGs abundances. Generally, the data support the notion that the impact of prescription rate of single classes of antibiotics in Dresden on seasonal selection or persistence of ARGs in municipal wastewater warrants further study. Spatial variations between ARGs levels in wastewater and treated wastewater Copy numbers of 16S rRNA in SEWER1, SEWER2 and MIXED INFLOW were one or two orders of magnitude higher than in the respective OUTFLOW (Table 1). Differences between the wastewater and treated wastewater were statistically significant in all seasons (P < 0.01). No significant differences in the overall relative ARGs levels were detected between SEWER1, SEWER2 and MIXED Downloaded from https://academic.oup.com/femsec/article/92/5/fiw060/2470064 by guest on 30 November 2022 Figure 3. Sum of monthly prescribed antibiotics (in Kg) by the AOK health insurance for the year 2012 and 2013 in Dresden, Germany. Blue bars: monthly antibiotic prescriptions of 13 antibiotics; red line: monthly amoxicillin prescriptions (ß-lactam with a seasonal prescription over the year); yellow line: monthly clindamycin prescriptions (lincosamide with no marked pattern); orange line: monthly prescriptions of the macrolides azi-, clari- and roxythromycin (seasonal pattern); green line: monthly prescriptions of penicillin V (ß-lactam with no marked pattern). The illustrated antibiotics (lines) are included in the overall monthly prescription of antibiotics. Numbers 1–12 in the x-axis refer to the calendar months January–December. Caucci et al. ARGs seasonal variations The relative gene abundance of single genes varied within the samples (Table S5b, Supporting Information panel 2) and their variability was explained by the gene type and the season. The highest variation of relative gene abundance was shown for sul1 while genes blactx-m32, blaoxa58 and vanA did not show strong gene level variation between seasons. For all the genes, a higher relative level was always encountered in the colder months, either in autumn or in winter (Fig. S5b, Supporting Information, panel 3). The analysis of a complex linear mix model (Table S5a, Supporting Information, AIC: −960.6) indicated an effect of the sampling location (MIXED INFLOW, OUTFLOW) on a single ARGs for most of the genes (with the exception of vanA). No stable difference between the relative ARGs levels (ARG) of the treated wastewater and wastewater could be shown to support either a constant reduction or increase of the relative ARGs levels in the OUTFLOW over the year (Fig. S5b, Supporting Information, panel 4). Therefore, we conclude that although the wastewater treatment processes might have an effect on single ARGs, the effect is always gene specific and season dependent. Seasonality effect on microbial community composition The analysis of the qPCR results suggested that the difference in ARGs levels was attributed to season and not to wastewater treatment processes, prompting us to explore the effect of seasonality on the bacterial community composition of wastewater and treated wastewater. The total number of raw reads for the 43 investigated samples was 11 238 980. After trimming and joining the read pairs, the number of reads was 5 929 221 and sequence clustering yielded a total of 5590 OTUs, each containing sequences that shared at least 97% identity. Contrary to the qPCR results on ARGs, wastewater (SEWER1, SEWER2 and Figure 4. Spatiotemporal changes in wastewater community structures before and after wastewater treatment. Non-metric multidimensional scaling (NMDS) plot based on Bray–Curtis index of microbial community inhabiting the wastewater (SEWER1, SEWER2, MIXED INFLOW) and treated wastewater (OUTFLOW) of Dresden municipality. Symbols indicate of wastewater and treated wastewater samples for the year 2012 and 2013. MIXED INFLOW) and treated wastewater (OUTFLOW) resulted in two distinct microbial communities (Fig. 4, adonis R2 : 13%, P < 0.001). The year and season effect was not statistically significant for discriminating microbial communities from the same sampling location (P = 0.8) and water samples of the same season formed no temporally distinct clusters. A total of 85%–90% of the highly diverse bacterial community in all samples was represented by the phyla Actinobacteria, Bacteroidetes, Firmicutes and Proteobacteria (Supporting Information S6). Although the overall microbial community did not change significantly within the samples of the same location over the seasons, minor changes can be noticed. This is especially evident for SEWER2, MIXED INFLOW and OUTFLOW in springsummer 2013. During this sampling period, extreme rainfall events led to a massive flooding in the city. The elevated volume of rain water in the sewers is the likely cause for both high wastewater dilution (MIXED INFLOW) and detachment of the biofilm coating (SEWER2). Such disturbances are in our opinion the reason for a diverse microbial community in the inflow (Fig. S6, Supporting Information). On the other hand, a safety system of the WWTP (combined sewer overflow) allows the wastewater to bypass the plant, which contributed to a similar microbial community composition between the treated wastewater (OUTFLOW) and the wastewater (SEWER2, MIXED INFLOW; Fig. 4). Compared to wastewater, the treated wastewater displayed a higher phylogenetic diversity (P < 0.001) and higher variation among temporal replicate samples (more OTUs in winter and summer). This might be an artefact caused by a higher number of redundant sequences (abundant taxa) in the wastewater compared to treated wastewater. The richness of genera among the wastewater samples did not differ significantly (Fig. S7, Supporting Information). In general, the microbial diversity in the treated wastewater was higher than that in the wastewater in all seasons except for spring when the diversity was lower. In this case, the lower diversity was not due to the reduced bacterial abundance because among all the treated wastewater samples, the treated wastewater (OUTFLOW) in spring displayed the highest microbial abundance (Table 1). Downloaded from https://academic.oup.com/femsec/article/92/5/fiw060/2470064 by guest on 30 November 2022 INFLOW (adonis 9999 permutations P = 0.7). Previous studies have shown that sewers can act as a reservoir for resident organisms (Chen, Leung and Hung 2003; Leung, Chen and Sharma 2005) and that the in situ diversity of biofilm forming bacteria does not differ between different sewers even if geographically distant (McLellan et al. 2010). In light of this knowledge, part of the ARGs present in the wastewater could be restrained by sewers through the resident microorganisms and hence potentially influence their abundance in MIXED INFLOW reaching the WWTP. The comparison of relative ARGs levels between the different parts of the wastewater (SEWER1, SEWER2 and MIXED INFLOW) revealed no significant differences (P < 0.01). Because of this, the spatial variation analysis of ARGs was carried out only between MIXED INFLOW, as representative of wastewater, and the OUTFLOW. No significant differences between the overall relative ARGs levels of MIXED INFLOW and OUTFLOW (adonis, 9999 permutations) were found for the two investigated years. With the notable exception of vanA which was constantly enriched in the OUTFLOW (Fig. 1; Table S2, Supporting Information, P < 0.001), the relative copy numbers of ARGs did not decrease significantly during treatment (Fig. 1, P < 0.0001 adonis, 9999 permutations). While previous studies have already demonstrated that the relative number of resistance genes were not reduced during the treatment processes in WWTP (Laht et al. 2014; Alexander et al. 2015), to our knowledge, only one study has previously demonstrated an enrichment of vanA in the treated wastewater (Alexander et al. 2015). 7 8 FEMS Microbiology Ecology, 2016, Vol. 92, No. 5 Relationship between microbial community composition and ARGs Canonical correspondent analysis showed that despite the distinct community differences between treated wastewater and wastewater, fitting of absolute ARGs levels to the wastewater bacterial community composition (MIXED INFLOW,OUTFLOW) showed a weak correlation for most of the investigated ARGs. This means that the variation of the ARGs levels cannot be explained by the shift of the microbial community from wastewater to treated wastewater (Fig. 5). Indeed, as for the microbial community inhabiting the wastewater and treated wastewater, genera characterizing the OUTFLOW (differently abundant genera, Fig. S9, Supporting Information) did not correlate with the majority of the ARGs, indicating that the ARGs are also widely distributed among these genera. However, vectors fitted for vanA and sul1 indicated a partially different distribution of the microbial community in the OUTFLOW. The responsible process for the non-reduction of ARGs in the OUTFLOW is still unclear for most of the investigated genes but in the case of vanA, an explanation is possible. The analysis of the 30 dominant genera in OUTFLOW and MIXED INFLOW (Table S8a and b, Supporting Information) revealed a presence of the Enterococcus genus which is an opportunistic pathogen known to carry the clinically relevant vanA (Gilmore, Lebreton and van Schaik 2013). The WWTP processes high abundances of bacteria belonging to the genus Enterococcus in wastewater, which suggests that mechanisms of selection rather than HGT are mainly involved in the enrichment of vanA in the treated wastewater. This result is worrying, because the persistence and the enrichment of vanA in the treated wastewater could lead to a permanent contamination of the receiving river sediments and therefore potentially spread the resistance for one of the last resort antibiotics (Narciso-da-Rocha et al. 2014). Effect of WWTP processes on absolute ARGs levels The wastewater contained substantial quantities of each of the investigated ARGs (Fig. 5). As in the case of relative ARGs levels, absolute gene levels of sul1, tetM and sul2 dominated in the microbial community in both years with a concentration of ∼107 –108 gene copies mL−1 . In contrast, the concentration of Figure 6. Absolute antibiotic resistance gene (ARG) levels (copy numbers ml−1 ) in wastewater (MIXED INFLOW, filled symbols) and treated wastewater (OUTFLOW, open symbols). Absolute ARG levels were plotted separately for the year 2012 and 2013. Data are displayed chronologically. Error bar: standard deviation. In 2013, the levels for shv34 and tetM overlap and therefore the symbols overlap as well. blactx32 , dfrA1, blaoxa58 , blashv34 was ∼106 gene copies mL−1 , and the concentration of vanA was the lowest with ∼104 gene copies mL−1 . In 2012, the absolute levels of ARGs in the treated wastewater decreased two to three orders of magnitude with the exception of vanA which decreased in only one order of magnitude (Fig. 6). The WWTP was capable to reduce the absolute quantities of ARGs from the wastewater. This reduction related directly to the bacterial removal rate which is commonly observed in conventional WWTPs. In 2013, although a similar reduction was observed, the wastewater treatment processes did not reduce the blashv34 and tetM as much as in the previous year. While for blashv34 the nonreduction was constant over the year, for tetM the reduction was not observed in some seasons. These results show that the influences of the wastewater treatment on the reduction of the ARGs levels (absolute and relative) are strongly dependent on the season and type of ARGs. CONCLUSIONS Seasonality of ARGs in wastewater has often been part of a scientific debate but the necessary data for sound conclusions are still missing. We present evidence for a seasonal pattern of ARGs in the wastewater of Dresden, which was independent from the treatment processes and microbial composition of the wastewater, but related to the seasonality of the prescription level of antibiotics in the city. Neither the transport through the sewers nor the treatment of wastewater resulted in a reduction of the relative level of ARGs. Contrasting to this, the analysis of the microbial community showed clear differences in the dominant taxa between wastewater and treated wastewater. Therefore, the analysis of ARGs and the community suggests that the bacteria carrying the resistance genes are either not part of the dominant microbial community, or that processes such as HGT disconnected the community and ARGs profiles of the treated wastewater. Investigations focusing on ARGs carrying bacteria in the WWTP certainly require special attention and deeper investigation, but for that a different experimental approach would be needed and a broader spectrum of ARGs investigated. Advanced methods as the recently published EPIC-PCR (Spencer et al. 2015) would allow linking ARGs to the species carrying the resistance gene and Downloaded from https://academic.oup.com/femsec/article/92/5/fiw060/2470064 by guest on 30 November 2022 Figure 5. Canonical Correspondence Analysis (CCA) of MiSeq community profiles from wastewater (MIXED INFLOW) and treated wastewater (OUTFLOW) with passively fitted relative antibiotic resistance gene (ARG) abundance. Arrows indicate the magnitude of measurable variables associated with bacterial community structures. Red crosses: the bacterial genera present in treated and untreated water; green triangles: differently abundant genera between treated wastewater and wastewater (Supporting Information S9); open circles: MIXED INFLOW; filled circles: OUTFLOW. vanA and sul1 accounted respectively for 30% and 24% of the variation of microbial community and correlated positively with the OUTFLOW. Caucci et al. could be used to investigate directly the dynamics of ARGs and the corresponding resistant bacteria. SUPPLEMENTARY DATA Supplementary data are available at FEMSEC online. ACKNOWLEDGEMENTS FUNDING This project is funded by ANTI-Resist 02WRS1272A; Bundesministerium für Bildung und Forschung (BMBF) within the Framework Concept ‘Risk Management of Emerging Compounds and Pathogens in the Water Cycle (RiSKWa)’. AK and MV were funded by Academy of Finland. TUB, MV,VV and DC acknowledge the funding of the ‘support the best’ program of the DFG (Deutsche Forschungsgemeinschaft) of the Technische Universität Dresden as well as the funding of the JPI water initiative within the program StARE (BMBF 02WU1351A) SC and TUB acknowledge the funding of the DAAD (Deutscher Akademischer Austauschdienst) within the program for the bilateral cooperation project PPP-FINLAND 2015-2016 (ID 57163214). Conflict of interest. None declared. REFERENCES Achermann R, Suter K, Kronenberg A et al. Antibiotic use in adult outpatients in Switzerland in relation to regions, seasonality and point of care tests. Clin Microbiol Infec 2011;17:855–61. Alexander J, Bollmann A, Seitz W et al. Microbiological characterization of aquatic microbiomes targeting taxonomical marker genes and antibiotic resistance genes of opportunistic bacteria. Sci Total Environ 2015;512:316–25. Anders S, Huber W. Differential expression analysis for sequence count data. 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