Pyrosequencing analysis of bacterial diversity in dental unit waterlines

Water Research 81 (2015) 223e231

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Pyrosequencing analysis of bacterial diversity in dental unit waterlines
ro ^ me Lesobre d, Vincent Delafont a, Damien Costa a, b, *, Anne Mercier a, Kevin Gravouil c, Je b a a, b Anne Bousseau , Julien Verdon , Christine Imbert a Equipe Microbiologie de l'Eau, Ecologie et Biologie des Interactions, Centre National de la Recherche Scientifique UMR 7267, Universit
e de Poitiers, Poitiers, France b ^ Batiment Urgences et Biologie M
edicale (UBM), CHU de Poitiers, Poitiers, France c Laboratoire coop
eratif ThanaplastSP-Carbios Bioplastics, Ecologie et Biologie des Interactions, Centre National de la Recherche Scientifique UMR 7267, Universit
e de Poitiers, Poitiers, France d Universit
e Blaise Pascal, UMR CNRS 6023, Laboratoire Microorganismes: G
enome et Environnement, 24 avenue des Landais, Aubi ere, France

a r t i c l e i n f o

a b s t r a c t

Article history: Received 5 January 2015 Received in revised form 22 May 2015 Accepted 31 May 2015 Available online 3 June 2015

Some infections cases due to exposure to output water from dental unit waterlines (DUWL) have been reported in the literature. However, this type of healthcare-associated risk has remained unclear and up until now the overall bacterial composition of DUWL has been poorly documented. In this study, 454 high-throughput pyrosequencing was used to investigate the bacterial community in seven dental offices (N ¼ 7) and to identify potential bacterial pathogenic sequences. Dental unit waters (DUW) were collected from the tap water supplying units (Incoming Water; IW) to the output exposure point of the turbine handpiece (Output water; OW) following a stagnation period (OWS), and immediately after the last patient of the sampling day (OWA). A high bacterial diversity was revealed in DUW with 394 operational taxonomic units detected at the genus level. In addition to the inter-unit variability observed, results showed increased total bacterial cell concentration and shifts in bacterial community composition and abundance at the genus level, mainly within the Gamma- and Alpha-Proteobacteria class, as water circulated in the dental unit (DU). Results showed that 96.7%, 96.8% and 97.4% of the total sequences from IW, OWS and OWA respectively were common to the 3 defined water groups, thereby highlighting a common core microbiome. Results also suggested that stagnation and DU maintenance practices were critical to composition of the bacterial community. The presence of potentially pathogenic genera was detected, including Pseudomonas and Legionella spp. Emerging and opportunistic pathogenic genera such as Mycobacterium, Propionibacterium and Stenotrophomonas were likewise recovered in DUW. For the first time, an exhaustive evaluation of the bacterial communities present in DUW was performed taking into account the circulation of water within the DU. This study highlights an ignored diversity of the DUWL bacterial community. Our findings also contribute to a better appreciation of the potential infectious risk associated with dental care and suggest the importance of better managing microbial quality in DUW. © 2015 Elsevier Ltd. All rights reserved.

Keywords: Dental unit waterlines (DUWL) Bacterial contamination Pyrosequencing Healthcare Pathogens

1. Introduction Dental Unit Waterlines (DUWL) appear to be regularly contaminated with microorganisms, ranging from 102 to 108 CFU (colony forming units)/mL of water (Arvand and Hack, 2013;

* Corresponding author. Equipe Microbiologie de l'Eau, Ecologie et Biologie des
de Interactions, Centre National de la Recherche Scientifique UMR 7267, Universite ^le Biologie Sante
, 1 rue Georges Bonnet, ba ^t B37, 86073 Poitiers, Poitiers, UFR SFA, Po cedex 9, France. E-mail address: [email protected] (D. Costa). http://dx.doi.org/10.1016/j.watres.2015.05.065 0043-1354/© 2015 Elsevier Ltd. All rights reserved.

Barbeau et al., 1996; Barbot et al., 2012; O'Donnell et al., 2011;
ska et al., 2008). This type of DUWL contamination is Szyman associated with a wide variety of microorganisms ranging from non-pathogenic to pathogenic bacteria, fungi, viruses and protozoa
ska, such as free-living amoebae (Puttaiah et al., 2012; Szyman
ska et al., 2008; Walker et al., 2000). Moreover, 2005; Szyman while most of these microorganisms come from water supply, species of human origin such as oral streptococci have also been detected, thereby contributing to the uniqueness of this ecosystem (Petti et al., 2013). The microbial contamination of DUWL can occur in three main ways: i) by the incoming water of a dental chair unit,

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which is usually municipal water, ii) by the suck-back of biological fluids from the oral cavities of patients and iii) by the proliferation of all these microorganisms, which can form biofilms in DUWL. Indeed, DUWL with: a high surface/volume ratio (6/1), laminar flow resulting in maximum flow at the center of the lumen and minimal flow at the periphery and/or frequent water stagnation periods occurring during nights, weekends and holidays; provide a conducive environment to microbial attachment and biofilm formation (Barbot et al., 2012; Szymanska, 2003). As a result, detachments of clusters from biofilm can heighten DUWL microbial contamination over the acceptable level. Despite the presence of potentially high microbial contamination and pathogenic microorganisms in DUWL, only few cases of bacterial infections among patients and care personnel have been described (Martin, 1987; Ricci et al., 2012). However, infections could be underestimated, particularly in terms of number of case reports because the epidemiological links between infection and recent exposure to contaminated dental water and/or aerosols are poorly investigated and difficult to confirm. Nevertheless, a case report was published in 2012 concerning a healthy 82-year-old italian woman who developed Legionnaire's disease after dental healthcare appointments (Ricci et al., 2012). In addition, several studies have investigated the prevalence of anti-Legionella antibodies in dental workers and put forward some controversial observations (Borella et al., 2008; Pankhurst, 2003; Szymanska, 2004). Taken together, these data reinforce the importance of monitoring the microbiological quality of the water used for dental care. In France, as in some other European countries, it has been recommended that the water delivered to dental units be kept at or below existing guidelines for potable drinking water with detected heterotrophic bacteria at less than 100 CFU/mL at 22  C (1998, res des solidarite
s and DGS/DHOS, 2005; Kumar et al., 2010; Ministe
ska et al., 2008). Incoming water usually complies with Szyman standards but is often treated by softeners, filters or chemical treatments before supplying DUWL, in order to protect lines against scaling (Robert et al., 2013). Such treatment may alter the microbial water quality in case of insufficient maintenance, especially when filtration is performed. Surprisingly, no standard specifically focuses on the microbial quality of the output water delivered by handpieces to patients in most European countries. In contrast, the corresponding regulations in USA from the American Dental Association (ADA) and the Center for Disease Control and Prevention (CDC) specify that aerobic heterotrophic microbial density of output dental water should be inferior to 500 CFU per mL (American Dental Association, 2012; Coleman et al., 2014). In this study, seven DUWL, representative of both hospital-based and private dental offices of the region of Poitiers (France) were studied under routine conditions. The aim was to investigate the bacterial biomass and composition in DUWL according to the flow from the water supply (incoming water) to the outer exposure point (output water). Shifts in bacterial community composition during their water routes were also investigated. Taken as a whole, these data should help to better characterize and evaluate the risk of exposure of both patients and dental staff to potential human pathogens. Dental Unit Water (DUW) samples were analyzed by 454 pyrosequencing, which allowed for rapid and massive production of targeted DNA sequences (Glenn, 2011). 2. Material and methods 2.1. Dental unit water sampling Seven dental units were chosen from hospitals (4 units) and public dental clinics (3 units) in the region of Poitiers (France), with no a priori selective criteria (Table 1) and based on voluntary

Table 1 Characteristics of studied DU. Dental units (N ¼ 7) n Origin Hospital Public clinic Age (years) <5 5e10 > 10 Water supply Municipal water system Treatment of incoming water Softener Filter None Treatment of DUWL Chemical disinfection No disinfection DU flushing Daily None Handpieces sterilization Daily After each patient

%

4 3

57 43

2 3 2

29 43 29

7

100

1 1 5

14 14 71

6 1

86 14

2 5

29 71

2 5

29 71

participation. DUW samples (1 L) were collected during at least 2 different time periods in routine conditions from (i) tap water upstream from unit (henceforth named Incoming Water or IW), (ii) turbine handpiece output water after a 48 h-stagnation period on Monday morning (named Output Water after Stagnation or OWS) and (iii) turbine handpiece output water immediately after dental care for the last patient of the sampling day (named Output Water after Activity or OWA). Water samples were stored in sterile bottles with 3 mL of sodium thiosulfate (18 g/L) at 4  C for no longer than 24 h before analysis. During sampling, appropriate personal protective equipment was used including sterile gloves and mask and care measures were taken to ensure that microorganisms did not originate from the sampler. Physico-chemical characteristics of the municipal water supplying the dental units, thus corresponding to the IW, were collected at each DUW sampling date (Supplementary material, Table S1). 2.2. Total aerobic and aero-tolerant cultivable microbial biomass in DUW Water from each DUW sample was processed by plating 50 mL in duplicate on R2A medium (Reasoner and Geldreich, 1985) using a WASP 2 spiral plater (Biomerieux, France). The number of CFU was counted 7 days after plate incubation under aerobic conditions at 28  C. 2.3. DNA extraction According to the filterability of DUW samples a volume of 100e500 mL was filtered over a sterile polycarbonate membrane (0.22 mm pore size, 47 mm diameter, Sartorius) held in a stainless steel vacuum filtration unit (Millipore), under a laminar flow hood. The volume of filtered water was precisely measured. Negative control of the DNA extraction was performed consisting of DNA extraction from water guaranteed DNA and RNA free filtered on polycarbonate membrane in the same conditions as for DUW samples. The membranes were aseptically cut into small strips, transferred to bead-beater tubes (MP Biomedicals) containing 0.50 (±0.05) g of 106 mm diameter glass beads and four glass beads of 2 mm diameter (Sigma) and vortexed with 1 mL of extraction buffer

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containing 100 mM TriseHCl (pH 8.0), 100 mM ethylenediaminetetraacetic acid (EDTA; pH 8.0), 50 mM NaCl, and 2% (w/ v) sodium dodecyl sulfate (SDS). Samples were homogenized for 20 s at speed five with a bead beater (FastPrep instrument, MP Biomedicals), incubated for 15 min at 70  C and then centrifuged at 10 000 g for 15 min. The collected supernatants were incubated for 10 min on ice with 1/10 volume of 3 M potassium acetate (pH 5.5) and centrifuged at 14 000 g for 10 min. After precipitation with one volume of ice-cold isopropanol, the nucleic acids were washed with 70% ethanol. Aliquots of DNA extracts were quantified in duplicate using SYBR Green I dye (Invitrogen, Fisher Scientific, France) on a LightCycler 480 instrument (Roche Applied Science). A standard curve of HindIII-digested l DNA fragments (Promega) was used to estimate the final DNA concentration as described previously (Leggate et al., 2006). Aliquots of 0.05 ng/mL DNA-diluted sample were prepared and stored at 20  C until use for molecular applications.

second PCR of 9 cycles, conducted under similar conditions, using primers containing pyrosequencing adaptors and ten base pair multiplex identifiers (MIDs) added to the reverse primers at 50 position (Berry et al., 2011; Terrat et al., 2012). PCR products were purified and quantified using the Quant-iT™ PicoGreen® dsDNA Assay Kit (Life Technologies). The size and purity of the amplicons were checked using the MultiNA bioanalyzer (Shimadzu). Preparation of PCR amplicon library from negative controls of DNA extraction and amplification failed excluding contamination bias. Equimolar PCR products (1  109 molecules/mL) were pooled for a single sequencing reaction run. The Lib-L kit (Roche Applied Science) was used for emPCR and unidirectional sequencing of the amplicon library. Pyrosequencing was carried out in a GS Junior 454 Sequencer (Roche Applied Science) following manufacturer's recommendations.

2.4. Quantitative polymerase chain reaction (qPCR)

Pyrosequencing data were analyzed using the GnS-PIPE (version 1.1.11) pipeline described by (Terrat et al., 2015). The parameters used for each bioinformatic step can be found in the Table S2. Briefly, the raw reads were sorted according to MIDs. All reads with mismatches in the primer sequence and ambiguities in sequence, sequence length less than 300 pb and without presence of exact primer sequences were discarded. Rigorous dereplication (i.e. clustering of strictly identical sequences) was performed using a PERL program. The retained dereplicated reads were then aligned using INFERNAL alignment as described by Terrat et al. (2015). Operational taxonomic units (OTUs) were clustered with a 5% dissimilarity cut-off at the genus level as recommended by the GnS-pipe pipeline to obtain reliable representation of bacterial communities through taxonomic classification of short 16S rRNA gene sequences, taking into account error rate from high throughput sequencing and also the variability of the 16S rRNA trovský and Baldrian, 2013). All the gene in bacterial genomes (Ve retained high-quality reads were taxonomically assigned according to the Silva r114 reference database (Quast et al., 2013). During the analysis, all singletons corresponding to reads detected only once and not clustered (that might be artefacts such as PCR chimeras and large sequencing errors produced by the PCR and the pyrosequencing) were checked on the basis of the quality of their taxonomic assignments (Terrat et al., 2015). Sequence data were clustered from the 21 water samples to obtain three groups representative of the IW, OWS and OWA. Normalization by random selection was carried out to obtain the same number of reads for each sample and to avoid biased bacterial community comparisons. The Shannon, number of detected OTUs and evenness, diversity and richness indices using the defined OTU composition at the genus level were determined. All the taxonomy-based analysis using similarity approaches against Silva r114 reference database obtained from the GnS-PIPE were then post-processed using R package and BioVenn (Hulsen et al., 2008). Roche 454 16S rRNA gene pyrosequencing reads are not long enough (mean length of 388 bp) to clearly identify bacterial species. But some of them can be inferred and thus, the detection of putative known pathogenic bacteria was considered among the most represented bacterial genera in the DUW samples (i.e. those for which the sum of the relative abundances of the genus in samples was higher than 0.1%). The human pathogenicity risk related to the observed species was evaluated based on published case reports on the PubMed database (Table S3). Sequences were aligned using the megaBLAST software over the NCBI nt database with default parameters. Only bacteria were considered, uncultured and environmental sample sequences were excluded. The alignment was

Quantitative PCR assays of the bacterial 16S rRNA gene were carried out using a SYBR Green detection system in a LightCycler 480 instrument (Roche Applied Science). All qPCR reactions were performed in duplicate for each DUW sample using the epMotion® 5070 pipetting robot (Eppendorf). The 341F (50 -CCTACGGGAGGCAGCAG-30 ) and 515R (50 -ATTACCGCGGCTGCTGGCA-30 ) primers set was used to amplify bacterial 16S rRNA gene (Baker et al., 2003). The 10 mL reaction mixture contained 0.5 mM of the 341F and 515R primers, 1X of LightCycler FastStart DNA MasterPLUS Sybr Green I mix (Roche Applied Science, France), 5 mL of H2O, and 2 mLof a 0.05 ng/mL DNA-diluted template. Thermal cycling conditions were as follows: an initial activation cycle at 95  C for 10 min and 35 cycles at 95  C for 15 s; 60  C for 10 s; 72  C for 15 s. An additional 65e95  C step (0.5  C/s) was added to obtain a specific denaturation curve for each amplified sequence products. Standard curve was obtained with serial dilutions of a standard sample containing a 1500 pb fragment of the 16S rRNA gene from Pseudomonas aeruginosa PA14 amplified using a 27F-1492R primers set (Baker et al., 2003), at concentrations ranging from 102 to 106 gene copies/ml. No-template control was run for each quantitative PCR assay. All DNA were tested for the inhibitory effects of co-extracted substances prior to quantification experiments. Bacterial qPCR data were normalized to take into account the precise filtered volume of water for each sample and then, were analyzed using ManneWhitney test. 2.5. Pyrosequencing The V3eV5 region of the 16S rRNA genes was amplified using the primers 341F (50 -CCTACGGGAGGCAGCAG-30 ) and 926R (50 CCGTCAATTCMTTTRAGT-30 ) in a vapo.protect™ Mastercycler (Eppendorf) (Baker et al., 2003). Each sample was prepared from the same DNA input quantity in order to normalize the libraries and achieve even representation of each library in the pyrosequencing results. PCR were performed in triplicate in 50 mL PCR mix with 0.5 mM of each forward and reverse primer, 1X PCR mix buffer with 200 mM of each dNTP and 1U of Phusion Hot Start II high-fidelity DNA polymerase (Fisher Thermo Scientific). The PCR conditions were as follows: 98  C for 1 min, 35 cycles of 30 s at 95  C, 50  C for 30 s and 72  C for 1 min, followed by 5 min at 72  C. No-template control was run for each PCR. Triplicate PCR products for each sample were pooled, purified using the PCR clean-up Kit according to the manufacturer's instructions (MachereyeNagel) and quantified using SYBR Green I dye (Invitrogen) as previously described. Purified PCR products were then specifically tagged in a

2.6. Bioinformatic analysis of pyrosequencing data

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considered only if its best Blast hit (blastn) had the nearly complete query coverage, with an identity percentage greater than 97.5% and a very low E-value. If the alignment was acceptable, only a maximum of 20 alignments with the same “max score” than the best alignment were considered. We attribute a species to the query sequence only if more than 50% of the previously filtered alignment results belong to the same clearly identify bacterial species. Otherwise no species was assigned for this sequence. The dataset is available on the EBI database system in the SequenceRead Archive (SRA), under study accession number PRJEB7584 (http://www.ebi.ac.uk/ena).

Table 2 Microbial concentrations in DUW using plate count on R2A medium.* Acceptable density for potable water according to regulations in the USA (American Dental Association, 2012).

3. Results

The complexity of the bacterial communities in the three water groups from the seven DUW was investigated based on the number of genus-level OTUs, Shannon and Evenness indices at 95% sequence similarity cutoff (Fig. 2). In terms of OTUs number, both OW groups displayed more richness and higher variability than IW, interestingly; p-value was <0.05 between IW and OWS conditions. The patterns of Shannon and Evenness indices did not reveal significant genus richness variations between IW and both OW. The median values of H ranged from 2.71 to 2.92 (Fig. 2) across the 3 water groups samples, indicating bacterial populations without a strongly dominant genus. Also, diversity metrics highlighted the inter-unit variability.

3.1. Analysis of bacterial colonization of the DUW The relative bacterial colonization of the seven DUW was monitored by quantitative PCR based on the 16S rRNA gene. Results showed a bacterial colonization in the three defined water groups (i.e IW, OWS and OWA) (Fig. 1). Interestingly, the bacterial colonization in OWS was higher than in IW (p ¼ 0.05), but no significant difference was observed between both OW and between IW and OWA (p > 0.05). Boxes and whiskers also suggested a high variability between the seven dental units for each of the three water groups. Plate counts confirmed these results (Table 2). Whereas 85.7% of the DU studied showed less than 500 CFU/ml of IW, only 14.3% of them had this acceptable water quality level for OWS. Densities remained up to 103 CFU/ml in 42.9% of the DU for both OW. 3.2. Bacterial genus richness and diversity indices in DUW A sum total of 144 502 reads with a mean length of 388 bases were obtained from the 21 dental water samples listed in three groups: IW, OWS and OWA. After bioinformatics filters and homogenization step, the libraries of IW, OWS and OWA contained 24 895, 24 826 and 24 949 effective sequences respectively. Members of 394 representative bacterial genera were obtained (Table S4), indicating a high bacterial diversity in the DUWL studied.

CFU/mL of water

Dental units (N ¼ 7) IW

<500* 500e1000 >1000

OWS

OWA

n

%

n

%

n

%

6 e 1

85.7 e 14.3

1 3 3

14.3 42.9 42.9

4 e 3

57.1 e 42.9

3.3. Bacterial community structure in DUW The pyrosequencing data were analyzed using principal coordinate analysis (PCoA) to examine the overall variation among bacterial communities of the seven dental units (Fig. 3). Results showed that 71.4% (i.e. 5 of the 7 DU studied) of the samples from the IW and OWS were clustered together, and that these two water group clusters were significantly discriminated on the first axis of the PCoA, which explained 41.44% of the total variability (Fig 3). This indicated that the stagnation of water in DUWL significantly affected the bacterial community structure. In contrast, samples from the OWA did not form a separate cluster from IW and OWS ones. This indicated that the bacterial community structure changed according to the water circulation within the unit.

3.4. Comparison of bacterial communities in DUW: core and unique genus-OTUs

Fig. 1. Range of variation of log 16S rDNA copies/mL of water (estimated by qPCR) from the seven dental units according to the water groups: IW, OWS and OWA. Box: Tukey box with median values (line), upper hinge of the box indicating 75th percentile of the data set, lower indicating 25th percentile and standard deviation (whiskers): maximum and minimum values.

A three-set Venn diagram analysis was made to identify the shared genus-OTUs among the three water sample groups. Among the 394 assigned OTUs, 137 were shared by the three water groups and accounted for 96.7%, 96.8% and 97.4% of the total classified sequences for IW, OWS and OWA, respectively (Fig. 4 and Table S4). This indicated that there is a continuous core bacterial community in the DUW throughout the water flow. Among sequences of this core microbiome, the most represented genera were: Sphingomonas, Halomonas, Shewanella, Sphingobium, Pseudomonas, Legionella and Propionibacterium each one representing 4e9% of total obtained sequences of the core microbiome (Fig. 5). Nevertheless, there were also 146 genera that were observed in only one water group, and accounted for 1.16%, 0.52% and 0.69% of total classified sequences in IW, OWS, and OWA respectively (Fig. 4 and Table S4). The Venn diagram also showed that a few bacterial genus sequences were shared only by OWA and OWS and, by OWA and IW. Only 29 OTUs were only common to OWS and IW, accounting for less than 0.8% of the total classified sequences of these two water groups (Fig. 4).

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Fig. 2. Bacterial richness and diversity indices from the seven dental units according to the water groups: IW, OWS and OWA. Box: Tukey box with medial values (line), upper hinge of the box indicating 75th percentile of the data set, lower indicating 25th percentile and standard deviation (whiskers): maximum and minimum values. Black points: outliers values.

Fig. 3. Bacterial communities associated with the different DUW by principal coordinate analysis (PCoA). Two-dimensional PCoA plot was based on the weighted UniFrac distance matrix. Percentage of the diversity distribution explained by each axis is indicated on the figure. Samples associated with the IW (red), OWA (green) and OWS (blue) are shown as circles, triangles and squares respectively. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 4. Three-set Venn diagram representing the shared and unique OTUs among IW, OWS and OWA. Percentages indicate the corresponding proportions of associated sequences. Red: proportions of the IW's total sequences. Green: proportions of the OWS's total sequences. Blue: proportions of the OWA's total sequences. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

3.5. Composition and dynamics of bacterial communites in DUW The pyrosequencing data were analyzed to identify changes in the bacterial community composition according to the water route in DUWL. The genera representing at least 0.5% of the total abundance in one of the three water groups were considered in every condition and their abundances compared to those in other samples (Fig. 6). The IW and both OW groups were dominated by two major phyla: Proteobacteria and Actinobacteria. Interestingly, 71.5% (±15.8%) of the sequences retrieved in OWS were attributed to Proteobacteria, which was higher than in IW and OWA reaching 65.6% (±20.9%) and 63.0% (±20.4%), respectively. Members of the Firmicutes, Bacteroidetes, and Chlamydiae were also found, with relatively low percentages. At the class level, as concerns Proteobacteria, DUW was dominated by Gamma Protebacteria (43.4e53.8% of total sequences), Alpha Proteobacteria (34.3e45.0%) and Beta Proteobacteria (5.74e15.1%). Differences between IW and both OW samples were obvious at the genus level (Fig 6). The Gamma Proteobacteria were dominated by Halomonas (17% ± 12%),

Shewanella (8% ± 5%) and Pseudomonas (4% ± 4%) in IW, whereas Stenotrophomonas (8% ± 16%), Legionella (6% ± 13%), Thioalkalispira (6% ± 14%), Halomonas (3% ± 2%) and Pseudomonas (2% ± 3%) were the main genera in OWS. The composition of Gamma Proteobacteria from OWA was quite similar to the one observed in IW, with preponderance of Halomonas and Shewanella. The class of Alpha-Proteobacteria was dominated by Sphingopyxis (5% ± 14%) and Sphingomonas (4% ± 4%) in IW, rather than Sphingomonas (9% ± 7%) and Sphingobium (8% ± 9%) in OWS. These last two genera were maintained in OWA with 4% ± 5% of sequences attributed to Sphingomonas and 8% ± 7% to Sphingobium. Propionibacterium (15% ± 11%) and Mycobacterium (4% ± 5%) dominated the phylum of Actinobacteria in IW. But interestingly, these proportions were inverted in OWA (19% ± 21% of Mycobacterium and 2.5% ± 6% Propionibacterium) and only 10% ± 7% of the sequences corresponded to these two genera in OWS. Overall, Fig 6 also showed that 3e11% of the sequences were attributed to genera representing less than 0.5% of the microbiome.

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Fig. 5. Relative abundance of bacterial genera in the DUWL core microbiome.

3.6. Potential human pathogenic bacteria in DUW Mycobacterium, Halomonas Propionibacterium, Pseudomonas, Stenotrophomonas, Legionella and Shewanella (Fig. 6, Table 3 and Table S4) were the most represented genera observed in the three water groups which were known to include species of pathogenic interest in human health. Interestingly the relative abundance of some of these genera tended to increase according to the water route in the DUW such as Mycobacterium (in OWA) or Stenotrophomonas, Sphingomonas and Legionella (in OWS). Some potential human pathogens were also identified at the species level, with preponderance of Mycobacterium abscessus/chelonae, Propionibacterium acnes, P. aeruginosa and Stenotrophomonas maltophilia (Table 3). 4. Discussion The quality of water delivered through DUWL is a public concern due to the frequent exposure of both patients and dental staff. Microbial contamination levels up to standard recommendations had previously been reported in other studies focusing on DUWL microbial contamination (Arvand and Hack, 2013; Barbeau et al.,
ska et al., 2008). In our study, 1996; Santiago et al., 1994; Szyman microorganisms were clearly detected in the three water groups (IW, OWS and OWA). The IW condition represents microorganisms provided in DUWL by the inlet water which came exclusively from the municipal water system of Poitiers in our study. The OW conditions (both OWS and OWA) represent the microbial community able to reach patients and dental staff through droplet inhalation or

splashed from the outlet of handpieces. However the microbial composition of the OWA condition was influenced both by the microbial composition of the supplying water of the DU and by the one of the biofilm developed inside DU pipes; this biofilm may release a part of its community due to the mechanical flushing action of water flow. The microbial composition of the OWS condition was influenced by the same factors that those described for OWA condition; however the OWS condition was considered as the preferred indicator of the influence of biofilm developed inside DU pipes: the water stagnation occurring in OWS before the sampling would promote the proliferation of sessile microorganisms present inside DUWL. Interestingly, a significant inter-unit variability in microbial contamination was highlighted, that may be related to the various practical procedures or installations of dental unit systems such as dental equipment, or water treatment. Higher bacterial contamination in OWS compared to IW was observed using quantification of 16S rDNA gene copies and plate counts. This could be explained by the presence of a biofilm within DUWL (Barbeau et al., 1996; Whitehouse et al., 1991) that regularly releases microorganisms in DUW. As OW are directly or indirectly in contact with patients and care personnel, it seemed essential to explore bacterial community diversity in DUWL. In our study, the bacterial community composition of water was studied in seven DU using 454-pyrosequencing-based analysis of 16S rRNA genes. As shown by alpha diversity indices, the PCoA and the Venn diagram, some differences in bacterial communities between IW and both OW were observed. This could indicate that diversified environmental and growth conditions may be present throughout the flow of water in a dental unit.

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Fig. 6. Heatmap representing relative abundance of bacterial communities in IW, OWS, and OWA conditions. Only genera representing at least 0.5% of relative abundance from the total sequences list in at least one condition (IW, OWS or OWA) were represented.

In addition, results showed that the bacterial phyla found in DUWL were similar to those described in drinking water (Holinger et al., 2014). Shifts in bacterial community composition and abundance were observed at the genus level. The abundance of some genera varied according to location; for example, Mycobacterium was more frequent in OWA compared to both IW and OWS. Anyway, in IW, abundance variations of Mycobacteria were counterbalanced by those of Propionibacterium. Mycobacteria have been described for their resistance to a wide range of antibiotic agents (Maurer et al., 2014) and may likewise resist to the different water treatments used in DUWL. More specifically, their resistance to treatments used in water distribution systems as chlorination and ozonation has been reported (Falkinham et al., 2001; Le Dantec et al., 2002; Taylor et al., 2000). Dental units are usually subjected to continuous treatment in order to limit DUWL microbial colonization (Coleman et al., 2014; Schel et al., 2006; Walker and Marsh, 2007). The treatment is triggered by the water flow involved in dental unit activity, suggesting that OWA samples could be more exposed to disinfectant agents than OWS and IW samples. Consequently, one possible explanation for the relatively high abundance of Mycobacterium in OWA resides in different sensitivities of genera

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to disinfecting treatments. We can also suppose that Mycobacterium and Propionibacterium could be embedded at different depths of the biofilm, with Propionibacterium certainly being found in deeper layers than Mycobacterium due to high relative abundance of Propionibacterium from the IW condition. As a result, Mycobacterium may be more easily dispersed in DUWL than Propionibacterium. Mycobacterium occurrence in IW was not surprising as it was recently reported by Kelly and collaborators to be among the dominant taxa of biofilms in drinking water distribution pipes with concentrations up to 14% (Holinger et al., 2014; Kelly et al., 2014), in accordance with a number of parameters such as nitrate, ammonium, total chlorine and monochloramine concentrations. In contrast, the high relative abundance of Propionibacterium highlighted in IW was not expected since the bacteria related to this genus are usually found on the skin (Scharschmidt and Fischbach, 2013). The abundance may have to do with their release in waste-water through showers or toilets as far as we know, their susceptibility to conventional water treatments has been poorly documented. In addition, some Propionibacterium are used as probiotics in dairy produce for both animals and humans (Cousin et al., 2012; Shu et al., 2013), further increasing their presence in water. Comparative study of each microbiome corresponding to the three water groups (i.e. IW, OWS and OWA) underscored the possible influence of local parameters on bacterial contamination. These factors include water pre-treatment by filtration or softener, continuous or discontinuous internal chemical treatment of DUWL and water stagnation. The detection of bacterial genus only in OW and not in IW suggested that suck-back of biological fluids into DUWL from the oral cavities of patients should not be excluded (Petti et al., 2013). To sum up, the different results suggested that dental unit maintenance procedures and practices could modify the composition of the bacterial community in water and may encourage some bacterial genera (Mycobacterium) while disadvantage others (as Propionibacterium). In this study, potential pathogenic bacterial sequences were recovered in the three water groups. Some observed species are being considered as emerging or opportunistic pathogens such as M. abscessus/chelonae, Stenotrophomonas maltophilia and Propionibacterium acnes. Their pathogenic behavior is particularly likely to involve vulnerable elderly or cystic fibrosis patients (Lipuma, 2010; Mahenthiralingam, 2014) and, more generally, may cause infections associated with dentures (Aubin et al., 2014; Portillo et al., 2013). Patients may be exposed to many of these bacterial pathogens by either aerosol droplet inhalation or blood exposure during dental care and develop associated infections. Abundance of these microorganisms varied according to the sampling localization (i.e. IW, OWA or OWS). As previously discussed, these variations could be partially explained by the effect of unit maintenance practices (flushes and/or water treatments). Using a pyrosequencing approach, Legionella spp. was found in each of our water group (i.e. IW, OWA and OWS). The presence of Legionella spp. in our samples was also confirmed by qPCR (Supplementary material table S5) based on the protocol from Dutil et al. (2007). The infectious risk associated with Legionella pneumophila is well-known, especially in a dental care context (Ricci et al., 2012). In our study, L. pneumophila was not detected, contrary to Legionella anisa which was isolated from one DU based on selective agar plate method and clearly identified by the French National Reference Center for Legionella (Lyon, France); L. anisa plays a role in community-acquired pneumonia (McNally et al., 2000; Thacker et al., 1990). In France, a decree sets a maximum

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D. Costa et al. / Water Research 81 (2015) 223e231

Table 3 Relative abundance of potential bacterial pathogens identified in DUWL. Only bacterial genera for which relative abundances were higher than 0.1% in samples were considered for the detection of the potential bacterial pathogens. Relative abundance (%) IW 0.11 0.25 3.28 0.21 0.46 0.20 0.16 0.12 0.18 0.29 0.13 OWS 0.85 3.30 0.14 0.19 0.25 1.01 0.12 3.44 6.28 OWA 0.10 13.22 0.18 0.56 0.57 2.41

Phylum

Class

Species

Representative accession number

Actinobacteria

Actinobacteria

Firmicutes

Bacilli

Proteobacteria

Betaproteobacteria Gammaproteobacteria

Corynebacterium accolens Mycobacterium abscessus/chelonae Propionibacterium acnes Propionibacterium granulosum Bacillus cereus Dolosigranulum pigrum Streptococcus oralis Streptococcus mitis Neisseria perflava Pseudomonas peli Acinetobacter lwoffi

KC429646 CP009616 KF910210 JF277164 NR_118646 KJ399985 GU395995 KJ170407 KC817299 AB904075 JX861583 KJ995845

Actinobacteria

Actinobacteria

Firmicutes Proteobacteria

Bacilli Alphaproteobacteria Gammaproteobacteria

Mycobacterium gordonae Propionibacterium acnes Propionibacterium granulosum Bacillus cereus Novosphingobium aromaticivorans Shewanella algae Acinetobacter lwoffi Pseudomonas aeruginosa Stenotrophomonas maltophilia

KF910207 CP003293 NR_118646 KJ399985 HF930753 KM269010 KJ995845 KJ419347 KC622052

Actinobacteria

Actinobacteria

Proteobacteria

Gammaproteobacteria

Micrococcus luteus Mycobacterium abscessus/chelonae Mycobacterium gordonae Propionibacterium acnes Acinetobacter lwoffi Pseudomonas aeruginosa

KJ398243 CP009616 KF910210 KF910207 AB777862 KJ995845 KJ419347

threshold of 5  103 L. pneumophila GU (genomic unit)/L of water for non-immuno-compromised patients (Ministry of Health (2010)). However no threshold is currently available for other species of Legionella.

5. Conclusion  To our knowledge, for the first time, bacterial communities of DUWL were described by high-throughput sequencing in real practice conditions.  Many of the most represented genera contain species potentially pathogenic for humans such as: Mycobacterium, Propionibacterium, Shewanella or Stenotrophomonas.  Shifts in bacterial community composition during their water routes were investigated.  Results show a high complexity in this ecosystem where bacteria from environmental and human origins coexist.  Both shared and specific bacterial communities were described.  The stagnation of water in dental units particularly influence the bacterial community composition and increase the bacterial load in outlet water.  With particular interest given to pathogenic or opportunistic genera, a better apprehension of real risk for health was provided.

Acknowledgments The authors sincerely thanks the scientific supports provided by S. Terrat and the GenoSol platform (INRA, Dijon, France, www2. dijon.inra.fr/plateforme_genosol/) for the development of the 454 pyrosequencing and data analysis. Jeffrey Arsham, an American medical translator, comprehensively reviewed and revised the original English-language document.

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