Abstract

This study evaluated the microbiological safety of the water distribution system of a city in the state of Minas Gerais (Brazil), population 120,000 inhabitants. During the study, the city suffered a severe drought that had a significant impact on water availability and quality in the river that supplies water to the city. Samples (2 liters) were collected from the distribution system over a period of six months, which included wet and dry months, from three points: the point with the lowest altitude in the distribution network, the farthest point from the water treatment works, and an intermediate point. Free chlorine was measured in situ using a Hach kit. DNA was extracted using a FastDNA Spin Kit Soil (Qbiogene). Advanced sequencing techniques (Ion Torrent) were used to identify and quantify the relative abundance of potentially pathogenic bacteria present in the samples. Coliforms and Escherichia coli, indicators currently used worldwide to assess microbiological safety of drinking water, were measured on all samples using an enzyme substrate method (ONPG-MUG Colilert®). Next generation sequencing results retrieved 16SrRNA sequences of E. coli and some potentially pathogenic bacteria, even in the presence of free chlorine. Operational taxonomic units related to pathogenic bacteria were present in all samples from the drinking water distribution system (DWS) and, in general, at high relative abundance (up to 5%). A total of 19 species related to bacterial pathogens were detected. Inadequate operational practices that could affect the microbiological safety of the DWS were identified and discussed. The current paper is the first to evaluate the community of potentially pathogenic bacteria in a real DWS.

INTRODUCTION

Brazil has approximately 18% of the earth's total freshwater, including rivers, lakes and reservoirs that have multiple uses, such as drinking water supply and power generation (ANA 2015). Approximately 80% of all freshwater in Brazil is located in the Pantanal and Amazon regions, which are sparsely populated. The remaining freshwater is distributed in the Southeast, South and Northeast regions, where the vast majority of the country's population lives (78%). Since late 2013, the southeast region of Brazil has been facing a serious water crisis as a result of both drought and inadequate water management, which has forced 27% of the country's municipalities to declare a state of emergency in 2015 (ANA 2015).

The significant reduction in water availability in rivers and reservoirs has forced several water companies to use water from multiple sources in order to meet demand. Some of these sources receive untreated sewage from municipalities and industries, increasing the risk of drinking water contamination by pathogenic organisms (Revetta et al. 2016). This was the case of the city in Minas Gerais (southeast Brazil), the subject of the current study. During the recent drought, it had to pump approximately 25% of its flowrate from a river that suffers from unregulated urban development.

The monitoring of the microbiological safety of the drinking water distribution system (DWS) is extremely important in order to minimize risks to public health, especially during severe drought events as they cause further deterioration of freshwater quality. In Brazil, the current drinking water legislation (MS 2914/11) requires a minimum free chlorine concentration of 0.2 mg L−1 in the entire distribution network, and only requires the monitoring of coliforms and Escherichia coli to evaluate the microbiological safety of water supply systems (BRASIL 2011). These indicators can be inadequate, as some pathogenic bacteria are more resistant to chorine than coliforms/E. coli. Research is still needed to assess the suitability of coliforms/E. coli as indicators for pathogenic organisms (Field & Samadpour 2007). The purpose of this study was to evaluate, using next generation sequencing (NGS) technology, the bacterial community of a DWS, with focus on pathogenic populations and assess whether E. coli is suitable to indicate the presence of pathogenic bacteria in a water distribution system severely stressed by drought.

METHODS

Description of the water supply system

The drinking water treatment plant (DWTP) has an average flowrate of 180 L/s, reaching 200 L/s on peak days, serving approximately 120,000 people. The DWTP uses conventional treatment, including coagulation, flocculation, sedimentation, single media filtration and the addition of free chlorine before distribution. The DWTP has four single layer (1.8 m thickness), rapid, up flow filters, that are backwashed every 12 hours with treated water. Free chlorine is added to filtered water at a concentration of 1.5 mg Cl2/L (contact time of approximately 30 min), and fluoride is added at a concentration of 1 mg L−1.

Sampling

Samples were collected from tree points in the DWS (Table 1), from July to December 2015, including the end of the dry season (July to September) and the beginning of the wet season (October to December).

Table 1

Sampling points in the water distribution system

PointSampling pointsFiltered volume (July to October)Filtered volume (November and December)
Municipal school (point with the lowest altitude in the DWS) 2 L 4 L 
Health center (intermediate point) 2 L 4 L 
Residence (farthest point from the WTWs) 2 L 4 L 
PointSampling pointsFiltered volume (July to October)Filtered volume (November and December)
Municipal school (point with the lowest altitude in the DWS) 2 L 4 L 
Health center (intermediate point) 2 L 4 L 
Residence (farthest point from the WTWs) 2 L 4 L 

Samples were collected from taps that received water directly from the distribution network and were kept in sterile glass bottles containing 1 tablet of 10% sodium thiosulfate to inactivate free chlorine. One hundred mL of water were used for the quantification of coliforms and E. coli using ONPG-MUG Colilert®. The samples were processed immediately after sampling in accordance with APHA 9223B (APHA/AWWA/WEF 2012). Samples were filtered (total volumes stated in Table 1) using cellulose acetate membrane filters (GF1 filter, 0.2 μm, Macherey and Nagel). The filters were preserved at −20 °C for genomic DNA extraction. Water temperature and free chlorine concentrations were measured in situ using a calibrated thermometer and a 21,055 colorimeter pocket Hach kit (Hach Lange, UK). Turbity was measured using a 2100Q portable turbidimeter.

DNA extraction

DNA was extracted from membrane filters using a FastDNA Spin Kit for Soil (Qbiogene), according to the manufacturer's instructions. Concentration and purity of the DNA extracts were determined using a NanoDrop 1000 spectrophotometer (Thermo Scientific).

PCR amplification and purification

Amplicon libraries were generated from each DNA sample by direct polymerase chain reaction (PCR) amplification, using a universal primers set (515F and 926R), targeting the V4 and most of the V5 region of the 16S rRNA gene, containing the Ion adapters A (5′- CCATCTCATCCCTGCGTGTCTCCGACTCAG-3′) and trP1 (5′- CCTCTCTATGGGCAGTCGGTGAT-3′), for forward and reverse primers, respectively. Samples were differentiated by adding unique 12 base pair barcodes to the forward primer, through a ‘GAT’ space. The PCR reaction was carried out using a FastStart High Fidelity PCR System and the PCR Nucleotide Mix (Roche Diagnostics GmbH, Mannheim, Germany). This kit contains: buffer, MgCl2, dNTP's, and DNA polymerase. For the PCR reaction, 1 μL of the 1:10 dilution for each of the DNA extracts (at least 10 ng/mL of DNA template) was added to a mixture containing 5 μL FastStart High Fidelity Reaction Buffer, 1 μL dNTP, 1 μL of each primer, 0.5 μL of Enzyme blend and 40.5 μL of nuclease free water, to obtain a final reaction volume of 50 μL. The following PCR program was used: initial denaturation 95 °C for 4 min, followed by 25 cycles of 95 °C for 1 min, 55 °C (annealing) 45 sec, 72 °C (extension) for 1 min and final elongation of 72 °C for 7 min. PCR products were checked by electrophoresis, run at 100 V for 45 min, using 5 μl of product/extract plus loading buffer on 2% agarose gels, containing Nancy-520 DNA Gel Stain (Sigma-Aldrich), and in 1× Tris-acetate-EDTA buffer. Gels were visualized by a UV illumination using a Bio-Rad Fluor-S Multi Imager (Bio-Rad, UK). PCR purification was made using the Agencourt AMPure XP PCR Purification system, which utilizes Agencourt's solid-phase paramagnetic bead technology for high-throughput purification of PCR amplicons. The resulting purified PCR product was used directly in the NGS workflow.

Amplicon library quantification

The quantification of the individual amplicon libraries was carried out using a Qubit 2.0 Fluorometer (Invitrogen), Qubit ds DNA HS reagent Assay kit and Qubit™ assay tubes. A Qubit working solution was prepared for all samples by diluting the Qubit dsDNA HS reagent 1:200 in Qubit dsDNA HS buffer. Each sample tube required 199 μl of Qubit working solution and 1 μl sample. The amplicon libraries were pooled in equimolar quantities into a unique solution for the downstream template preparation procedure for clonal amplification on Ion Spheres. The diluted library was freshly prepared before being used on the Ion OneTouch2 System.

NGS by Ion Torrent

The samples were sequenced using the Ion Torrent PGM (400 bp) (School of Engineering & Geosciences, Newcastle University) with the 316™ ion chip using the manufacturer's instructions (Life Technologies, USA). Raw sequences were analysed using QIIME (v 1.7.0) bioinformatics pipeline. After quality filter (minimum quality score of 20, perfect match to sequence barcode and primer), the remaining sequences were clustered in operational taxonomic units (OTU) at 97% similarity level, and the representative sequences taxonomically assigned using the SILVA database (Quast et al. 2013). The final results were given in relative abundance (%).

Identification and relative abundance of potentially pathogenic bacteria

Potentially pathogenic bacteria were identified based on the NGS data, species level (Level 7 on Silva's database), by comparing (using 97% similarity) the identified species with those reported in the medical literature (Lloyd-Puryear et al. 1991; Podschun et al. 2001; Auzias et al. 2003; Lindquist et al. 2003; Loubinoux et al. 2003; Bhatia et al. 2004; Ryan et al. 2006; de Baets et al. 2007; Hironaga et al. 2008; Siciliano et al. 2008; Sharma & Kalawat 2010; Park et al. 2011; Brooke 2012; Okada et al. 2013; Saeb et al. 2014; Touchon et al. 2014; Lo et al. 2015). The relative abundances of all potentially pathogenic OTUs were added, giving a total relative abundance of potentially pathogenic bacteria.

RESULTS AND DISCUSSION

A total of 19 species related to bacterial pathogens were detected in this study (Table 2). Some of these bacteria are opportunistic and can cause disease in patients with debilitated immune systems. P. acnes, detected in two sampling points in two occasions, is generally considered non-pathogenic. However, evidence suggests that it can be a low-virulence pathogen in a variety of postoperative infections and other chronic conditions (Bhatia et al. 2004).

Table 2

Pathogenic bacteria detected by NGS on samples from the water distribution network

Pathogenic speciesPoints/month
DiseaseReference
MSHCR
1. Achromobacter xylosoxidans JD JAN JD Infection or colonisation in cystic fibrosis patients. De Baets et al. (2007)  
2. Acinetobacter calcoaceticus  JD Nosocomial infection Touchon et al. (2014)  
3. Acinetobacter ursingii  JD Bacteremia Loubinoux et al. (2003)  
4. Brucella spp. JSD ON JOND Brucellosis, osteoarthritis, endocarditis and several neurological disorders. Saeb et al. (2014)  
5. Chromobacterium haemolyticum  JND SN Bacteremia Okada et al. (2013)  
6. Corynebacterium aurimucosum   Urinary tract infection Lo et al. (2015)  
7. Corynebacterium durum   Respiratory tract infection Riegel et al. (1997)  
8. Corynebacterium freneyi   JN Bacteremia Auzias et al. (2003)  
9. Coxiella SN AND JSD Q fever Siciliano et al. (2008)  
10. Dygonomonas sp. ND   Infection gall bladder Hironaga et al. (2008)  
11. Klebsiella   Nosocomial infections Podschun et al. (2001)  
12. Legionella nagasakiensis   Pneumonia Yang et al. (2012)  
13. Massilia timonae  General infections in low immunity patients Lindquist et al. (2003)  
14. Propionibacterium acnes JA  JD Androgen stimulated seborrhoea, hyperkeratinisation and obstruction of the follicular epithelium and inflammation. Bhatia et al. (2004)  
15. Psychrobacter immobilis   Ocular infection, meningitis Lloyd-Puryear et al. (1991)  
16. Ralstonia pickettii  Nosocomial infections Ryan et al. (2006)  
17. Rhodococcus erythropolis   Septicaemia Park et al. (2011)  
18. Shewanella putrefaciens  Hepatobiliary disease, peripheral vascular disease, with chronic leg ulcer Sharma & Kalawat (2010)  
19. Stenotrophomonas maltophilia   JN Nosocomial infections Brooke (2012)  
Pathogenic speciesPoints/month
DiseaseReference
MSHCR
1. Achromobacter xylosoxidans JD JAN JD Infection or colonisation in cystic fibrosis patients. De Baets et al. (2007)  
2. Acinetobacter calcoaceticus  JD Nosocomial infection Touchon et al. (2014)  
3. Acinetobacter ursingii  JD Bacteremia Loubinoux et al. (2003)  
4. Brucella spp. JSD ON JOND Brucellosis, osteoarthritis, endocarditis and several neurological disorders. Saeb et al. (2014)  
5. Chromobacterium haemolyticum  JND SN Bacteremia Okada et al. (2013)  
6. Corynebacterium aurimucosum   Urinary tract infection Lo et al. (2015)  
7. Corynebacterium durum   Respiratory tract infection Riegel et al. (1997)  
8. Corynebacterium freneyi   JN Bacteremia Auzias et al. (2003)  
9. Coxiella SN AND JSD Q fever Siciliano et al. (2008)  
10. Dygonomonas sp. ND   Infection gall bladder Hironaga et al. (2008)  
11. Klebsiella   Nosocomial infections Podschun et al. (2001)  
12. Legionella nagasakiensis   Pneumonia Yang et al. (2012)  
13. Massilia timonae  General infections in low immunity patients Lindquist et al. (2003)  
14. Propionibacterium acnes JA  JD Androgen stimulated seborrhoea, hyperkeratinisation and obstruction of the follicular epithelium and inflammation. Bhatia et al. (2004)  
15. Psychrobacter immobilis   Ocular infection, meningitis Lloyd-Puryear et al. (1991)  
16. Ralstonia pickettii  Nosocomial infections Ryan et al. (2006)  
17. Rhodococcus erythropolis   Septicaemia Park et al. (2011)  
18. Shewanella putrefaciens  Hepatobiliary disease, peripheral vascular disease, with chronic leg ulcer Sharma & Kalawat (2010)  
19. Stenotrophomonas maltophilia   JN Nosocomial infections Brooke (2012)  

Legend: J, July; A, August; S, September; O, October; N, November; D, December; MS, Municipal school; HC, Health center; R, Residence.

In total, NGS generated 131,941 OTUs, based on Silva's ribosomal RNA gene database. This value refers to the total of OTUs in the three sampling points throughout the monitoring period. The traditional enzymatic method did not detect E. coli or coliforms in any of the samples throughout the study. However, NGS detected E. coli and several potentially pathogenic bacteria in all but two samples (Figure 1), at relative abundance as high as 5% of the total community for one point (Health center, September). NGS has a very low detection limit. It is important to note that NGS does not indicate the viability of the cells detected, as the presence of DNA fragments from dead cells would also be detected by NGS. Nonetheless, these results are worrisome, because pathogenic bacteria were only present at very low relative abundance in samples collected from the treated water reservoir that feeds the distribution network (data not shown).

Figure 1

Relative abundance (%) of E. coli and potentially pathogenic bacteria determined by NGS and free residual chlorine concentration (mg L−1).

Figure 1

Relative abundance (%) of E. coli and potentially pathogenic bacteria determined by NGS and free residual chlorine concentration (mg L−1).

Some OTUs were only detected in the dry months of July-September (Ralstonia pickettii, Shewanella putrefaciens, Legionella nagasakiensis, Klebsiella, Corynebacterium aurimucosum), whereas other species were detected only in the wet months of October-December (Corynebacterium durum, Chromobacterium subtsugae, Dygonomonas sp., Psychrobacter immobilis). This could suggest the effect of the water source on the pathogenic community composition of the distribution network, as water from a new intake built on a different river was pumped to the treatment works from October, during the peak of the water scarcity crisis. Revetta et al. (2016) observed that source water influences the resistance, survival and community composition of pathogens and indicators in water distribution systems.

E. coli are frequently used as indicators to infer faecal pollution in water. All strains of this indicator organism should experience the same persistence (maintenance of culturable cells) in water sources, although, some strains may have comparatively extended persistence outside the host, while others may persist very poorly in environmental waters. E. coli is viable for at least three months under environmental conditions and could be viable for four months under laboratory conditions. The possible reason of the difference in viability between both conditions is that there are more microflora under natural than laboratory conditions, and an indicator organism that survives for long periods in the environment is not desired (Edberg et al. 2000). The absence of culturable E. coli and the presence of pathogenic bacteria in the DWS studied could be attributed to differences in strains' persistence. Furthermore, cells that are not active within the DWS could become active inside the human host (Pinto et al. 2012).

For most samples, E. coli (grey bars in Figure 1) were detected when pathogenic bacteria were also detected (black bars in Figure 1), except for the month of October at the farthest point from the water treatment works (WTWs) (Residence) and for the month of November at the point with the lowest altitude in the distribution network (Municipal school), giving two potential false negative results for the presence of pathogens when using E. coli as indicator organisms. However, in both samples, only very low total abundances of pathogenic bacteria were detected (<0.4%). Conversely, E. coli were also present when pathogenic bacteria were not detected (Residence-August and Municipal school-October) or were detected at very low abundance (<0.05, in Health centre-July, Municipal school-August, Health centre-September), giving five false positive results as indicator organisms. The samples with the highest abundance of E. coli did not coincide with those with the highest relative abundance of pathogenic bacteria. Spearman correlation between E. coli and total pathogenic bacteria was not significant (0.05%).

Free residual chlorine concentrations in virtually all samples met the minimum and maximum values stipulated by the Brazilian Regulation (MS 2914/2011, minimum of 0.2 mg L−1, maximum of 5 mg L−1 combined chlorine residual), except for one sample collected at the farthest point in the distribution system in the wet month of December (7.3 mg L−1). Operators reported that extra chlorine was added directly to the distribution system as a precautionary measure due to the detection of high turbidity in the distribution system. It has yet to be determined if the increase in turbidity was due to failure in the WTWs or due to possible intrusion of untreated water in the distribution network. Both these possible causes are of concern due to the close association between high turbidity values and the presence of pathogenic organisms in water. Additionally, the practice of adding free chlorine directly into the distribution network when higher turbidity is detected is of concern due to its higher risk of forming trihalomethanes in the DWS, and because the network might not provide the necessary contact time required for disinfection, increasing risks to public health due to waterborne diseases (Edberg et al. 2000).

Even samples with free chlorine concentrations higher than 1 mg/L also showed high relative abundance of pathogenic bacteria (Figure 1). The method used to determine the abundance of potentially pathogenic bacteria does not indicate whether the pathogenic bacteria detected were alive or had been inactivated by free chlorine. Pathogens are able to colonize water distribution systems even after disinfection by chlorination with chlorine (Pinto et al. 2012). Chloramine and chlorine could be a selective influence on the microbial community of DWSs (Holinger et al. 2014). The occurrence of some pathogenic bacteria, such as Legionella, Mycobacterium and Pseudomonas, is influenced by the presence or absence of the disinfectant residual and the type of disinfectant residual used (Bautista-de los Santos et al. 2016).

CONCLUSIONS

Culture-dependent methods, such as enzymatic methods used for coliforms and E. coli quantification, are a valuable tool as they are widely used by sanitation companies worldwide to monitor the microbiological safety of drinking water. Because of their high coverage, NGS technologies provide more comprehensive information about microbial communities, and can detect organisms at low abundance. Douterelo et al. (2014) state that NGS can exceptionally increase our understanding of the diversity and structure of microbial communities in DWS. The current paper is the first to evaluate the community of potentially pathogenic bacteria in a real DWS. The approach revealed a highly diverse group of bacterial pathogens (total of 19 OTUs). Although bacterial pathogens are of major interest due to public health concerns, their detection and quantification of their relative abundance in environmental samples using sensitive, molecular methods (such as NGS) are still a challenge. This is due to the fact that pathogenic bacteria are of highly diverse phylogenetic groups, which makes it impossible to target them as a group, using a single marker.

The current work also showed that E. coli was a fairly good indicator organism for pathogenic bacteria, when using NGS. However, the traditional enzymatic method was unable to detect coliforms and E. coli in all samples. The authors suggest, in line with Douterelo et al. (2014), that future research should use integrated approaches that combine a range of techniques to explore and link microbial diversity and activity to ultimately understand the relationship between microorganisms and system function in water distribution systems.

The current study also showed that the water distribution system evaluated is in dire need of operational changes, including effective maintenance to prevent biofilm formation and accumulation of other particles that allow bacterial adhesion to improve water quality and safety. Furthermore, the current study highlights the importance of protecting catchment areas that are used for water supply in order to minimise risks to public health due to waterborne diseases.

ACKNOWLEDGEMENTS

This study was supported by a scholarship to A.M.M.B, funded by CAPES, Brazil (Coordenação de Aperfeiçoamento do Pessoal Docente) and the Project Global Innovation Partnership to Investigate, Restore and Protect the Urban Water Environment, funded by the British Council and the Department for Business, Innovation and Skills, via the Global Innovation Initiative.

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