In-situ features of LNA and HNA bacteria in branch ends of drinking water distribution systems

In China, it was reported that about 69% of drinking water hygienic accidents (e.g. abdominal pain, diarrhea and enteritis) were attributed to biological pollution (Li et al. 2007). Current standard methods of microbial examination of drinking water are based on cultivation, e.g. heterotrophic plate counts (HPCs) and multiple tube-fermentation. However, most of microorganisms in natural environments are viable but non-culturable, and only less than 1% of the microorganisms can be cultivated by HPCs (Wang et al. 2010a). Meanwhile, conventional methods are not suitable for prompt monitoring of microbial changes in drinking water due to their long examination duration, and may lead to delays in safety management (Besmer et al. 2014). In addition, water quality of end consumers would change during the transport process in drinking water distribution systems (DWDSs) due to the difference of hydraulic retention time and pipeline materials (El-Chakhtoura et al. 2015; Roeselers et al. 2015; Abokifa et al. 2016; Liu et al. 2016a). Microorganisms would be massively propagated if the concentration of assimilable organic carbon (AOC) was too high in drinking water, where opportunistic pathogens would cause hygienic problems (Prest et al. 2016). Excessive growth of bacteria would also result in bio-corrosion of water supply pipelines and further lead to secondary pollution of drinking water (Holinger et al. 2014; Van Nevel et al. 2016). Therefore, it is essential to understand and control the microbial growth at the branch end of DWDSs in drinking water quality management (El-Chakhtoura et al. 2015).

Flow cytometry (FCM) has been applied recently to analyze microbial dynamics in drinking water, as a new monitoring method (Wang et al. 2010a; Besmer et al. 2014). Bacteria would cluster into two distinct subgroups, low nucleic acid content (LNA) bacteria and high nucleic acid content (HNA) bacteria, when analyzed by FCM in combination with fluorescent staining (Wang et al. 2010a). LNA bacteria, playing a critical role in aquatic ecosystems, are considered to be oligotrophic and dominant in low nutrient environments (Solic et al. 2009). Drinking water is a typical oligotrophic environment where the amount of total organic carbon is usually less than 1 mg/L (Lautenschlager et al. 2013; Kelly et al. 2014). It was proposed that there may be microenvironments where LNA and HNA bacteria could co-grow in drinking water (Prest et al. 2013). Particularly, oligotrophic bacteria may maintain high abundance in drinking water, which would impose a potential threat to human health and safety (Liu et al. 2013a). Hence, it is necessary to investigate the bacteriology on LNA and HNA features and their difference in DWDSs networks.

Here the LNA and HNA bacteria were analyzed at different outlets from a DWDS where the residual chlorine is no less than 1.4 mg/L in the effluent of drinking water treatment processes. The aims of the present study were: (1) to analyze the LNA and HNA bacteria content and compare their in-situ co-growth characteristics in branch ends of DWDS; (2) to illustrate the effect of water physicochemical properties on LNA and HNA bacteria.

Drinking water samples were taken from four main outlets of the DWDS in TEDA College (Tianjin, China). It covers a field of 70,000 m² and comprises teaching and living districts. Chlorination was used as one of the main disinfection methods in the drinking water process. The residual chlorine is no less than 1.4 mg/L at the beginning of the DWDS. All outlets were supplied from the same water treatment plant and three replicates of 2 L water samples were collected in each outlet at room temperature and the sampling water was analyzed immediately according to the following methods.

The cultivation medium was composed of peptone (10 g/L), beef extract (3 g/L), NaCl (5 g/L) and agar (20 g/L) with pH 7.4 to 7.6. The medium was sterilized at 121°C for 20 min. 100 μL of water sample was transferred to a petri dish containing 15 mL sterilized medium. The plates were incubated at 37°C for 48 hour so the colonies can be counted. All measurements were done in triplicate, and three blanks were set as the control.

The cultivation medium was composed of peptone (20 g/L), beef extract (6 g/L), lactose (10 g/L), NaCl (10 g/L), bromocresol purple (16 g/L ethanol-soluble, 2 mL), pH 7.2–7.4. The medium was sterilized at 121°C for 20 min. 10 mL of water sample was transferred to a tube containing 10 mL sterilized medium. The samples were incubated at 37°C for 24 h. All measurements were done with five replicates.

Staining and flow cytometric measurements were performed following the procedures as described previously (Lautenschlager et al. 2013). Each sample water (1 mL) was stained with 10 μL/mL staining solution, a mixture of 100 times diluted SYBR Green I in dimethyl sulfoxide and 20 mmol/L propidium iodide (Invitrogen, USA) at a volume ratio of 50:1. The samples were then incubated in the dark for 25 min before measurement. FCM measurements were performed on a CyFlow Space (Partec, Germany). Green and red fluorescences were collected at 520 nm and 615 nm, respectively. The specific instrumental gain settings were as follows: side scatter (SSC) = 280, FL1 = 410, FL3 = 700, and collected as logarithmic (4 decades) signals. LNA and HNA bacterial concentrations were counted separately and the respective geometrical means of green fluorescence (FL1) and SSC were calculated. All samples were measured in triplicates. In order to be measured by FCM, water samples were diluted in cell-free Milli-Q water and the concentration was always lower than 2 × 10⁵ cells/mL. The detection limit was under 500 cells/s with an average standard deviation of 5% (Liu et al. 2016b).

Turbidity, chrominance, pH and conductivity were measured using an YSI EC300 Water Quality Sonde, and ammonia nitrogen (NH₃), nitrite (NO₂), phosphate (PO₄) and residual chlorine (RCl) were assayed as described in our previous studies (Ma et al. 2016). Heavy metals (Cd, Pb, As and Cr) were measured using inductively coupled plasma mass spectrometry. Standard samples of heavy metal with different concentration gradients were prepared and Milli-Q water was used as the control sample to generate standard curves. Water samples were filtered through a 0.45 μm pore size filter, and directly used to measure the heavy metal concentration. All measurements were done in triplicate.

To illustrate the effect of water physicochemical properties on LNA and HNA bacterial characteristics (including the specific growth rate and proportion of LNA and HNA) and variance of water quality, a multivariate constrained ordination technique (redundancy analysis, RDA) was performed by R statistical software using the package ‘Vegan’ (http://www.r-project.org/) after data centralization and standardization, and the Monte Carlo test was used to examine the significance of correlations between LNA and HNA bacterial characteristics and water physicochemical properties in RDA. One-way analysis of variance was conducted to compare data variation.

Furthermore, the results showed that LNA bacterial specific growth rate was positively correlated to the AOC concentration (Pearson's R = 0.987, P < 0.01) (Figure 4). In contrast, no significant correlation between HNA growth rate and AOC was observed (Figure 4). For instance, the AOC level was similar between Outlet 1 and 4 (22.5 vs. 30.6 μg C/L), but HNA specific growth rate in Outlet 1 (1.279 ± 0.069/d) was significantly higher than that of Outlet 4 (0.896 ± 0.033/d) (P < 0.01). This may be due to the variation in carbon utilization ability among HNA bacterial community. Furthermore, factors including disinfection concentration, pH, temperature, etc., could also affect the correlation between bacterial growth rate and AOC concentration in DWDSs (Vital et al. 2007; Liu et al. 2015). Hence, more water samples from different outlets and in different DWDSs should be tested in order to have a better understanding of bacterial growth characteristics in drinking water.

The results showed that the water quality had great variance among different outlets where outlets was clustered together based on their location and linked to different variables in Figure 4 (indicated by corresponding individual cycles, Pb concentration was not shown as it was below the detection limit except in Outlet 1), it was reflected that each outlet corresponded to different individual physicochemical environments. The results indicated that the water had experienced complex changes during the transport process in the water supply network after leaving the treatment plant. The variations could result from variations in hydraulic conditions (e.g. flow velocity and residence time) (Abokifa et al. 2016). When flow rate and pressure change, biofilm detachment and loose deposits may occur in DWDSs (Liu et al. 2013b), leading to deterioration of water quality during distribution (Nguyen et al. 2012; Douterelo et al. 2016). It was reported that bacteria associated with pipe wall biofilm and loose deposits contribute to over 98% of the total bacteria in DWDSs (Liu et al. 2014).

AOC and residual chlorine were considered as the main indicators for microbial regrowth in DWDSs (Srinivasan & Harrington 2007). AOC concentration showed significant variation (P < 0.001) among different outlets, which may be a result of different transportation distances. It was suggested that the acceptable AOC concentration for biological stability was 10.9 μg C/L under a minimal chlorine residual condition (0.05 mg/L) (Ohkouchi et al. 2013). When AOC was higher than 10.9 μg C/L but lower than 100 mg C/L, it would require maintaining residual chlorine (higher than 0.5 mg/L of free chlorine or 1.0 mg/L of chloramines) to restrain coliform regrowth in drinking water (Hammes et al. 2010). In the present study, AOC concentration ranged from 22.5 to 258.6 μg C/L and the level of residual chlorine concentration was about 0.06 mg/L in the outlets. Hence, there is a potential risk of bacterial overgrowth in the tested outlets due to their low residual chlorine concentration and high AOC concentration.

Compared with the HPC method, FCM is more efficient and irrespective of culturability. Hence, it can be applied to real time microorganism monitoring in drinking water treatment and supply systems to reduce the risk of water quality in DWDSs (Lautenschlager et al. 2013; Van Nevel et al. 2016). Furthermore, it was reported that some readily culturable bacteria may enter a temporarily non-culturable state in response to certain stimuli. FCM has overcome one major obstacle on cultivability in microbiology studies, which has made the observation of unculturable bacteria feasible (Wang et al. 2010a). Meanwhile, FCM can be used to evaluate the effect of disinfection processes by analyzing the microbial distribution (live and dead bacteria) in drinking water treatment plants (Wang et al. 2010b).

In this study, it was found that typical clusters of LNA and HNA bacteria were observed in the branch ends of DWDSs, an LNA bacteria was the predominant community.

LNA and HNA bacteria showed different responses to water physicochemical properties. The LNA bacteria dominated in relatively high heavy metal (Cd, As) or conductivity conditions, and the HNA bacteria were the predominant in high turbidity or alkalescence environments.

The specific growth rate of LNA bacteria was lower than that of HNA in drinking water, and was positively correlated to AOC concentration.

The authors have no conflict of interest to declare. We are grateful for the financial support from the National Science Foundation of China (31322012 and 21207068).