Composition of active bacterial communities and presence of opportunistic pathogens in disinfected and non-disinfected drinking water distribution systems in Finland.

Many factors, including microbiome structure and activity in the drinking water distribution system (DWDS), affect the colonization potential of opportunistic pathogens. The present study aims to describe the dynamics of active bacterial communities in DWDS and identify the factors that shape the community structures and activity in the selected DWDSs. Large-volume drinking water and hot water, biofilm, and water meter deposit samples were collected from five DWDSs. Total nucleic acids were extracted, and RNA was further purified and transcribed into its cDNA from a total of 181 water and biofilm samples originating from the DWDS of two surface water supplies (disinfected with UV and chlorine), two artificially recharged groundwater supplies (non-disinfected), and a groundwater supply (disinfected with UV and chlorine). In chlorinated DWDSs, concentrations of <0.02-0.97 mg/l free chlorine were measured. Bacterial communities in the RNA and DNA fractions were analysed using Illumina MiSeq sequencing with primer pair 341F-785R targeted to the 16S rRNA gene. The sequence libraries were analysed using QIIME pipeline, Program R, and MicrobiomeAnalyst. Not all bacterial cells were active based on their 16S rRNA content, and species richness was lower in the RNA fraction (Chao1 mean value 490) than in the DNA fraction (710). Species richness was higher in the two DWDSs distributing non-disinfected artificial groundwater (Chao1 mean values of 990 and 1 000) as compared to the two disinfected DWDSs using surface water (Chao1 mean values 190 and 460) and disinfected DWDS using ground water as source water (170). The difference in community structures between non-disinfected and disinfected water was clear in the beta-diversity analysis. Distance from the waterworks also affected the beta diversity of community structures, especially in disinfected distribution systems. The two most abundant bacteria in the active part of the community (RNA) and total bacterial community (DNA) belonged to the classes Alphaproteobacteria (RNA 28 %, DNA 44 %) and Gammaproteobacteria (RNA 32 %, DNA 30 %). The third most abundant and active bacteria class was Vampirovibrionia (RNA 15 %), whereas in the total community it was Paceibacteria (DNA 11 %). Class Nitrospiria was more abundant and active in both cold and hot water in DWDS that used chloramine disinfection compared to non-chlorinated or chlorine-using DWDSs. Thirty-eight operational taxonomic units (OTU) of Legionella, 30 of Mycobacterium, and 10 of Pseudomonas were detected among the sequences. The (RT)-qPCR confirmed the presence of opportunistic pathogens in the DWDSs studied as Legionella spp. was detected in 85 % (mean value 4.5 × 104 gene copies/100 ml), Mycobacterium spp. in 95 % (mean value 8.3 × 106 gene copies/100 ml), and Pseudomonas spp. in 78 % (mean value 1.6 × 105 gene copies/100 ml) of the water and biofilm samples. Sampling point inside the system (distance from the waterworks and cold/hot system) affected the active bacterial community composition. Chloramine as a chlorination method resulted in a recognizable community composition, with high abundance of bacteria that benefit from the excess presence of nitrogen. The results presented here confirm that each DWDS is unique and that opportunistic pathogens are present even in conditions when water quality is considered excellent.

Potential and limitations of a pilot-scale drinking water distribution system for bacterial community predictive modelling.

Waterborne disease outbreaks are a persistent and serious threat to public health according to reported incidents across the globe. Online drinking water quality monitoring technologies have evolved substantially and have become more accurate and accessible. However, using online measurements alone is unsuitable for detecting microbial regrowth, potentially including harmful species, ahead of time in the distribution systems. Alternatively, observational data could be collected periodically, e.g. once per week or once per month and it could include a representative set of variables: physicochemical water characteristics, disinfectant concentrations, and bacterial abundances, which would be a valuable source of knowledge for predictive modelling that aims to reveal pathogen-related threats. In this study, we utilised data collected from a pilot-scale drinking water distribution system. A data-driven random forest model was used for predictive modelling and was trained for nowcasting and forecasting abundances of bacterial groups. In all the experiments, we followed the realistic crossline scenario, which means that when training and testing the models the data is collected from different pipelines. In spite of the more accurate results of the nowcasting, the 1-week forecasting still provided accurate predictions of the most abundant bacteria, their rapid increase and decrease. In the future predictive modelling might be used as a tool in designing control measures for opportunistic pathogens which are able to multiply in the favourable conditions in drinking water distribution systems (DWDS). Eventually, the forecasting information will be able to produce practically helpful data for controlling the DWDS regrowth.

Active eukaryotes in drinking water distribution systems of ground and surface waterworks.

BACKGROUND: Eukaryotes are ubiquitous in natural environments such as soil and freshwater. Little is known of their presence in drinking water distribution systems (DWDSs) or of the environmental conditions that affect their activity and survival. METHODS: Eukaryotes were characterized by Illumina high-throughput sequencing targeting 18S rRNA gene (DNA) that estimates the total community and the 18S rRNA gene transcript (RNA) that is more representative of the active part of the community. DWDS cold water (N = 124), hot water (N = 40), and biofilm (N = 16) samples were collected from four cities in Finland. The sampled DWDSs were from two waterworks A-B with non-disinfected, recharged groundwater as source water and from three waterworks utilizing chlorinated water (two DWDSs of surface waterworks C-D and one of ground waterworks E). In each DWDS, samples were collected from three locations during four seasons of 1 year. RESULTS: A beta-diversity analysis revealed that the main driver shaping the eukaryotic communities was the DWDS (A-E) (R = 0.73, P < 0.001, ANOSIM). The kingdoms Chloroplastida (green plants and algae), Metazoa (animals: rotifers, nematodes), Fungi (e.g., Cryptomycota), Alveolata (ciliates, dinoflagellates), and Stramenopiles (algae Ochrophyta) were well represented and active-judging based on the rRNA gene transcripts-depending on the surrounding conditions. The unchlorinated cold water of systems (A-B) contained a higher estimated total number of taxa (Chao1, average 380-480) than chlorinated cold water in systems C-E (Chao1 ≤ 210). Within each DWDS, unique eukaryotic communities were identified at different locations as was the case also for cold water, hot water, and biofilms. A season did not have a consistent impact on the eukaryotic community among DWDSs. CONCLUSIONS: This study comprehensively characterized the eukaryotic community members within the DWDS of well-maintained ground and surface waterworks providing good quality water. The study gives an indication that each DWDS houses a unique eukaryotic community, mainly dependent on the raw water source and water treatment processes in place at the corresponding waterworks. In particular, disinfection as well as hot water temperature seemed to represent a strong selection pressure that controlled the number of active eukaryotic species.