The provision of safe drinking water relies primarily on disinfection as a cornerstone for pathogen control. Since its full-scale implementation in the early 20th century, it has become the standard final treatment step to inactivate microorganisms in most drinking water treatment plants around the world. We have known for a long time that safe drinking water is not sterile. Yet, the extent of microbial diversity in drinking water systems has only recently become apparent thanks to high-throughput DNA sequencing approaches. If we take a closer look at each individual drinking water system, there is heterogeneity from the source to the tap in each of them. Nevertheless, despite all this heterogeneity, we know that the concentrations of drinking water chemical parameters in most systems fall into specific ranges, some are health-based guideline values (i.e. related to potability) while others are related to palatability (e.g. taste, odour, colour). What about the microbial communities across these “chemically-similar” drinking water environments?
We have some knowledge on this subject, but the data that we have is from separate studies that used different approaches for sample collection, processing, analysis and reporting. To try to answer this question, we conducted a collective analysis or meta-analysis of microbial communities in full-scale drinking water systems. At the phylum and class levels, we found broad similarities between drinking water systems irrespective of type and presence/absence of disinfectant residual. But, as you start refining from phylum/class down to family or genus, microbial communities in these systems start looking different from each other. We also ran into the stumbling block that most meta-analysis studies of this type run into — data heterogeneity (sequencing platform, hypervariable region, etc.). So, we knew our findings would not be quantitative but mostly qualitative, to help identify indicative trends that could be important to follow-up on.
First, there were clear distinctions in terms of diversity in systems that maintain a disinfectant residual and those that don’t. This might not seem that surprising — sure, chemical stress in disinfected systems will impact diversity. But, disinfectant residual free systems tend to be more nutrient deprived (i.e. significantly lower growth substrates) than systems that are disinfected. So, this may be a nice follow-up area to dig deeper into in the context of drinking water systems — chemical vs. nutrient stress and microbial diversity, using controlled experiments. It was also interesting to see how abundant and frequently detected potentially predatory bacteria were across drinking water systems (e.g. Bdellovibrio). There is very little that has been done on understanding the dynamics of predatory bacteria in drinking water systems, which is somewhat surprising given that their likely technological application is quite clear (e.g. biological disinfectant or biofouling control in filtration systems). We also looked at potential opportunistic pathogens (OPPs) across datasets, but the big caveat is that partial 16S rRNA gene sequence (or in some cases even full 16S rRNA gene) is not sufficient to confidently identify OPPs. However, we felt it was important to dig deeper into this aspect even if it was solely for the purpose of generating hypotheses about the effect of the use and type of disinfectant residual on potential OPPs and we discuss the differences in the paper.
Data heterogeneity was a big limitation for our paper. On the chemistry side of things, most drinking water studies do a rigourous job of analyzing multiple water quality parameters and use protocols that are US EPA approved or recommended. However, most papers typically only report average/stdev for each measurement per time point/sampling location. It would be extremely helpful if this data is made available as supplementary material with the publication in tab or comma delimited format, so that the data can be easily integrated into comparative studies. On the biological side, it would be nice to have standardized sample collection, extraction and sequencing protocols across studies. It won’t take away all variabilities, but it could be a big step forward. At the same time, we felt that it is probably not the right time to do this. For some reason (…funding), drinking water studies tend to lag a little behind other fields in terms of adoption of new methods. For example, the first paper applying high-throughput amplicon sequencing to study drinking water microbial communities was more than four years after the first paper describing this method. Also, long-read amplicon sequencing is here and here, and standardizing primer choice for short-read sequencing platforms may be a moot point at this juncture. So, maybe we could standardize sample collection, DNA extraction, etc.? This is possible, but would need a critical mass of researchers in the field to come to reasonable agreement.
What seems probably easier to accomplish immediately is sharing DNA extracts between groups. It is a little challenging because of low concentrations in drinking water DNA extractions (every microliter is precious), but could be possible without making things too onerous. Probably an even easier place to start would be data sharing. We had some challenges getting access to data (and eventually only managed to get access to 66% of datasets that have been used in publications). Not all publishers make data release mandatory and in some cases the data is owned by private water utilities who are not obliged to release it. So, given that we have some way to go in terms of data openness in drinking water research, it is probably much more beneficial to focus on this in the first instance.