PT  - JOURNAL ARTICLE
AU  - Claire Thom
AU  - Cindy J Smith
AU  - Graeme Moore
AU  - Paul Weir
AU  - Umer Z Ijaz
TI  - Microbiomes in drinking water treatment and distribution: a meta-analysis from source to tap
AID  - 10.1101/2021.08.30.457654
DP  - 2022 Jan 01
TA  - bioRxiv
PG  - 2021.08.30.457654
4099  - http://biorxiv.org/content/early/2022/01/12/2021.08.30.457654.short
4100  - http://biorxiv.org/content/early/2022/01/12/2021.08.30.457654.full
AB  - A meta-analysis of existing and available Illumina 16S rRNA datasets from drinking water source, treatment and drinking water distribution systems (DWDS) were collated to compare changes in abundance and diversity throughout. Samples from bulk water and biofilm were used to assess principles governing microbial community assembly and the value of amplicon sequencing to water utilities. Individual phyla relationships were explored to identify competitive or synergistic factors governing DWDS microbiomes. The relative importance of stochasticity in the assembly of the DWDS microbiome was considered to identify the significance of source and treatment in determining communities in DWDS. Treatment of water significantly reduces overall species abundance and richness, with chlorination of water providing the most impact to individual taxa relationships. The assembly of microbial communities in the bulk water of the source, primary treatment process and DWDS is governed by more stochastic processes, as is the DWDS biofilm. DWDS biofilm is significantly different from bulk water in terms of local contribution to beta diversity, type and abundance of taxa present. Water immediately post chlorination has a more deterministic microbial assembly, highlighting the significance of this process in changing the microbiome, although elevated levels of stochasticity in DWDS samples suggest that this may not be the case at customer taps. 16S rRNA sequencing is becoming more routine, and may have several uses for water utilities, including: detection and risk assessment of potential pathogens such as those within the genera of Legionella and Mycobacterium; assessing the risk of nitrification in DWDS; providing improved indicators of process performance and monitoring for significant changes in the microbial community to detect contamination. Combining this with quantitative methods like flow cytometry will allow a greater depth of understanding of the DWDS microbiome.Competing Interest StatementThe authors have declared no competing interest.