Impact of Water Chemistry, Pipe Material and Stagnation on the Building Plumbing Microbiome

Abstract

A unique microbiome establishes in the portion of the potable water distribution system within homes and other buildings (i.e., building plumbing). To examine its composition and the factors that shape it, standardized cold water plumbing rigs were deployed at the treatment plant and in the distribution system of five water utilities across the U.S. Three pipe materials (copper with lead solder, CPVC with brass fittings or copper/lead combined pipe) were compared, with 8 hour flush cycles of 10 minutes to simulate typical daily use patterns. High throughput Illumina sequencing of 16S rRNA gene amplicons was employed to profile and compare the resident bulk water bacteria and archaea. The utility, location of the pipe rig, pipe material and stagnation all had a significant influence on the plumbing microbiome composition, but the utility source water and treatment practices were dominant factors. Examination of 21 water chemistry parameters suggested that the total chlorine concentration, pH, P, SO42- and Mg were associated with the most of the variation in bulk water microbiome composition. Disinfectant type exerted a notably low-magnitude impact on microbiome composition. At two utilities using the same source water, slight differences in treatment approaches were associated with differences in rare taxa in samples. For genera containing opportunistic pathogens, Utility C samples (highest pH of 9-10) had the highest frequency of detection for Legionella spp. and lowest relative abundance of Mycobacterium spp. Data were examined across utilities to identify a true universal core, special core, and peripheral organisms to deepen insight into the physical and chemical factors that shape the building plumbing microbiome.

Fig 1. Dissimilarity in water chemistry of samples from different utilities, rig locations, and pipe materials.

PCA plot of water chemistry data. Each point represents a water sample, with proximity of points in 2-D space indicative of relative similarities. Principle component (PC) 1 and PC2 are combinations of water chemistry variables that best explained variation among samples. a. samples from five utilities (n = 100 samples), color and shape coded based on utility location; PC1 and PC2 explained 44.2% and 27.3% variation, respectively. b. samples from Utility E (n = 20), shape coded by rig location and color coded by stagnation stage; PC1 and PC2 explained 46.5% and 16.3% each. c. samples from Utility E, color and shape coded by pipe material; PC1 and PC2 explained 46.5% and 16.3% each.

Fig 2. Microbiome taxonomy composition of samples from each rig (phylum level).

Data were combined across all 27 pipe samples for each rig. Relative abundance was calculated as the ratio of sequences. Phyla with relative abundance less than 0.1% were combined into “Other Phyla (RA<0.1%)”.

Fig 3. Dissimilarity in microbiome composition of samples from different utilities, rig locations, and pipe materials.

3-D beta diversity plots derived from jackknifed unweighted (a, b, c) and weighted (d, e, f) UniFrac distance matrices, color coded by: 1) utility (a and d), all samples (n = 60, 54, 59, 60, 59, color = red, blue, yellow, green, purple for A, B, C, D, E, respectively); 2) rig location (b and e), Utility E samples (WTP in blue, n = 29; DS in red, n = 30); 3) pipe material and stagnation (c and f), Utility E, WTP rig samples (n = 9, 9, 9, 3, color = blue, red, green, brown for Copper, CPVC, Copper/lead, and influent, respectively).

Fig 4. Core OTU comparison across each rig location at each utility.

Relative abundance was calculated by normalizing number of core OTU sequences to the total number of sequences within specific Utility.Rig combination. The universal core is defined as OTUs shared among all samples, while the specific core consists of OTUs shared within each Utility.Rig, but not across all samples.

Fig 5. Microbiome composition (genus level) in association with “BEST” water chemistry parameters of Batch 1 samples.

Each point represents microbiome of one sample. CCA1 and CCA2 each explained 43.4% and 21.1% of all five constrained axes generated by Canonical Correspondence Analysis (CCA).