Shotgun Metagenomics Reveals Taxonomic and Functional Shifts in Hot Water Microbiome Due to Temperature Setting and Stagnation

Abstract

Hot water premise plumbing has emerged as a critical nexus of energy, water, and public health. The composition of hot water microbiomes is of special interest given daily human exposure to resident flora, especially opportunistic pathogens (OPs), which rely on complex microbial ecological interactions for their proliferation. Here, we applied shotgun metagenomic sequencing to characterize taxonomic and functional shifts in microbiomes as a function of water heater temperature setting, stagnation in distal pipes, and associated shifts in water chemistry. A cross-section of samples from controlled, replicated, pilot-scale hot water plumbing rigs representing different temperature settings (39, 42, and 51°C), stagnation periods (8 h vs. 7 days), and time-points, were analyzed. Temperature setting exhibited an overarching impact on taxonomic and functional gene composition. Further, distinct taxa were selectively enriched by specific temperature settings (e.g., Legionella at 39°C vs. Deinococcus at 51°C), while relative abundances of genes encoding corresponding cellular functions were highly consistent with expectations based on the taxa driving these shifts. Stagnation in distal taps diminished taxonomic and functional differences induced by heating the cold influent water to hot water in recirculating line. In distal taps relative to recirculating hot water, reads annotated as being involved in metabolism and growth decreased, while annotations corresponding to stress response (e.g., virulence disease and defense, and specifically antibiotic resistance) increased. Reads corresponding to OPs were readily identified by metagenomic analysis, with L. pneumophila reads in particular correlating remarkably well with gene copy numbers measured by quantitative polymerase chain reaction. Positive correlations between L. pneumophila reads and those of known protozoan hosts were also identified. Elevated proportions of genes encoding metal resistance and hydrogen metabolism were noted, which was consistent with elevated corrosion-induced metal concentrations and hydrogen generation. This study provided new insights into real-world factors influencing taxonomic and functional compositions of hot water microbiomes. Here metagenomics is demonstrated as an effective tool for screening for potential presence, and even quantities, of pathogens, while also providing diagnostic capabilities for assessing functional responses of microbiomes to various operational conditions. These findings can aid in informing future monitoring and intentional control of hot water microbiomes.

Keywords: Legionella; drinking water; hydrogen metabolism; metagenome; metal resistance; microbiome; opportunistic pathogens; premise plumbing.

FIGURE 1

PCoA plots comparing overall similarities in (A) taxonomic structure and (B) functional gene composition as a function of time, temperature, and stagnation. Blue, red, and black data points represent samples from the control rig (Ctr), experimental rig (Exp), and influent cold water, respectively. Open, half-solid, and solid circles represent samples from T₀ (both rigs run at 39°C), T₁ (experimental rig run at 42°C), and T₂ (experimental rig run at 51°C), respectively. Individual data points are labeled with “R” to indicate samples from the recirculating line, with stagnation time (8 h, 7 d) to indicate samples from distal taps, and with letters (a, b) indicating tap replicates. Dashed arrows correspond to the time series as samples were collected from the same tap from T₀, T₁ to T₂.

FIGURE 2

Effect of temperature setting on genera and functions of the microbiota inhabiting hot water recirculation lines. Temperature setting (39°C vs. 51°C) affects (A) fold-change of the relative abundances (RA) of 241 most abundant genera in recirculating hot waters (39°C or 51°C) in comparison to influent water and (B) proportions of level-1 hierarchical functions of hot water microbes in recirculating lines. Relative abundance (RA) is the count of reads annotated as the taxa normalized by total read counts. Bubble size in panel A reflects the relative abundance of each genus in the influent. In panel B: blue and red circles represent functions with a higher proportion in the control (CtrR) and experimental (ExpR) recirculating hot waters, respectively, relative to the other. Proportion is the count of reads annotated as encoding a function normalized by total read counts. The symbol ‘^∗’ indicates p-value < 1e^-15 and ‘^#’ indicates p-value = 2.28e^-10 when statistically comparing the two samples using STAMP. Refer to Supplementary Figure S5 for full definition of level-1 functions.

FIGURE 3

Functional gene shifts following stagnation of hot water in distal taps sampled at T₂. Comparing recirculating hot waters with tap waters in the proportions of (A) level-1 course categories; (B) level-2 finer classifications of the virulence disease defense function in panel A; and normalized abundance of (C) ARGs. T₂Recirc includes two recirculating waters and T₂Tap has six distal tap samples, from both rigs at T₂. Orange and green circles in panel A represent functions with a higher proportion in T₂Recirc and T₂Tap, respectively. Error bars in panel A represent standard deviations. The symbol ‘^∗’ indicates p-value < 0.05 (0.011 to 0.044), ‘^#’ indicates p-value < 0.001, and – indicates p-values >0.05 (0.241–0.977) from STAMP analysis. Normalized abundance of ARGs was dividing the count of reads annotated as an ARG by the count of 16S rRNA genes.

FIGURE 4

Quantification of OPs DNA sequences in hot water samples. (A) Comparing the numbers of L. pneumophila in water samples (gc/mL) quantified by metagenomics [relative abundance (%) of L. pneumophila (i.e., counts of reads identified as L. pneumophila / total read counts) × 16S rRNA gene counts from qPCR quantification (gc/mL)] with L. pneumophila numbers from direct quantification by qPCR targeting the mip gene. (B) Relative abundances of L. pneumophila, M. avium, and P. aeruginosa (i.e., counts of reads identified as the bacteria species / total read counts in a sample, %) derived from metagenomic analysis. Distinct responses to temperature setting and stagnation are apparent for the three pathogens. LOQ: limit of quantification. Error bars in panel B represent standard deviations of two biological replicates.

FIGURE 5

The abundance of metal resistance genes correlated with metal concentration. Normalized abundance of (A) zinc and (B) iron resistance genes (RPKM, reads per kilobase of transcript per million) in hot water samples correlated positively with zinc and iron levels, respectively, which reduced from T₀ to T₂. All distal tap waters and recirculating water from the same rig at the same time point were grouped together for correlation analysis. Correlation coefficient equals to 0.61–0.76, p < 0.0001. Distal tap water from both rigs operated at 39°C with a stagnation period of 8 h were labeled as diamonds to emphasize solely temporal change.

FIGURE 6

Increased abundance of taxonomic and functional gene markers associated with hydrogen metabolism in hot water. Increased (A) relative abundance of four known species of hydrogen-oxidizing bacteria and (B) proportion of genes responsible for hydrogen oxidation (hydrogenases and NiFe hydrogenase maturation) in hot waters sampled from the recirculating lines and taps, relative to the cold influent water (Influent). Relative abundance (%) or the proportion (%) was calculated by dividing the count of reads annotated as a particular species or as a gene encoding the particular function by total read counts in a sample. Error bars in panel A are standard deviations. The recirculating water group has 2 samples (ExpR and CtrR), and the tap water group has 9 samples from all three time-points. Box-plots in panel B show the maximum, 75th percentile, medium, 25th percentile, and minimum values.