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Daily sampling reveals household-specific water microbiome signatures and shared antimicrobial resistomes in premise plumbing

Nature Water volume 2, pages 1178–1194 (2024)Cite this article

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Abstract

Stagnation in premise plumbing can lead to the degradation of drinking water quality, yet the variability of microbiomes and resistomes in these systems at fine spatiotemporal scales remains poorly understood. Here we track the water microbiome daily across households in St. Louis, Missouri, alongside functional gene profiles and antimicrobial resistomes. Our results show substantial differences in species composition between households, with household identity, instead of temporal fluctuations or specific water-use devices, emerging as the dominant variable shaping microbiome composition. Using LASSO regression models, we identified informative taxa for each household, achieving an average accuracy of 97.5% in estimating a sample’s household origin. Notably, distinct profiles of opportunistic premise plumbing pathogens (OPPPs) were detected, with Mycobacterium gordonae being twice as prevalent as M. chelonae. Community assembly simulations indicated that stochastic processes primarily drive household-level taxonomic variation. In contrast, antimicrobial resistomes and functional gene repertoires were similar across households, likely influenced by common chloramine residuals applied throughout the local water distribution systems. Genes conferring resistance to beta-lactams were prevalent in bathtub faucet water across all households. These results highlight the need to incorporate household-level species variation when assessing health risks from OPPPs and monitoring antimicrobial resistance. These findings also pave the way for new research to better understand plumbing environments as potential routes for the transmission of resistant bacteria and their genes.

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Fig. 1: Schematic of the two sampling campaigns and analyses performed.
Fig. 2: Area charts showing the relative abundance of taxa reported by MetaPhlAn4.
Fig. 3: Variation in microbial community composition across households.
Fig. 4: Variation in microbial community composition within- and between- households.
Fig. 5: Presence and prevalence of OPPPs.
Fig. 6: Antimicrobial resistance across households.
Fig. 7: Functional profile and its correlation to taxonomic profile.
Fig. 8: Community assembly in drinking water microbiome.

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Data availability

The raw DNA sequences from this study are available on NCBI under Bioproject PRJNA1066374.

Code availability

The custom code used in this study is available on GitHub(https://github.com/linglab-washu/DW_daily_dynamics).

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Acknowledgements

This work was supported by a McKelvey School of Engineering Startup Fund and a Ralph E. Powe Junior Faculty Enhancement Award by the Oak Ridge Associated Universities to F.L. This research was also partially supported by the Division of Chemical, Bioengineering, Environmental and Transport Systems (CBET) of the National Science Foundation under award 2047470 to F.L. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the sponsoring organizations, agencies or the US government. We thank D. Giammar, J. Ballard, K. Andres, T.-Y. Lin, and K. Chibwe for valuable discussions. We thank A. Dang, P. Liu, Y. Liu, P. Prathibha, J. Wei, H. Zhang and Z. Zou for collecting the water samples.

Author information

Author notes

  1. Winston Chen

    Present address: Department of Earth and Environmental Science, University of Pennsylvania, Philadelphia, PA, USA

Authors and Affiliations

  1. Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA

    Lin Zhang, David Mantilla-Calderon, Bingdi Liu, Winston Chen & Fangqiong Ling

  2. Institute for Environmental Genomics, University of Oklahoma, Norman, OK, USA

    Daliang Ning & Jizhong Zhou

  3. School of Biological Sciences, University of Oklahoma, Norman, OK, USA

    Daliang Ning & Jizhong Zhou

  4. Division of Computational and Data Sciences, Washington University in St. Louis, St. Louis, MO, USA

    Yirong Xu & Fangqiong Ling

  5. Department of Chemical and Environmental Engineering, University of California, Riverside, CA, USA

    Jinyu Gao & Jinyong Liu

  6. School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ, USA

    Kerry A. Hamilton

  7. The Biodesign Center for Environmental Health Engineering, Arizona State University, Tempe, AZ, USA

    Kerry A. Hamilton

  8. School of Civil Engineering and Environmental Sciences, University of Oklahoma, Norman, OK, USA

    Jizhong Zhou

  9. School of Computer Science, University of Oklahoma, Norman, OK, USA

    Jizhong Zhou

  10. Earth and Environmental Sciences, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Jizhong Zhou

Authors
  1. Lin Zhang
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  2. Daliang Ning
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  3. David Mantilla-Calderon
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  4. Yirong Xu
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  5. Bingdi Liu
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  6. Winston Chen
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  7. Jinyu Gao
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  8. Kerry A. Hamilton
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  9. Jinyong Liu
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  10. Jizhong Zhou
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  11. Fangqiong Ling
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Contributions

L.Z.: conceptualization, data curation, formal analysis, investigation, methodology, software, validation, visualization, writing—original draft, writing—review and editing. D.N.: formal analysis, methodology, software, writing—review and editing. D.M.-C.: writing—original draft, writing—review and editing. Y.X.: investigation, methodology. B.L.: investigation, methodology. W.C.: investigation, writing—review and editing. J.G.: investigation, writing—review and editing. K.A.H.: writing—review andediting. J.L.: investigation, methodology, writing—review and editing. J.Z.: writing—review and editing. F.L.: conceptualization, funding acquisition, project administration, supervision, writing—original draft, writing—review and editing.

Corresponding author

Correspondence to Fangqiong Ling.

Ethics declarations

Competing interests

The Washington University Human Research Protection Office reviewed this project and determined that it did not involve activities that are subject to Institutional Review Board oversight. All participants provided informed consent at enrollment. The authors declare no competing interests.

Peer review

Peer review information

Nature Water thanks Xianghua Wen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Bray Curtis dissimilarities relevant for sample volumetric segments comparisons.

a, The between-group Bray Curtis dissimilarities between each volumetric segments in each households. Each box represents four pairs of comparisons. b, The between and within-group Bray Curtis dissimilarities grouped by sample types. Each box comparing Bray Curtis dissimilarities between different volumetric segments represents 36 pairs of comparisons. Each box comparing Bray Curtis dissimilarities within the same volumetric segment represents 15 pairs of comparisons. In the box plot, the box shows the interquartile range (IQR), which spans from the 25th percentile (Q1) to the 75th percentile (Q3) of the data. The thick line inside the box represents the median of the data. The lower whisker extends from Q1 to the smallest value in the dataset that is greater than or equal to Q1 - 1.5 × IQR (minima); the upper whisker extends from Q3 to the largest value in the dataset that is less than or equal to Q3 + 1.5 × IQR (maxima).

Extended Data Fig. 2 PCoA plot on ARG profiles.

PCoA plot on Hellinger distance between ARG profiles from different homes. Distance was computed on number of reads (each sample rarefied to 18,783 reads, which got 17 samples excluded).

Extended Data Fig. 3 RPKMs of FOAM level 2 functions in methylotrophy.

A heatmap showing RPKMs of FOAM level 2 functions in methylotrophy.

Extended Data Fig. 4 RPKMs of KOs involved in the nitrogen cycle.

A heatmap showing RPKMs of KOs involved in the nitrogen cycle.

Extended Data Fig. 5 PCoA plot of Bray Curtis distances computed on KO profiles.

KO profiles clustered by households (PERMANOVA p = 0.001 R2 = 0.81). P-value was computed based on two-sided tests.

Extended Data Fig. 6 Relative abundances of FOAM level 1 functions of water samples from a pilot-scale hot water plumbing rig study.

A stacked bar chart showing relative abundances of environmentally relevant functional groups at FOAM level 1 of water samples from a pilot-scale hot water plumbing rig study (Dai et al., 2018). The sample labels were kept consistent with the original publication.

Extended Data Fig. 7 RPKMs of level 2 functions in the nitrogen cycle of St. Louis premise plumbing water in comparison to a pilot-scale hot water plumbing rig study.

A heatmap showing RPKMs of level 2 functions in the nitrogen cycle of St. Louis premise plumbing water from this study (STL-1 through 8) in comparison to a previously published pilot-scale hot water plumbing rig study in Blacksburg, VA (those samples of which the sample names starting with “T”, Dai et al., 2018).

Extended Data Fig. 8 RPKMs of KOs involved in the nitrogen cycle of St. Louis premise plumbing water in comparison to a pilot-scale hot water plumbing rig study.

A heatmap showing RPKMs of KOs involved in the nitrogen cycle in bathtub faucet water samples from this study (STL-1 through 8) in comparison to those from a published pilot-scale hot water plumbing rig study in Blacksburg, VA (those samples of which the sample names starting with “T”, Dai et al., 2018).

Extended Data Table 1 PERMANOVA p values between different households, sample types, and volumetric segments
Full size table
Extended Data Table 2 Coefficients and p values of MRM models
Full size table

Supplementary information

Supplementary Information (download PDF )

Supplementary Methods 1 and 2, Results, Discussion and Figs. 1–28.

Reporting Summary (download PDF )

Supplementary Tables 1–12 (download XLSX )

Supplementary Table 1. PERMANOVA P values of species profile between different homes without multiple test correction and after Benjamini–Hochberg and Holm corrections. P values were computed based on two-sided tests. Supplementary Table 2. The accuracy of LASSO models estimating the household origin of samples. Supplementary Table 3. PERMANOVA P values of ASV profiles between different volumetric segments of stagnant bathtub faucet water, stagnant kitchen faucet water and fresh kitchen faucet water. P values were computed based on two-sided tests. Supplementary Table 4. MetaPhlAn4 report for clades related to Mycobacterium, Acinetobacter, Legionella and Pseudomonas as well as their relative abundances (as percentages) in daily samples of households. Note that a detection limit of 0.1% is recommended for the interpretation of these data, while all reports are provided for completeness. Supplementary Table 5. Associations of OP-related clades with environmental variables as shown by linear models. P values for coefficients were computed based on two-sided tests. Supplementary Table 6. PERMANOVA P values of AMR gene profile between different homes after Benjamini–Hochberg and Holm correction. P values were computed based on two-sided tests. Supplementary Table 7. Detection frequency of ARGs with an average RPKM ranking in the top 20. Supplementary Table 8. PERMANOVA P values of KO profile between different homes after Benjamini–Hochberg and Holm correction. P values were computed based on two-sided tests. Supplementary Table 9. Coefficients and P values of MRM models using backwards selection. P values for coefficients were computed based on two-sided tests. Supplementary Table 10. Information of two sampling campaigns. Supplementary Table 11. Catalogue numbers of commercial reagents and kits. Supplementary Table 12. Metagroup setup in iCAMP.

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Zhang, L., Ning, D., Mantilla-Calderon, D. et al. Daily sampling reveals household-specific water microbiome signatures and shared antimicrobial resistomes in premise plumbing. Nat Water 2, 1178–1194 (2024). https://doi.org/10.1038/s44221-024-00345-z

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