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Drinking water chlorination has minor effects on the intestinal flora and resistomes of Bangladeshi children

Nature Microbiology volume 7, pages 620–629 (2022)Cite this article

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Abstract

Healthy development of the gut microbiome provides long-term health benefits. Children raised in countries with high infectious disease burdens are frequently exposed to diarrhoeal pathogens and antibiotics, which perturb gut microbiome assembly. A recent cluster-randomized trial leveraging >4,000 child observations in Dhaka, Bangladesh, found that automated water chlorination of shared taps effectively reduced child diarrhoea and antibiotic use. In this substudy, we leveraged stool samples collected from 130 children 1 year after chlorine doser installation to examine differences between treatment and control children’s gut microbiota. Water chlorination was associated with increased abundance of several bacterial genera previously linked to improved gut health; however, we observed no effects on the overall richness or diversity of taxa. Several clinically relevant antibiotic resistance genes were relatively more abundant in the gut microbiome of treatment children, possibly due to increases in Enterobacteriaceae. While further studies on the long-term health impacts of drinking chlorinated water would be valuable, we conclude that access to chlorinated water did not substantially impact child gut microbiome development in this setting, supporting the use of chlorination to increase global access to safe drinking water.

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Fig. 1: Differentially abundant gut taxa between treatment and control children aged 6–61 months, and effects on overall richness and diversity.
Fig. 2: Effects of automated water chlorination on the taxonomic structure of children’s gut microbiomes in urban Bangladesh.
Fig. 3: Effects of automated water chlorination on the richness, diversity and relative abundance of ARGs harboured by children’s intestinal flora in urban Bangladesh.

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Data availability

All raw reads (human sequences removed) were deposited in the Sequence Read Archive (SRA; https://www.ncbi.nlm.nih.gov/sra) under the project number PRJNA726052. Metadata are publicly available at the following link: https://osf.io/wb3pv/. We accessed the following publicly available database to conduct our analyses: NCBI’s taxonomy database (https://ftp.ncbi.nlm.nih.gov/pub/taxonomy/), downloaded 23 November 2021. Source data are provided with this paper.

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Acknowledgements

We thank N. Akter for field management. This work was funded by the Thrasher Research Fund (grant no. 14205) and The World Bank Strategic Impact Evaluation Fund. M.L.N. was supported by National Institutes of Health (NIH) award KL2TR002545 and the Stuart B. Levy Center for Integrated Management of Antimicrobial Resistance at Tufts (Levy CIMAR), a collaboration of Tufts Medical Center and the Tufts University Office of the Vice Provost for Research (OVPR) Research and Scholarship Strategic Plan (RSSP). C.J.W. and A.M.E. were supported by the National Institute of Allergy and Infectious Diseases of the NIH under award no. U19AI110818 to the Broad Institute. E.R.F. was supported by the NSF Postdoctoral Research Fellowships in Biology Programme under Grant No. 1906957. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of any of the aforementioned funding organizations. The funder had no role in data collection, data analysis, data interpretation or writing of this report

Author information

Author notes

  1. These authors contributed equally: Timothy R. Julian, Amy J. Pickering.

Authors and Affiliations

  1. Department of Civil and Environmental Engineering, Tufts University, Medford, MA, USA

    Maya L. Nadimpalli, Erica R. Fuhrmeister & Jenna M. Swarthout

  2. Stuart B. Levy Center for Integrated Management of Antimicrobial Resistance (Levy CIMAR), Tufts University, Boston, MA, USA

    Maya L. Nadimpalli & Amy J. Pickering

  3. Bioinformatics Unit, Ramón y Cajal Health Research Institute (IRYCIS), Madrid, Spain

    Val F. Lanza

  4. Network Research Center for Infectious Diseases (CIBERINFEC), Madrid, Spain

    Val F. Lanza

  5. Eawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland

    Maria Camila Montealegre, Lisa Teichmann, Lea Caduff & Timothy R. Julian

  6. International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka, Bangladesh

    Sonia Sultana & Mohammad Aminul Islam

  7. Infectious Disease & Microbiome Program, Broad Institute, Cambridge, MA, USA

    Colin J. Worby & Ashlee M. Earl

  8. Energy and Resources Group, University of California Berkeley, Berkeley, CA, USA

    Yoshika S. Crider

  9. King Center on Global Development, Stanford University, Stanford, CA, USA

    Yoshika S. Crider

  10. Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA

    Joe Brown

  11. Infectious Diseases and Geographic Medicine, Stanford University, Stanford, CA, USA

    Stephen P. Luby

  12. Paul G. Allen School for Global Health, Washington State University, Pullman, WA, USA

    Mohammad Aminul Islam

  13. Swiss Tropical and Public Health Institute, Basel, Switzerland

    Timothy R. Julian

  14. University of Basel, Basel, Switzerland

    Timothy R. Julian

  15. Department of Civil and Environmental Engineering, University of California, Berkeley, CA, USA

    Amy J. Pickering

  16. Blum Center for Developing Economies, University of California, Berkeley, CA, USA

    Amy J. Pickering

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Contributions

A.J.P. and T.R.J. conceptualized this follow-up study and obtained funding. A.J.P., S.S. and S.P.L. designed the original trial. A.J.P., S.S., Y.S.C. and J.S. contributed to data collection in the original trial. M.C.M., L.T., L.C. and E.R.F. performed stool DNA extractions. Y.S.C. and J.B. contributed to the enteric pathogen analysis. C.W., A.M.E., M.A.I. and V.F.L. provided input on methods and data interpretation. M.L.N. and V.F.L. contributed to metagenomic data analysis. M.L.N. wrote the first draft. All co-authors contributed to writing and editing the manuscript.

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Correspondence to Timothy R. Julian or Amy J. Pickering.

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Extended data

Extended Data Fig. 1 Treatment coefficients generated by the R package corncob, representing the additive change in the logit-transformed relative abundance of bacterial genera between treatment and control children, compared with the log of the ratio of the mean relative abundance among treatment children to the mean relative abundance among control children, that is, the log fold change.

Treatment coefficients generated by corncob generally approximate the log fold change.

Source data

Extended Data Fig. 2 Average fraction of reads from 130 Bangladeshi children’s gut metagenomes that were not classified to any taxonomy by Kraken2, stratified by age.

Error bars represent the 95% confidence interval around the mean. The proportion of unclassified reads significantly differed at the P= 0.05 level between treatment and control children aged 15–30 months by a two-sided, two-sample t-test, but not among other age strata. The number of biologically independent samples considered for each age strata is as follows: 6–14 months, n = 27; 15–30 months, n = 51; 31–61 months, n = 52.

Source data

Extended Data Fig. 3 Gut resistomes and antibiotic consumption patterns among 130 children participating in an automated water chlorination trial in urban Bangladesh.

Panel a depicts relative abundance of antibiotic resistance genes (ARGs) belonging to each ARG class harboured by the fecal metagenomes of control and treatment children, expressed as reads per kilobase per million (RPKM) mapped reads. Panel b depicts the average proportion of control and treatment children whose caretakers reported they had consumed antibiotics in the two months prior to stool collection, stratified by age. Error bars represent the 95% CI around the mean. Antibiotic use was significantly associated with age strata (P < 0.001 by chi-square) but was not associated with treatment status in this subset of children from the parent trial. For both panels, the number of biologically independent samples considered for each age strata is as follows: 6–14 months, n = 27; 15–30 months, n = 51; 31–61 months, n = 52.

Source data

Extended Data Fig. 4 Differences in resistance to specific antibiotic classes as detected in the fecal metagenomes of 130 children participating in an automated water chlorination trial in urban Bangladesh.

Treatment coefficients were generated by the R package corncob. Positive treatment coefficients indicate that ARGs belonging to the given antibiotic class were relatively more abundant among treatment children relative to controls; negative treatment coefficients indicate ARGs belonging to the given antibiotic class were relatively less abundant. Error bars depict the 95% confidence interval around the treatment coefficient. The number of biologically independent samples examined for each age strata is as follows: 15–30 months, n = 51; 31–61 months, n = 52. The “Overall” category included 130 biologically independent samples.

Source data

Extended Data Fig. 5 Relative abundance of genes conferring resistance to medically important antibiotics in the fecal metagenomes of 130 children participating in an automated chlorinated water intervention trial in urban Bangladesh.

Genes conferring resistance to fluoroquinolones (qnr), azithromycin (mph), fosfomycin (fos), beta-lactams (blaOXA), and third-generation cephalosporins (blaCTX) were detected. Genes conferring resistance to colistin and carbapenems, which are considered ’last resort’ antibiotics, were not detected.

Source data

Extended Data Fig. 6 Comparison of two sets of extraction controls, extracted from the stool of a child aged 6–14 months (Sample A) and 31–61 months (Sample B).

Within each set of duplicates, we observed a similar relative abundance of bacterial families and genera that comprised at least 1% of bacterial reads among all fecal metagenomes sequenced for this study. We observed some discordance in the genera that were identified within each extraction pair (3 discordant genera versus 1552 concordant genera among extraction duplicates for Sample A; 85 discordant genera versus 1063 concordant genera among extraction duplicates for Sample B); however, all discordant genera were of exceptionally low abundance (<0.007%).

Source data

Supplementary information

Supplementary Information

Supplementary Abstract (following CONSORT 2010 guidelines for abstracts), CONSORT 2010 checklist of information to include when reporting a cluster-randomized trial and Supplementary Tables 1–3.

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Nadimpalli, M.L., Lanza, V.F., Montealegre, M.C. et al. Drinking water chlorination has minor effects on the intestinal flora and resistomes of Bangladeshi children. Nat Microbiol 7, 620–629 (2022). https://doi.org/10.1038/s41564-022-01101-3

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