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Interconnected microbiomes and resistomes in low-income human habitats

Abstract

Antibiotic-resistant infections annually claim hundreds of thousands of lives worldwide. This problem is exacerbated by exchange of resistance genes between pathogens and benign microbes from diverse habitats. Mapping resistance gene dissemination between humans and their environment is a public health priority. Here we characterized the bacterial community structure and resistance exchange networks of hundreds of interconnected human faecal and environmental samples from two low-income Latin American communities. We found that resistomes across habitats are generally structured by bacterial phylogeny along ecological gradients, but identified key resistance genes that cross habitat boundaries and determined their association with mobile genetic elements. We also assessed the effectiveness of widely used excreta management strategies in reducing faecal bacteria and resistance genes in these settings representative of low- and middle-income countries. Our results lay the foundation for quantitative risk assessment and surveillance of resistance gene dissemination across interconnected habitats in settings representing over two-thirds of the world’s population.

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Figure 1: RES and PST human faecal microbiota and resistomes versus global populations.
Figure 2: Salvadoran rural agriculturalist (RES) human faecal and environmental microbiota and resistomes.
Figure 3: Peruvian peri-urban slum (PST) human faecal and sewage microbiota and resistomes.
Figure 4: Antibiotic resistance proteins found in multiple habitats and genetic contexts in RES and PST.

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Acknowledgements

We thank the residents of our study communities in El Salvador and Peru for their generosity and trust, without which this study would not have been possible; Epilogos Charities Inc. for on-site logistical support and community networking; the Fundación Luis Edmundo Vásquez (FUNDALEV), Universidad Dr. José Matías Delgado, Asociación Benéfica Prisma, and Universidad Peruana Cayetano Heredia for logistical support in the collection and shipment of samples; S. del Pilar Basilio at SEDAPAL in Lima for facilitating access and sample collection at the ‘PTAR San Juan’ WWTP; J. Hoisington-Lopez at the Center for Genome Sciences and Systems Biology and staff at the Genome Technology Access Center at Washington University School of Medicine for generating Illumina sequencing data; S. Alvarez and staff at the Proteomics & Mass Spectrometry Facility at the Donald Danforth Plant Science Center for mass-spectrometry analyses of water samples; and members of the Dantas laboratory for discussions of the results and analyses. This work is supported in part by awards to G.D. through the Edward Mallinckrodt, Jr. Foundation (Scholar Award), the Children’s Discovery Institute (MD-II-2011-117), and the National Institute of General Medical Sciences of the National Institutes of Health (R01-GM099538). Work at the DDPSC was supported by the National Science Foundation (DBI-0521250) for acquisition of the QTRAP LC-MS/MS instrument. E.C.P. is funded by the Department of Defense (DoD) through the National Defense Science and Engineering Graduate (NDSEG) Fellowship. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.

Author information

Authors and Affiliations

Authors

Contributions

D.E.B., G.D., M.T.B., and E.C.P. planned the RES study; D.E.B., G.D., R.H.G., and P.T. planned the PST study; M.T.B. and W.H.A. implemented the RES study approval in El Salvador; E.C.P. implemented the RES study approval in the USA; R.H.G. and L.C. implemented the PST study approval in Peru; P.T. implemented the PST study approval in the USA; M.T.B., W.H.A., K.M.N., M.M.B., G.S.S., and E.C.P. collected surveys and samples in RES; P.T., M.C., and L.C. collected samples in PST; E.C.P., M.M.B., G.S.S., and S.P. extracted DNA and generated 16S, functional metagenomic, and shotgun data for RES samples; P.T. and S.P. extracted DNA and generated 16S, functional metagenomic, and shotgun data for PST samples; E.C.P. and P.T. performed analyses and interpreted results; and E.C.P., P.T., and G.D. wrote the paper with input from other co-authors.

Corresponding author

Correspondence to Gautam Dantas.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Additional information

Assembled functional metagenomic contigs and 16S and shotgun metagenomic reads have been deposited to NCBI GenBank and SRA (PRJNA300541).

Extended data figures and tables

Extended Data Figure 1 Overview of study and methods.

a, Location and overview of study sites in El Salvador and Peru. RES photographs by the authors G.S.S. and M.M.B., PST photographs by the author P.T. b, Antibiotic resistance markers and OTUs detected vs number of samples interrogated by whole metagenome and 16S sequencing by habitat in RES and PST. c, Proportion of metagenomic libraries (n = 67), all antibiotic resistance proteins identified from functional metagenomic selections (n = 1,100), and novel antibiotic resistance proteins identified from functional metagenomic selections (n = 121) originating from each microbial habitat. The percent of total libraries/proteins in that category originating from each microbial habitat is listed above the bar. For all antibiotic resistance proteins and novel antibiotic resistance proteins, the total sums to >100% due to proteins identified in more than one habitat. The number of novel antibiotic resistance proteins vs libraries screened was significantly different than expected compared to the total for human and latrines (chi-squared test, P < 0.005).

Extended Data Figure 2 Human faecal and environmental microbiota from RES and PST.

Microbiota are coloured by habitat. a, PCoA of Bray–Curtis distances between resistomes. (n = 86, n = 10, n = 16, n = 30, n = 4, n = 27 and n = 7 for human, animal, latrine, soil, water, pre-treatment sewage and post-treatment sewage, respectively) Adonis R2 = 22.4%, P < 0.001. b, PCoA of weighted UniFrac distances between microbiota. (n = 105, n = 14, n = 36, n = 84, n = 22, n = 30 and n = 13 for human, animal, latrine, soil, water, pre-treatment sewage and post-treatment sewage, respectively.) Adonis R2 = 41.9%, P < 0.001. c, Procrustes transformation of taxonomic composition vs resistome. Only samples interrogated with both methods were included (n = 172). M2 = 0.360, P < 0.001 (172 dimensions, 999 permutations).

Extended Data Figure 3 Phylogenetic composition of RES and PST human faecal microbiota and published microbiota from previous studies14,19,25.

a, b, e, f, RES vs PST. (RES n = 60, PST n = 45) c, d, g, h, RES and PST vs published human microbiota. (RES n = 60, PST n = 46, other n = 446; see Supplementary Table 14) a, PCoA of weighted UniFrac distances between RES and PST human faecal microbiota, coloured by cohort. Adonis R2 = 29.7%, P < 0.001. b, Taxa discriminating between RES and PST human faecal microbiota as determined by LEfSe. The phylogenetic tree includes all kingdom- to family-level taxa present in any sample. Coloured taxa are discriminative between cohorts and have an LDA effect size of ≥ 4.0; they are coloured by the cohort in which they have the highest abundance. Circle size is relative to the highest abundance in either cohort. c, PCoA of weighted UniFrac distances between RES and PST human faecal microbiota and published human faecal microbiota, coloured by cohort. Cohorts are labelled by lifestyle and study (*(ref. 19), **(ref. 35), ***(ref. 14)). Adonis R2 = 37.6%, P < 0.001. d, Taxa discriminating between host lifestyles for RES and PST and published human faecal microbiota as determined by LEfSe, effect size threshold 3.0. Discriminative taxa are coloured by the host lifestyle in which they are most abundant. e, f, Relative abundances of microbial phyla (e) and families (f) in human faecal microbiota from RES and PST. *P < 0.05, Wilcox test with Bonferroni correction. g, h, Relative abundances of microbial phyla (g) and families (h) in human faecal microbiota from RES and PST and published human faecal microbiota, by lifestyle. *P < 0.05, Kruskal–Wallis test with Bonferroni correction. e–h, Only taxa with a mean relative abundance of ≥ 1% in one cohort/lifestyle are shown. Taxa are in order of increasing overall mean relative abundance. Error bars, s.d.; centre bars, median.

Extended Data Figure 4 RES and PST human faecal resistomes and comparison to the published data sets from ref. 25.

ae, RES and PST resistomes, coloured by cohort. (RES n = 42, PST n = 44) f, g, RES and PST vs published human data sets, coloured by cohort. (RES n = 42, PST n = 44, other n = 53; see Supplementary Table 15) ac, Absolute abundances of antibiotic resistance categories (a), antibiotic targets (b), and mechanisms of action (c) in human faecal resistomes from RES and PST. Only categories with a mean RPKM of >10 in one cohort are shown. Categories are in increasing order of overall mean absolute abundance. Abundances are plotted in log10 scale. *P < 0.05, Wilcox test with Bonferroni correction. d, Number of antibiotic resistance proteins per RES and PST human faecal resistome. *P < 0.05, non-parametric Student’s t-tests. e, PCoA of Bray–Curtis distances between RES and PST resistomes, with abundance-weighted coordinates of the top five most discriminative antibiotic resistance categories enriched in each cohort (squares, size proportional to overall abundance). Adonis R2 = 25.0%, P < 0.001. f, PCoA of Bray–Curtis distances between human faecal resistomes from RES and PST and ref. 25. Adonis R2 = 19.7%, P < 0.001. g, Total reads mapping to antibiotic resistance markers per person (normalized by marker length) normalized by the total reads in that sample in RES and PST and published human faecal microbiota, by cohort. Includes both paired and unpaired reads. The overall distribution of normalized antibiotic resistance read depth was significantly different than expected (Kruskal–Wallis, P < 1 × 10−15). n.s., not significant. All other comparisons are P < 0.05, Wilcox test with Bonferroni correction. ad, f, Error bars, s.d.; centre bars, median.

Extended Data Figure 5 RES human faecal and environmental microbiota and resistomes.

a, b, Relative abundances of microbial phyla (a) and families (b) in RES microbiota, by habitat. (n = 60, n = 6, n = 36, n = 84 and n = 22 for human, animal, latrine, soil, water, respectively) Only taxa with a mean relative abundance of ≥ 1% in one habitat are shown. Taxa are in increasing order of overall mean relative abundance. *P < 0.05, Kruskal–Wallis test with Bonferroni correction. c, d, Absolute abundances of antibiotic resistance categories (c) and antibiotic targets (d) in RES resistomes, by habitat. (n = 42, n = 4, n = 16, n = 30 and n = 4 for human, animal, latrine, soil, water, respectively). Only categories with a mean RPKM of >10 in one habitat are shown. Categories are in increasing order of overall mean absolute abundance. Abundances are plotted in log10 scale. *P < 0.05, Kruskal–Wallis test with Bonferroni correction. ad, Error bars, s.d.; centre bars, median.

Extended Data Figure 6 PST human faecal and environmental microbiota and resistomes.

a, b, Relative abundances of microbial phyla (a) and families (b) in human faecal and sewage microbiota from PST, by stage. (n = 45, n = 16, n = 14 and n = 13 for human, street-access, influent and effluent, respectively) Only taxa with a mean relative abundance of ≥ 1% in one stage are shown. Taxa are in increasing order of overall mean relative abundance. *P < 0.05, Kruskal–Wallis test with Bonferroni correction. c, d, Absolute abundances of antibiotic resistance categories (c) and antibiotic targets (d) in PST resistomes, by stage. (n = 44, n = 14, n = 13 and n = 7 for human, street-access, influent and effluent, respectively). Only categories with a mean RPKM of >10 in one stage are shown. Categories are in increasing order of overall mean absolute abundance. Abundances are plotted in log10 scale. *P < 0.05, Kruskal–Wallis test with Bonferroni correction. ad, Error bars, s.d.; centre bars, median.

Extended Data Figure 7 Antibiotic resistance gene sharing across habitats.

a, Highly cosmopolitan antibiotic resistance proteins. The prevalence of each antibiotic resistance protein in metagenomes from each microbial habitat is depicted for all proteins detected in six of the seven habitats (n = 21). Detection was based on ShortBRED quantification of the protein in each metagenome. Prevalences for an antibiotic resistance protein are linked by lines of the same colour. The shape of each point reflects the number of habitats in which it was found, as well as the minimum prevalence within each habitat. The legend lists the annotation for each protein. b, Protein sequences of antibiotic resistance genes isolated from functional metagenomic selections were clustered at 100% amino acid identity, and the number of metagenomic libraries, microbial habitats (for example, human faecal, soil), and cohorts in which each unique protein (n = 1,100) was encoded were calculated across all members of the cluster. Antibiotic resistance contigs (n = 1,955) were clustered at 90% local identity to identify different genetic contexts, and the number of genetic contexts in which each unique protein was encoded was calculated across all contigs encoding a protein in that cluster. Spearman’s rho = 0.59, P < 2.2 × 10−16, number of genetic contexts vs libraries; rho = 0.47, P < 2.2 × 10−16, number of genetic contexts vs. habitats; Wilcox test, P < 2.2 × 10−16, number of genetic contexts vs cohorts (one or both).

Extended Data Figure 8 Mobilome analyses.

a, PCoA of Bray–Curtis distances between RES and PST human and environmental resistomes, coloured by habitat. (n = 86, n = 10, n = 16, n = 30, n = 4, n = 27 and n = 7 for human, animal, latrine, soil, water, pre-treatment sewage and post-treatment sewage, respectively) Adonis R2 = 24.1%, P < 0.001. b, Procrustes transformation of taxonomic composition vs resistome. Only samples interrogated with both methods were included (n = 172). M2 = 0.493, P < 0.001 (172 dimensions, 999 permutations). c, PCoA of Bray–Curtis distances between RES (n = 42) and PST (n = 44) resistomes, coloured by cohort. Adonis R2 = 31.0%, P < 0.001. df, RES human faecal and environmental microbiota and resistomes, coloured by habitat. (n = 42, n = 4, n = 16, n = 30 and n = 4 for human, animal, latrine, soil, water, respectively). d, PCoA of Bray–Curtis distances between resistomes. Adonis R2 = 32.0%, P < 0.001. e, Observed antibiotic resistance proteins. *P < 0.05, non-parametric Student’s t-tests, Bonferroni correction. f, Percentage of latrine, soil, and water resistomes attributable to human faeces, as determined by SourceTracker29. *P < 0.05, pairwise Wilcox tests, Bonferroni correction. gi, PST human faecal and sewage microbiota and resistomes, coloured by stage. (n = 44, n = 14, n = 13 and n = 7 for human, street-access, influent, effluent, respectively). g, PCoA of Bray–Curtis distances between resistomes. Adonis R2 = 34.8%, P < 0.001. h, Observed antibiotic resistance proteins. *P < 0.05, non-parametric Student’s t-tests, Bonferroni correction. i, Percentage of sewage resistomes attributable to human faeces at each sewage treatment stage, as determined by SourceTracker. *P < 0.05, pairwise Wilcox tests, Bonferroni correction. Error bars, s.d.; centre bars, median.

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Pehrsson, E., Tsukayama, P., Patel, S. et al. Interconnected microbiomes and resistomes in low-income human habitats. Nature 533, 212–216 (2016). https://doi.org/10.1038/nature17672

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