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Longitudinal dynamics of farmer and livestock nasal and faecal microbiomes and resistomes

Nature Microbiology volume 9, pages 1007–1020 (2024)Cite this article

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Abstract

Globally, half a billion people are employed in animal agriculture and are directly exposed to the associated microorganisms. However, the extent to which such exposures affect resident human microbiomes is unclear. Here we conducted a longitudinal profiling of the nasal and faecal microbiomes of 66 dairy farmers and 166 dairy cows over a year-long period. We compare farmer microbiomes to those of 60 age-, sex- and ZIP code-matched people with no occupational exposures to farm animals (non-farmers). We show that farming is associated with microbiomes containing livestock-associated microbes; this is most apparent in the nasal bacterial community, with farmers harbouring a richer and more diverse nasal community than non-farmers. Similarly, in the gut microbial communities, we identify more shared microbial lineages between cows and farmers from the same farms. Additionally, we find that shared microbes are associated with antibiotic resistance genes. Overall, our study demonstrates the interconnectedness of human and animal microbiomes.

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Fig. 1: Farmer nasal microbiome comprises cow-associated microbes.
Fig. 2: Occupational impacts on the gut microbiome are apparent at higher resolutions.
Fig. 3: Functional metagenomics enables cow gut resistome profiling.
Fig. 4: Spread of cow-associated ARGs to farmers.

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Data availability

All 16S and shotgun sequencing data pertaining to this study are available from the NCBI SRA under BioProject ID PRJNA964705. The databases used in this study include the SILVA database (v.138.1) (https://www.arb-silva.de/documentation/release-1381/), DeconSeq (v.4.3; -dbs hsref38,cow) (https://deconseq.sourceforge.net/), shortBRED (v.0.9.4) (https://github.com/biobakery/biobakery/wiki/shortbred), CARD (v.3.2.2) (https://card.mcmaster.ca/download), NCBI AR gene catalogue (v.2022-04-04.1) (https://www.ncbi.nlm.nih.gov/pathogens/refgene/#), UniRef90 (v.2022-05-29) (https://ftp.uniprot.org/pub/databases/uniprot/uniref/uniref90) and BLASTn (blast-plus) (https://blast.ncbi.nlm.nih.gov/doc/blast-help/downloadblastdata.html#downloadblastdata). Source data are provided with this paper.

Code availability

The code for all computational analyses is available at https://github.com/dantaslab/DOME/tree/main/Scripts.

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Acknowledgements

We thank all study participants, particularly the dairy farmers and dairy-farm owners, who, in spite of their busy schedules and harvesting season, participated in the study and also allowed their cows to be enrolled in the study. We thank and acknowledge R. Pilsner, M. Presson and N. Esser of Marshfield Agricultural Station for helping with biospecimen collection from dairy farms. We would like to acknowledge the IACUC of the University of Wisconsin-Madison for reviewing our animal protocol and guidance. We also thank the staff at The Edison Family Center for Genome Sciences & Systems Biology at the Washington University School of Medicine in St Louis, including E. Martin and B. Koebbe for computational support, J. Hoisington-López and M. Crosby for managing the high-throughput sequencing core, and B. Dee, K. Matheny, J. Theodore and K. Page for administrative support. Finally, we would like to thank the members of the Dantas laboratory for helpful general discussions and comments on the manuscript. We thank the National Institute for Occupational Safety and Health of the US Centers for Disease Control and Prevention (grant no. R01OH011578 to G.D. and S.K.S.) for funding support, as well as Marshfield Clinic Research Institute and Weber Endowment Fund (to S.K.S.), the Society for Healthcare Epidemiology of America Research Scholar Award (to K.V.S.), the Initiative for Maximizing Student Development R25 (grant no. GM103757 to R.C.V.) and the Genome Analysis Training Program T32 (grant no. HG000045 to R.C.V.).

Author information

Author notes

  1. These authors contributed equally: Bejan Mahmud, Rhiannon C. Vargas.

Authors and Affiliations

  1. The Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St Louis, MO, USA

    Bejan Mahmud, Rhiannon C. Vargas, Kimberley V. Sukhum, Sanket Patel, James Liao, Lindsey R. Hall, Akhil Kesaraju & Gautam Dantas

  2. Department of Pathology and Immunology, Division of Laboratory and Genomic Medicine, Washington University School of Medicine, St Louis, MO, USA

    Kimberley V. Sukhum, Sanket Patel, James Liao & Gautam Dantas

  3. Integrated Research Development Laboratory, Marshfield Clinic Research Institute, Marshfield, WI, USA

    Thao Le

  4. Center for Precision Medicine Research, Marshfield Clinic Research Institute, Marshfield, WI, USA

    Terrie Kitchner & Sanjay K. Shukla

  5. Center for Clinical Epidemiology & Population Health, Marshfield Clinic Research Institute, Marshfield, WI, USA

    Erik Kronholm & Jeffrey J. VanWormer

  6. National Farm Medicine Center, Marshfield Clinic Research Institute, Marshfield, WI, USA

    Kyle Koshalek & Casper G. Bendixsen

  7. Computational Informatics in Biology and Medicine program, University of Wisconsin-Madison, Madison, WI, USA

    Sanjay K. Shukla

  8. Center for Genomic Science Innovation, University of Wisconsin-Madison, Madison, WI, USA

    Sanjay K. Shukla

  9. Department of Molecular Microbiology, Washington University School of Medicine, St Louis, MO, USA

    Gautam Dantas

  10. Department of Biomedical Engineering, Washington University in St Louis, St Louis, MO, USA

    Gautam Dantas

  11. Department of Pediatrics, Washington University School of Medicine, St Louis, MO, USA

    Gautam Dantas

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Contributions

S.K.S., G.D., C.G.B. and J.J.V. designed the study and secured the funding. S.K.S., G.D., C.G.B., J.J.V. and K.K. strategized the sample collection method. T.K., T.L. and E.K. performed the logistics of sample collection, processing and management of the sample databases. B.M., R.C.V., K.V.S., S.P. and J.L. processed the samples and prepared the sequencing libraries. B.M., R.C.V., L.R.H. and A.K. conducted the computational analysis. B.M. wrote the initial draft of the manuscript, with subsequent review and editing by R.C.V., L.R.H., C.G.B., J.J.V., S.K.S. and G.D. All authors reviewed and approved the final manuscript.

Corresponding authors

Correspondence to Sanjay K. Shukla or Gautam Dantas.

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The authors declare no competing interests.

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Nature Microbiology thanks Timothy Walsh and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Genus-level richness of nasal and faecal samples.

Genus-level richness of the cow, farmer, and non-farmer nasal (a) and faecal (b) microbiomes across seasons. Boxplots show median (center line), quartiles (box limits), and 1.5x interquartile range (whiskers). Dots correspond to individual samples. Half-violins show the data distribution. P values were calculated using the two-tailed Wilcoxon rank-sum test, with subsequent Benjamini-Hochberg correction for multiple hypotheses.

Source data

Extended Data Fig. 2 Microbial families differentially abundant in nasal samples.

Microbial families with significant (P < 0.05) differential abundances between farmer (n = 145) and non-farmer (n = 103) nasal microbiomes are indicated. For each family, when significant, the corresponding coefficient in cow samples (n = 363) relative to those of non-farmers is also shown. Points denote mean coefficients; whiskers correspond to standard error. Enrichment tested using MaAsLin 2 (see Methods), Benjamini-Hochberg correction for multiple hypotheses.

Source data

Extended Data Fig. 3 Principal coordinate analysis of Bray-Curtis dissimilarities.

Analysis consists of genus (a–h) and species (i-l) compositions of human fecal samples across seasons. Taxonomic profiling is based on 16S (a-d) and shotgun metagenomic (e-l) sequencing data P values were calculated using PERMANOVA and adjusted for multiple hypotheses using the Benjamini-Hochberg method.

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Extended Data Fig. 4 Average fecal Bray-Curtis distance of farmers and non-farmers to cows residing in the same or different collection site.

Beta diversities are based on genus (a) or resistome (b) compositions. Boxplots show median (center line), quartiles (box limits), and 1.5x interquartile range (whiskers). Dots correspond to individual samples. Half-violins show the data distribution. P values were calculated using the two-tailed Wilcoxon rank-sum test, with subsequent Benjamini-Hochberg correction for multiple hypotheses. No significant differences (P < 0.05) were identified.

Source data

Extended Data Fig. 5 Differential abundances of microbiome characteristics across farmers and non-farmers.

Species (a), genera (b), and microbial pathways (c) with significant (P < 0.05) differential abundances between farmer (n = 134) and non-farmer (n = 95) fecal microbiomes are indicated. Points denote mean coefficients; whiskers correspond to standard error. Enrichment tested using MaAsLin 2 (see Methods), Benjamini-Hochberg correction for multiple hypotheses.

Source data

Extended Data Fig. 6 Total ARG relative abundances of cow, farmer, and non-farmer fecal microbiomes across seasons.

Boxplots show median (center line), quartiles (box limits), and 1.5x interquartile range (whiskers). Dots correspond to individual samples. Half-violins show the data distribution. P values were calculated using the two-tailed Wilcoxon rank-sum test, with subsequent Benjamini-Hochberg correction for multiple hypotheses.

Source data

Extended Data Fig. 7 ARGs overrepresented in the cow gut.

ARGs enriched (P < 0.05) in the cow gut resistome (n = 330) relative to that of humans (n = 229). The enrichment coefficients were determined through MaAsLin2 (see Methods), with Benjamini-Hochberg correction for multiple hypotheses. The ARGs are colored according to the corresponding antibiotic classes.

Source data

Extended Data Fig. 8 Farmer and non-farmer gut ARG richness in spring.

The analysis of richness involved only ARGs correlated with genera that i) are enriched in the farmer and cow nasal microbiomes relative to that of non-farmers, and ii) represent the microbial lineages cooccurring the farmer and cow guts. Boxplots show median (center line), quartiles (box limits), and 1.5x interquartile range (whiskers). Dots correspond to individual samples. Half-violins show the data distribution. The P value was calculated using the two-tailed Wilcoxon rank-sum test.

Source data

Extended Data Fig. 9 Rarefaction analysis for 16S rRNA sequencing.

Analysis consisted of fecal (a,c,e) and nasal (b,d,f) samples of cows (a, b), farmers (c, d), and non-farmers (e, f). The analysis was based on genus richness. Boxplots show median (center line), quartiles (box limits), and 1.5x interquartile range (whiskers). Dots correspond to individual subsamples. Half-violins show the data distribution. The differences among subsamples were tested for significance using Dunn’s test, and the P values adjusted for multiple hypotheses using Benjamini-Hochberg. ns, not significant.

Source data

Extended Data Fig. 10 Rarefaction analysis for shotgun sequencing.

Analysis consisted of cow (a), farmer (b), and non-farmer (c) fecal samples. The analysis was based on ARG richness. Boxplots show median (center line), quartiles (box limits), and 1.5x interquartile range (whiskers). Dots correspond to individual subsamples. Half-violins show the data distribution. The differences among subsamples were tested for significance using Dunn’s test, and the P values adjusted for multiple hypotheses using Benjamini-Hochberg. ns, not significant.

Source data

Supplementary information

Supplementary Information

Information guide to Supplementary Tables 1–8.

Reporting Summary

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Supplementary Tables 1–8

Supplementary Tables 1–8.

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Mahmud, B., Vargas, R.C., Sukhum, K.V. et al. Longitudinal dynamics of farmer and livestock nasal and faecal microbiomes and resistomes. Nat Microbiol 9, 1007–1020 (2024). https://doi.org/10.1038/s41564-024-01639-4

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