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Multiple micronutrient deficiencies in early life cause multi-kingdom alterations in the gut microbiome and intrinsic antibiotic resistance genes in mice

Nature Microbiology volume 8, pages 2392–2405 (2023)Cite this article

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Abstract

Globally, ~340 million children suffer from multiple micronutrient deficiencies, accompanied by high pathogenic burden and death due to multidrug-resistant bacteria. The microbiome is a reservoir of antimicrobial resistance (AMR), but the implications of undernutrition on the resistome is unclear. Here we used a postnatal mouse model that is deficient in multiple micronutrients (that is, zinc, folate, iron, vitamin A and vitamin B12 deficient) and shotgun metagenomic sequencing of faecal samples to characterize gut microbiome structure and functional potential, and the resistome. Enterobacteriaceae were enriched in micronutrient-deficient mice compared with mice fed an isocaloric experimental control diet. The mycobiome and virome were also altered with multiple micronutrient deficiencies including increased fungal pathogens such as Candida dubliniensis and bacteriophages. Despite being antibiotic naïve, micronutrient deficiency was associated with increased enrichment of genes and gene networks encoded by pathogenic bacteria that are directly or indirectly associated with intrinsic antibiotic resistance. Bacterial oxidative stress was associated with intrinsic antibiotic resistance in these mice. This analysis reveals multi-kingdom alterations in the gut microbiome as a result of co-occurring multiple micronutrient deficiencies and the implications for antibiotic resistance.

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Fig. 1: Micronutrient deficiencies reshape gut bacterial community.
Fig. 2: Micronutrient deficiencies alter mycobiome and virome.
Fig. 3: Indicator analyses identify key microbes driving change.
Fig. 4: Low micronutrients expand opportunistic bacteria and fungi.
Fig. 5: Opportunistic bacteria correlate with resistome expansion.
Fig. 6: Bacterial oxidative stress correlates with intrinsic resistance.

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Data availability

All metadata and annotated taxonomic and functional data necessary to replicate these analyses and Sanger sequencing results are stored in the GitHub repository at https://github.com/armetcal/Littlejohn_Micronutrient_ARG_2023. Raw sequencing reads were submitted to the European Nucleotide Archive (ENA) under the project code PRJEB56324Source data are provided with this paper.

Code availability

All R code necessary to replicate these analyses is stored in the GitHub repository at https://github.com/armetcal/Littlejohn_Micronutrient_ARG_2023.

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Acknowledgements

This work was supported by research grants from the Canadian Institutes of Health Research (CIHR) (to B.B.F., FDN-159935). We thank K. Jones for contribution to the conceptualization of this model; T. Bozorgmehr for assistance with animal experiments; W. Deng for insights into this manuscript; and the Finlay Lab overall for support and feedback. We also thank S. Bloom for the insightful input and feedback on the metagenomics data and antibiotic susceptibility testing; A. Kozik, E. Cunningham-Oakes and R. Robertson for critical feedback on the data; Finlay lab member M. Cirstea for the resources and M. Bains (Robert Hancock Lab, University of British Columbia) for the fluoroquinolone and methicillin antibiotics; and A. Sham for critical feedback on the revised manuscript.

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Authors and Affiliations

  1. Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada

    Paula T. Littlejohn, Avril Metcalfe-Roach, Ravi Holani, Haggai Bar-Yoseph, Sarah E. Woodward & B. Brett Finlay

  2. Department of Microbiology and Immunology, University of British Columbia, Vancouver, British Columbia, Canada

    Paula T. Littlejohn, Avril Metcalfe-Roach, Sarah E. Woodward & B. Brett Finlay

  3. Microbiome Insights, Vancouver, British Columbia, Canada

    Erick Cardenas Poire

  4. Department of Cellular and Physiological Sciences, University of British Columbia, Vancouver, British Columbia, Canada

    Yiyun M. Fan

  5. Biochemistry and Molecular Biology Department, University of British Columbia, Vancouver, British Columbia, Canada

    B. Brett Finlay

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  1. Paula T. Littlejohn
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Contributions

P.T.L. conceptualized, designed the study, interpreted data, prepared the original and revised manuscript. A.M.-R. performed the functional, taxonomic and correlation bioinformatics analysis, produced Figures, interpreted data, edited and revised the manuscript. H.B.-Y. performed microbial culturing. R.H. assisted with experiments, paper structure and imaging. Y.M.F. assisted with literature review. E.C.P. performed the CARD and MaAsLin2 analyses and contributed to the methods section. S.E.W. performed the Sanger sequencing experiment, analysis and interpretation. All authors were involved in review and editing. P.T.L., A.M.-R. and B.B.F. approved the final revised version of the manuscript.

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Correspondence to Paula T. Littlejohn or B. Brett Finlay.

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Nature Microbiology thanks Sean Moore, Frank Aarestrup 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 Gut virome assembly is altered by micronutrient deficiencies in early life.

Weanling C57Bl/6 N male mice (n = 10) were given a control or multiple low-micronutrient diet (deficient in vitamin A, B12, B9 [folate], zinc, and iron) ad libitum for 28 days. Shotgun metagenomics sequencing was performed on mouse fecal samples collected at Day 0 and Day 28. (a) Shannon alpha diversity of virome at each time point Day 0 (P = 0.28) and Day 28 (Wilcoxon, P = 0.92) (b) Virile bar plot between groups at the genus level on Day 0 and 28. (c) Virus/bacteriophage altered at the genus level following dietary treatment. All P values were FDR-adjusted (Q). *Q < 0.05, **Q < 0.01, ***Q < 0.001, ****Q < 0.0001. Data shown are from 10 individual mice per group.

Source data

Extended Data Fig. 2 Antibiotic resistome is expanded in micronutrient deficient mice.

ShortBRED and CARD analysis revealed significantly higher antibiotic drug-class genes in the low-micronutrient treated mice. (a) Shannon alpha diversity of resistome (Wilcoxon). (b) Bray-Curtis dissimilarity of resistome (PERMANOVA, D28 LM vs CON P = 0.001, see Supplementary Data). (c) Venn diagram of ARGs that are ≥30% prevalent within a given group. (d) Correlation of antibiotic resistance and mycobiome. (e) Correlation of antibiotic resistance and virome. Corrected P values were calculated using MaAsLin2 and represented as asterisks. *Q < 0.05, **Q < 0.01, *** Q < 0.001, ****Q < 0.0001. Data shown are from 10 individual mice per group.

Source data

Extended Data Fig. 3 Antibiotic resistome is expanded in micronutrient deficient mice.

ShortBRED and CARD analysis revealed significantly higher antibiotic drug-class genes in the low-micronutrient treated mice. (a) RPKM counts of antibiotic drug class per group. Corrected P values were calculated using MaAsLin2 and represented as asterisks. (b) Spearman correlation analysis heatmap of opportunistic bacteria and antibiotic drug class. *Q < 0.05, **Q < 0.01, *** Q < 0.001, ****Q < 0.0001. Data shown are from 10 individual mice per group.

Source data

Supplementary information

Supplementary Information

Supplementary Figs. 1–3, Materials 1 and 2, and Methods.

Reporting Summary

Peer Review File

Supplementary Tables 1–7

ARG master file, drug class, ARG mechanisms, and family. Sanger sequencing results, Dietary ingredients and ARG top hits.

Source data

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Littlejohn, P.T., Metcalfe-Roach, A., Cardenas Poire, E. et al. Multiple micronutrient deficiencies in early life cause multi-kingdom alterations in the gut microbiome and intrinsic antibiotic resistance genes in mice. Nat Microbiol 8, 2392–2405 (2023). https://doi.org/10.1038/s41564-023-01519-3

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