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Genomic variation and strain-specific functional adaptation in the human gut microbiome during early life

A Publisher Correction to this article was published on 05 February 2019

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Abstract

The human gut microbiome matures towards the adult composition during the first years of life and is implicated in early immune development. Here, we investigate the effects of microbial genomic diversity on gut microbiome development using integrated early childhood data sets collected in the DIABIMMUNE study in Finland, Estonia and Russian Karelia. We show that gut microbial diversity is associated with household location and linear growth of children. Single nucleotide polymorphism- and metagenomic assembly-based strain tracking revealed large and highly dynamic microbial pangenomes, especially in the genus Bacteroides, in which we identified evidence of variability deriving from Bacteroides-targeting bacteriophages. Our analyses revealed functional consequences of strain diversity; only 10% of Finnish infants harboured Bifidobacterium longum subsp. infantis, a subspecies specialized in human milk metabolism, whereas Russian infants commonly maintained a probiotic Bifidobacterium bifidum strain in infancy. Groups of bacteria contributing to diverse, characterized metabolic pathways converged to highly subject-specific configurations over the first two years of life. This longitudinal study extends the current view of early gut microbial community assembly based on strain-level genomic variation.

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Fig. 1: Strain diversity across species in early gut metagenomes.
Fig. 2: Bifidobacterium strains in DIABIMMUNE children.
Fig. 3: Contributional diversity of microbial pathways.

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Data availability

All 16S rRNA and metagenomic sequencing data are available in the NCBI Sequence Read Archive under BioProject PRJNA497734 and through the DIABIMMUNE microbiome website at https://pubs.broadinstitute.org/diabimmune/.

Change history

  • 05 February 2019

    In the version of this Article originally published, in the first sentence of the second paragraph of the Discussion section, the word “operingrationally” should have read “operationally”. This has now been amended in all versions of the Article.

References

  1. Kundu, P., Blacher, E., Elinav, E. & Pettersson, S. Our gut microbiome: the evolving inner self. Cell 171, 1481–1493 (2017).

    Article  CAS  Google Scholar 

  2. Backhed, F. et al. Dynamics and stabilization of the human gut microbiome during the first year of life. Cell Host Microbe 17, 690–703 (2015).

    Article  Google Scholar 

  3. Chu, D. M. et al. Maturation of the infant microbiome community structure and function across multiple body sites and in relation to mode of delivery. Nat. Med. 23, 314–326 (2017).

    Article  CAS  Google Scholar 

  4. Bach, J. F. The hygiene hypothesis in autoimmunity: the role of pathogens and commensals. Nat. Rev. Immunol. 18, 105–120 (2018).

    Article  CAS  Google Scholar 

  5. Haahtela, T. et al. The biodiversity hypothesis and allergic disease: World Allergy Organization position statement. World Allergy Organ. J. 6, 3 (2013).

    Article  Google Scholar 

  6. Rewers, M. & Ludvigsson, J. Environmental risk factors for type 1 diabetes. Lancet 387, 2340–2348 (2016).

    Article  CAS  Google Scholar 

  7. Arrieta, M. C. et al. Early infancy microbial and metabolic alterations affect risk of childhood asthma. Sci. Transl. Med. 7, 307ra152 (2015).

    Article  Google Scholar 

  8. Arvonen, M. et al. Gut microbiota–host interactions and juvenile idiopathic arthritis. Pediatr. Rheumatol. Online J. 14, 44 (2016).

    Article  Google Scholar 

  9. Simonyte Sjodin, K., Vidman, L., Ryden, P. & West, C. E. Emerging evidence of the role of gut microbiota in the development of allergic diseases. Curr. Opin. Allergy. Clin. Immunol. 16, 390–395 (2016).

    Article  Google Scholar 

  10. Lewis, J. D. et al. Inflammation, antibiotics, and diet as environmental stressors of the gut microbiome in pediatric Crohn’s disease. Cell Host Microbe 18, 489–500 (2015).

    Article  CAS  Google Scholar 

  11. Knip, M. & Siljander, H. The role of the intestinal microbiota in type 1 diabetes mellitus. Nat. Rev. Endocrinol. 12, 154–167 (2016).

    Article  CAS  Google Scholar 

  12. Maffeis, C. et al. Association between intestinal permeability and faecal microbiota composition in Italian children with beta cell autoimmunity at risk for type 1 diabetes. Diabetes Metab. Res. Rev. 32, 700–709 (2016).

    Article  CAS  Google Scholar 

  13. Thaiss, C. A., Zmora, N., Levy, M. & Elinav, E. The microbiome and innate immunity. Nature 535, 65–74 (2016).

    Article  CAS  Google Scholar 

  14. Honda, K. & Littman, D. R. The microbiota in adaptive immune homeostasis and disease. Nature 535, 75–84 (2016).

    Article  CAS  Google Scholar 

  15. Lebreton, F. et al. Emergence of epidemic multidrug-resistant Enterococcus faecium from animal and commensal strains. Preprint at https://doi.org/10.1128/mBio.00534-13 (2013).

  16. Hall, A. B. et al. A novel Ruminococcus gnavus clade enriched in inflammatory bowel disease patients. Genome Med. 9, 103 (2017).

    Article  Google Scholar 

  17. Schonherr-Hellec, S. et al. Clostridial strain-specific characteristics associated with necrotizing enterocolitis. Appl. Environ. Microbiol. 84, e02428-17 (2018).

    Article  Google Scholar 

  18. Bron, P. A., van Baarlen, P. & Kleerebezem, M. Emerging molecular insights into the interaction between probiotics and the host intestinal mucosa. Nat. Rev. Microbiol. 10, 66–78 (2011).

    Article  Google Scholar 

  19. Ward, D. V. et al. Metagenomic sequencing with strain-level resolution implicates uropathogenic E. coli in necrotizing enterocolitis and mortality in preterm infants. Cell Rep. 14, 2912–2924 (2016).

    Article  CAS  Google Scholar 

  20. Hazen, T. H. et al. Genomic diversity of EPEC associated with clinical presentations of differing severity. Nat. Microbiol. 1, 15014 (2016).

    Article  CAS  Google Scholar 

  21. Truong, D. T., Tett, A., Pasolli, E., Huttenhower, C. & Segata, N. Microbial strain-level population structure and genetic diversity from metagenomes. Genome Res. 27, 626–638 (2017).

    Article  CAS  Google Scholar 

  22. Lloyd-Price, J. et al. Strains, functions and dynamics in the expanded Human Microbiome Project. Nature 550, 61–66 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Korpela, K. et al. Selective maternal seeding and environment shape the human gut microbiome. Genome Res. 28, 561–568 (2018).

    Article  CAS  Google Scholar 

  24. Mende, D. R., Sunagawa, S., Zeller, G. & Bork, P. Accurate and universal delineation of prokaryotic species. Nat. Methods 10, 881–884 (2013).

    Article  CAS  Google Scholar 

  25. Asnicar, F. et al. Studying vertical microbiome transmission from mothers to infants by strain-level metagenomic profiling. mSystems 2, e00164-16 (2017).

    Article  Google Scholar 

  26. Nayfach, S., Rodriguez-Mueller, B., Garud, N. & Pollard, K. S. An integrated metagenomics pipeline for strain profiling reveals novel patterns of bacterial transmission and biogeography. Genome Res. 26, 1612–1625 (2016).

    Article  CAS  Google Scholar 

  27. Yassour, M. et al. Strain-level analysis of mother-to-child bacterial transmission during the first few months of life. Cell Host Microbe 24, 146–154 (2018).

    Article  CAS  Google Scholar 

  28. Ferretti, P. et al. Mother-to-infant microbial transmission from different body sites shapes the developing infant gut microbiome. Cell Host Microbe 24, 133–145 (2018).

    Article  CAS  Google Scholar 

  29. Qin, J. et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464, 59–65 (2010).

    Article  CAS  Google Scholar 

  30. Nielsen, H. B. et al. Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes. Nat. Biotechnol. 32, 822–828 (2014).

    Article  CAS  Google Scholar 

  31. Scher, J. U. et al. Expansion of intestinal Prevotella copri correlates with enhanced susceptibility to arthritis. eLife 2, e01202 (2013).

    Article  Google Scholar 

  32. Bottacini, F., van Sinderen, D. & Ventura, M. Omics of bifidobacteria: research and insights into their health-promoting activities. Biochem. J. 474, 4137–4152 (2017).

    Article  CAS  Google Scholar 

  33. Sela, D. A. & Mills, D. A. Nursing our microbiota: molecular linkages between bifidobacteria and milk oligosaccharides. Trends Microbiol. 18, 298–307 (2010).

    Article  CAS  Google Scholar 

  34. Sela, D. A. et al. The genome sequence of Bifidobacterium longum subsp. infantis reveals adaptations for milk utilization within the infant microbiome. Proc. Natl Acad. Sci. USA 105, 18964–18969 (2008).

    Article  CAS  Google Scholar 

  35. Garrido, D. et al. A novel gene cluster allows preferential utilization of fucosylated milk oligosaccharides in Bifidobacterium longum subsp. longum SC596. Sci. Rep. 6, 35045 (2016).

    Article  CAS  Google Scholar 

  36. Sela, D. A. Bifidobacterial utilization of human milk oligosaccharides. Int. J. Food Microbiol. 149, 58–64 (2011).

    Article  CAS  Google Scholar 

  37. Kostic, A. D. et al. The dynamics of the human infant gut microbiome in development and in progression toward type 1 diabetes. Cell Host Microbe 17, 260–273 (2015).

    Article  CAS  Google Scholar 

  38. Yassour, M. et al. Natural history of the infant gut microbiome and impact of antibiotic treatment on bacterial strain diversity and stability. Sci. Transl. Med. 8, 343ra381 (2016).

    Article  Google Scholar 

  39. Vatanen, T. et al. Variation in microbiome LPS immunogenicity contributes to autoimmunity in humans. Cell 165, 842–853 (2016).

    Article  CAS  Google Scholar 

  40. Zhao, G. et al. Intestinal virome changes precede autoimmunity in type I diabetes-susceptible children. Proc. Natl Acad. Sci. USA 114, E6166–E6175 (2017).

    Article  CAS  Google Scholar 

  41. He, Q. et al. Two distinct metacommunities characterize the gut microbiota in Crohn’s disease patients. Gigascience 6, 1–11 (2017).

    Article  CAS  Google Scholar 

  42. Browne, H. P. et al. Culturing of ‘unculturable’ human microbiota reveals novel taxa and extensive sporulation. Nature 533, 543–546 (2016).

    Article  CAS  Google Scholar 

  43. Schloissnig, S. et al. Genomic variation landscape of the human gut microbiome. Nature 493, 45–50 (2013).

    Article  Google Scholar 

  44. Lange, A. et al. Extensive mobilome-driven genome diversification in mouse gut-associated Bacteroides vulgatus mpk. Genome Biol. Evol. 8, 1197–1207 (2016).

    Article  CAS  Google Scholar 

  45. Skennerton, C. T., Imelfort, M. & Tyson, G. W. Crass: identification and reconstruction of CRISPR from unassembled metagenomic data. Nucleic Acids Res. 41, e105 (2013).

    Article  CAS  Google Scholar 

  46. Land, M. et al. Insights from 20 years of bacterial genome sequencing. Funct. Integr. Genomics. 15, 141–161 (2015).

    Article  CAS  Google Scholar 

  47. Snel, B., Bork, P. & Huynen, M. A. Genome phylogeny based on gene content. Nat. Genet. 21, 108–110 (1999).

    Article  CAS  Google Scholar 

  48. Frese, S. A. et al. Persistence of supplemented Bifidobacterium longum subsp. infantis EVC001 in breastfed infants.mSphere 2, e00501-17 (2017).

    Article  Google Scholar 

  49. Franzosa, E. A. et al. Species-level functional profiling of metagenomes and metatranscriptomes. Nat. Methods 15, 962–968 (2018).

    Article  CAS  Google Scholar 

  50. Morris, J. J., Lenski, R. E. & Zinser, E. R. The Black Queen Hypothesis: evolution of dependencies through adaptive gene loss. mBio 3, e00036-12 (2012).

  51. Andreani, N. A., Hesse, E. & Vos, M. Prokaryote genome fluidity is dependent on effective population size. ISME J. 11, 1719–1721 (2017).

    Article  CAS  Google Scholar 

  52. Subramanian, S. et al. Persistent gut microbiota immaturity in malnourished Bangladeshi children. Nature 510, 417–421 (2014).

    Article  CAS  Google Scholar 

  53. Uusitalo, U. et al. Association of early exposure of probiotics and islet autoimmunity in the TEDDY Study. JAMA Pediatr. 170, 20–28 (2016).

    Article  Google Scholar 

  54. Fox, M. J., Ahuja, K. D., Robertson, I. K., Ball, M. J. & Eri, R. D. Can probiotic yogurt prevent diarrhoea in children on antibiotics? A double-blind, randomised, placebo-controlled study. BMJ Open 5, e006474 (2015).

    Article  Google Scholar 

  55. Henrick, B. M. et al. Elevated fecal pH indicates a profound change in the breastfed infant gut microbiome due to reduction of Bifidobacterium over the past century. mSphere 3, e00041-18 (2018).

  56. Insel, R. & Knip, M. Prospects for primary prevention of type 1 diabetes by restoring a disappearing microbe. Preprint at https://doi.org/10.1111/pedi.12756 (2018).

  57. Gevers, D. et al. The treatment-naive microbiome in new-onset Crohn’s disease. Cell Host Microbe 15, 382–392 (2014).

    Article  CAS  Google Scholar 

  58. Edgar, R. C. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10, 996–998 (2013).

    Article  CAS  Google Scholar 

  59. Edgar, R. C. & Flyvbjerg, H. Error filtering, pair assembly and error correction for next-generation sequencing reads. Bioinformatics 31, 3476–3482 (2015).

    Article  CAS  Google Scholar 

  60. McDonald, D. et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J. 6, 610–618 (2012).

    Article  CAS  Google Scholar 

  61. Morgan, X. C. et al. Dysfunction of the intestinal microbiome in inflammatory bowel disease and treatment. Genome Biol. 13, R79 (2012).

    Article  CAS  Google Scholar 

  62. Segata, N. et al. Metagenomic microbial community profiling using unique clade-specific marker genes. Nat. Methods 9, 811–814 (2012).

    Article  CAS  Google Scholar 

  63. Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25–29 (2000).

    Article  CAS  Google Scholar 

  64. Li, D., Liu, C. M., Luo, R., Sadakane, K. & Lam, T. W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676 (2015).

    Article  CAS  Google Scholar 

  65. Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11, 119 (2010).

    Article  Google Scholar 

  66. Fu, L., Niu, B., Zhu, Z., Wu, S. & Li, W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28, 3150–3152 (2012).

    Article  CAS  Google Scholar 

  67. Li, J. et al. An integrated catalog of reference genes in the human gut microbiome. Nat. Biotechnol. 32, 834–841 (2014).

    Article  CAS  Google Scholar 

  68. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    Article  CAS  Google Scholar 

  69. Huerta-Cepas, J. et al. Fast genome-wide functional annotation through orthology assignment by eggNOG-Mapper. Mol. Biol. Evol. 34, 2115–2122 (2017).

    Article  CAS  Google Scholar 

  70. Schliep, K. P. phangorn: phylogenetic analysis in R. Bioinformatics 27, 592–593 (2011).

    Article  CAS  Google Scholar 

  71. Scholz, M. et al. Strain-level microbial epidemiology and population genomics from shotgun metagenomics. Nat. Methods 13, 435–438 (2016).

    Article  CAS  Google Scholar 

  72. Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).

    Article  CAS  Google Scholar 

  73. Huang, K. et al. MetaRef: a pan-genomic database for comparative and community microbial genomics. Nucleic Acids Res. 42, D617–D624 (2014).

    Article  CAS  Google Scholar 

  74. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    Article  CAS  Google Scholar 

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Acknowledgements

The authors thank T. Poon and S. Steelman (Broad Institute) for help with sequence production and sample management, A. Rahnavard for help with HMP SNP haplotype analysis, D. Shungin for discussions and connections regarding the use of infant milk products in Russia, K. Koski and M. Koski (University of Helsinki) for the coordination and database work in the DIABIMMUNE study and T. Reimels for editorial help with writing and figure generation. T.V. was supported by funding from the Juvenile Diabetes Research Foundation (JDRF). A.B.H. is a Merck Fellow of the Helen Hay Whitney Foundation. P.C.M. received funding from the German Research Foundation (grant no. 315980449). C.H. was supported by funding from the JDRF (3-SRA-2016–141-Q-R) and the National Institutes of Health (R24DK110499). M.K. was supported by the European Union Seventh Framework Programme FP7/2007–2013 (202063) and the Academy of Finland Centre of Excellence in Molecular Systems Immunology and Physiology Research (250114). R.J.X. was supported by funding from JDRF (2-SRA-2016–247-S-B and 2-SRA-2018–548-S-B), the National Institutes of Health (DK43351 and AI110498) and the Center for Microbiome Informatics and Therapeutics.

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Authors and Affiliations

Authors

Contributions

T.V., D.R.P., J.S. and P.C.M. analysed the sequencing data. T.D.A., S.R., E.J.O., X.K., R.A.Y., H.J.H. and J.A.P. contributed to B. dorei isolate sequencing. A.B.H. and R.K. contributed to bioinformatic analysis. M.Y., K.L. and H.S. contributed to study design. J.I., S.M.V., R.U., V.T., S.M. and N.D. collected clinical samples. A.C.M., H.L., H.V., C.H., M.K. and R.J.X. served as principal investigators. T.V., D.R.P., J.S., P.C.M., H.V., C.H., M.K. and R.J.X. drafted the manuscript. All authors discussed the results, contributed to critical revisions and approved the final manuscript.

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Correspondence to Ramnik J. Xavier.

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The authors declare no competing interests.

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Supplementary information

Supplementary Information

Supplementary Notes, Supplementary References.

Reporting Summary

Supplementary Table 1

Cohort metadata.

Supplementary Table 2

PERMANOVA results.

Supplementary Table 3

Microbial alpha-diversity.

Supplementary Table 4

Taxonomic associations.

Supplementary Table 5

Strain diversity of gut microbial species.

Supplementary Table 6

Extended B. dorei pangenome.

Supplementary Table 7

Tentative circular genomic elements in the sequenced B. dorei isolates.

Supplementary Table 8

CRISPR Spacer mapping to virome contigs and DIABIMMUNE assembly.

Supplementary Table 9

Most frequent taxa assigned to CRISPR spacer carrier contigs with matches to virome contigs of the DIABIMMUNE assembly.

Supplementary Table 10

Bacterial species by body site.

Supplementary Table 11

Contributional diversities of biological process GO terms.

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Vatanen, T., Plichta, D.R., Somani, J. et al. Genomic variation and strain-specific functional adaptation in the human gut microbiome during early life. Nat Microbiol 4, 470–479 (2019). https://doi.org/10.1038/s41564-018-0321-5

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