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Infant diet and maternal gestational weight gain predict early metabolic maturation of gut microbiomes

Abstract

Commensal gut bacterial communities (microbiomes) are predicted to influence human health and disease1,2. Neonatal gut microbiomes are colonized with maternal and environmental flora and mature toward a stable composition over 2–3 years3,4. To study pre- and postnatal determinants of infant microbiome development, we analyzed 402 fecal metagenomes from 60 infants aged 0–8 months, using longitudinal generalized linear mixed models (GLMMs). Distinct microbiome signatures correlated with breastfeeding, formula ingredients, and maternal gestational weight gain (GWG). Amino acid synthesis pathway accretion in breastfed microbiomes complemented normative breastmilk composition. Prebiotic oligosaccharides, designed to promote breastfed-like microflora5, predicted functional pathways distinct from breastfed infant microbiomes. Soy formula in six infants was positively associated with Lachnospiraceae and pathways suggesting a short-chain fatty acid (SCFA)-rich environment, including glycerol to 1-butanol fermentation, which is potentially dysbiotic. GWG correlated with altered carbohydrate degradation and enriched vitamin synthesis pathways. Maternal and postnatal antibiotics predicted microbiome alterations, while delivery route had no persistent effects. Domestic water source correlates suggest water may be an underappreciated determinant of microbiome acquisition. Clinically important microbial pathways with statistically significant dietary correlates included dysbiotic markers6,7, core enterotype features8, and synthesis pathways for enteroprotective9 and immunomodulatory10,11 metabolites, epigenetic mediators1, and developmentally critical vitamins12, warranting further investigation.

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Fig. 1: Taxonomic composition of infant fecal microbiota.
Fig. 2: Dynamic development of amino acid synthesis pathways.
Fig. 3: Taxonomic and functional changes associated with soy formula.
Fig. 4: Altered development of vitamin synthesis and carbohydrate utilization pathways in association with GWG.

Data availability

Sequence data supporting these findings have been deposited, along with relevant clinical metadata, in the SRA under BioProject ID PRJNA473126, with primary BioSample accession codes SAMN09259835SAMN09260236 (study SRP148966). Source data for Figs. 14 are available online. Any additional data generated and analyzed in this study are available from the corresponding author upon reasonable request.

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Acknowledgements

This work is supported in part by awards to G.D. through the Edward Mallinckrodt, Jr. Foundation (Scholar Award), and the National Institute of General Medical Sciences (http://www.nigms.nih.gov/) of the National Institutes of Health (NIH) under award number R01GM099538. A.M.B.-D. was supported by the National Institutes of Diabetes and Digestive and Kidney Diseases of the NIH under award number K08-DK102673. A.W.D. received support from the Institutional Program Unifying Population and Laboratory-Based Sciences Burroughs Wellcome Fund grant to Washington University. B.B.W. and P.I.T. received support for the cohort and sample collection from the Children’s Discovery Institute of Washington University and St. Louis Children’s Hospital, and P.I.T. is supported by P30DK052574 (Biobank Core). P.I.T., B.B.W., and G.D. are also supported in part by a grant from the Eunice Kennedy Shriver National Institute of Child Health & Human Development (https://www.nichd.nih.gov/) of the NIH under award number R01HD092414. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies. We would like to thank E. Martin, B. Koebbe, and J. Hoisington-López from the Edison Family Center for Genome Sciences & Systems Biology at Washington University School of Medicine for technical support in high-throughput computing and sequencing. We would like to thank A. J. Gasparrini, B. Wang, and B. Berla for technical assistance in experimental and computational protocol optimization for whole-metagenome shotgun sequencing of fecal samples. We would like to thank I. M. Ndao, N. Shaikh, S. Patel, B. Wang, and S. X. Sun for archival and maintenance of frozen fecal sample inventory. We would like to thank F. S. Cole and members of the Dantas lab for general helpful discussions regarding the research presented in this manuscript, and K. Guilonard for helpful comments on the text.

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A.M.B.-D., A.W.D., B.B.W., P.I.T., and G.D. conceived of experiments and design of work and analyses. B.B.W. and P.I.T. oversaw collection and stewardship of fecal samples and clinical metadata inventories. A.M.B.-D. performed wet-lab experiments with advice from G.D. A.M.B.-D. performed computational analyses with advice from A.W.D. and G.D. Article drafting was performed by A.M.B.-D. with critical revision performed by A.W.D., B.B.W., P.I.T., and G.D.

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Correspondence to Aimee M. Baumann-Dudenhoeffer or Gautam Dantas.

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Supplementary Text and Figures

Supplementary Figures 1–7 and Supplementary Tables 1 and 2

Reporting Summary

Supplementary Table 3

Maximum-likelihood longitudinal multivariate GLMM model information

Supplementary Table 4

Pathway-identified taxa

Supplementary Table 5

Qualitative summary of significant associations of clinical variables with taxa and pathways

Supplementary Table 6

Sample size for binary variables

Supplementary Table 7

Infant formula brands and ingredients

Supplementary Table 8

Taxa identified in zymobiomics community standard positive control samples

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Baumann-Dudenhoeffer, A.M., D’Souza, A.W., Tarr, P.I. et al. Infant diet and maternal gestational weight gain predict early metabolic maturation of gut microbiomes. Nat Med 24, 1822–1829 (2018). https://doi.org/10.1038/s41591-018-0216-2

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