Natural history of the infant gut microbiome and impact of antibiotic treatment on bacterial strain diversity and stability

doi:10.1126/scitranslmed.aad0917

. 2016 Jun 15;8(343):343ra81.

doi: 10.1126/scitranslmed.aad0917.

Natural history of the infant gut microbiome and impact of antibiotic treatment on bacterial strain diversity and stability

Affiliations

¹ Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard University, Cambridge, MA 02142, USA. Center for Computational and Integrative Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.
² Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard University, Cambridge, MA 02142, USA. Department of Computer Science, Aalto University School of Science, 02150 Espoo, Finland.
³ Children's Hospital, University of Helsinki and Helsinki University Hospital, 00290 Helsinki, Finland. Research Programs Unit, Diabetes and Obesity, University of Helsinki, 00290 Helsinki, Finland. Department of Pediatrics, Tampere University Hospital, 33521 Tampere, Finland.
⁴ Department of Pediatrics, Jorvi Hospital, Helsinki University Hospital, 02740 Espoo, Finland.
⁵ Children's Hospital, University of Helsinki and Helsinki University Hospital, 00290 Helsinki, Finland. Research Programs Unit, Diabetes and Obesity, University of Helsinki, 00290 Helsinki, Finland.
⁶ Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA.
⁷ Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard University, Cambridge, MA 02142, USA.
⁸ Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard University, Cambridge, MA 02142, USA. Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA.
⁹ Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard University, Cambridge, MA 02142, USA. Department of Biology, MIT, Cambridge, MA 02139, USA. Department of Systems Biology, Harvard Medical School, Boston, MA 02114, USA.
¹⁰ Children's Hospital, University of Helsinki and Helsinki University Hospital, 00290 Helsinki, Finland. Research Programs Unit, Diabetes and Obesity, University of Helsinki, 00290 Helsinki, Finland. Department of Pediatrics, Tampere University Hospital, 33521 Tampere, Finland. Folkhälsan Research Center, 00290 Helsinki, Finland.
¹¹ Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard University, Cambridge, MA 02142, USA. Center for Computational and Integrative Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA. Gastrointestinal Unit and Center for the Study of Inflammatory Bowel Disease, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA. Center for Microbiome Informatics and Therapeutics, MIT, Cambridge, MA 02139, USA. xavier@molbio.mgh.harvard.edu.

PMID: 27306663
PMCID: PMC5032909
DOI: 10.1126/scitranslmed.aad0917

Natural history of the infant gut microbiome and impact of antibiotic treatment on bacterial strain diversity and stability

Moran Yassour et al. Sci Transl Med. 2016.

. 2016 Jun 15;8(343):343ra81.

doi: 10.1126/scitranslmed.aad0917.

Authors

Affiliations

¹ Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard University, Cambridge, MA 02142, USA. Center for Computational and Integrative Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.
² Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard University, Cambridge, MA 02142, USA. Department of Computer Science, Aalto University School of Science, 02150 Espoo, Finland.
³ Children's Hospital, University of Helsinki and Helsinki University Hospital, 00290 Helsinki, Finland. Research Programs Unit, Diabetes and Obesity, University of Helsinki, 00290 Helsinki, Finland. Department of Pediatrics, Tampere University Hospital, 33521 Tampere, Finland.
⁴ Department of Pediatrics, Jorvi Hospital, Helsinki University Hospital, 02740 Espoo, Finland.
⁵ Children's Hospital, University of Helsinki and Helsinki University Hospital, 00290 Helsinki, Finland. Research Programs Unit, Diabetes and Obesity, University of Helsinki, 00290 Helsinki, Finland.
⁶ Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA.
⁷ Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard University, Cambridge, MA 02142, USA.
⁸ Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard University, Cambridge, MA 02142, USA. Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA.
⁹ Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard University, Cambridge, MA 02142, USA. Department of Biology, MIT, Cambridge, MA 02139, USA. Department of Systems Biology, Harvard Medical School, Boston, MA 02114, USA.
¹⁰ Children's Hospital, University of Helsinki and Helsinki University Hospital, 00290 Helsinki, Finland. Research Programs Unit, Diabetes and Obesity, University of Helsinki, 00290 Helsinki, Finland. Department of Pediatrics, Tampere University Hospital, 33521 Tampere, Finland. Folkhälsan Research Center, 00290 Helsinki, Finland.
¹¹ Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard University, Cambridge, MA 02142, USA. Center for Computational and Integrative Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA. Gastrointestinal Unit and Center for the Study of Inflammatory Bowel Disease, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA. Center for Microbiome Informatics and Therapeutics, MIT, Cambridge, MA 02139, USA. xavier@molbio.mgh.harvard.edu.

PMID: 27306663
PMCID: PMC5032909
DOI: 10.1126/scitranslmed.aad0917

Abstract

The gut microbial community is dynamic during the first 3 years of life, before stabilizing to an adult-like state. However, little is known about the impact of environmental factors on the developing human gut microbiome. We report a longitudinal study of the gut microbiome based on DNA sequence analysis of monthly stool samples and clinical information from 39 children, about half of whom received multiple courses of antibiotics during the first 3 years of life. Whereas the gut microbiome of most children born by vaginal delivery was dominated by Bacteroides species, the four children born by cesarean section and about 20% of vaginally born children lacked Bacteroides in the first 6 to 18 months of life. Longitudinal sampling, coupled with whole-genome shotgun sequencing, allowed detection of strain-level variation as well as the abundance of antibiotic resistance genes. The microbiota of antibiotic-treated children was less diverse in terms of both bacterial species and strains, with some species often dominated by single strains. In addition, we observed short-term composition changes between consecutive samples from children treated with antibiotics. Antibiotic resistance genes carried on microbial chromosomes showed a peak in abundance after antibiotic treatment followed by a sharp decline, whereas some genes carried on mobile elements persisted longer after antibiotic therapy ended. Our results highlight the value of high-density longitudinal sampling studies with high-resolution strain profiling for studying the establishment and response to perturbation of the infant gut microbiome.

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Figures

**Fig. 1**
Study design and common features of infant gut microbiota. (A) Study design showing 1,069 samples (gray circles and triangles) collected from 39 children (y-axis) over 36 months (x-axis), together with information regarding the time and type of antibiotic treatments (colored circles). 16S rRNA gene sequencing was performed for all samples (gray circles and triangles) and 240 samples were selected for metagenomic sequencing (triangles). (B) Common features of the developing infant gut microbiota. Graph shows relative abundance (y-axis) of the most common bacterial families over time (x-axis), averaged across all 39 children. Shaded regions indicate 95% confidence intervals. See figure S1 for genus-level abundance plots. (C to E) Stream plots (57) of individual microbial trajectories of three children over time (x-axis), where each genus is color-coded by its phylum. Shown are typical trajectories for a vaginally born child (C), where species of the *Bacteroides* genus are present from an early age; a child born by Cesarean section (D), in which members of the *Bacteroides* genus are undetectable in the first 10 months; and an atypical trajectory for a vaginally born child (E), which appears similar to the Cesarean section signature.

**Fig. 2**
Diversity and strain similarities of the infant gut microbiota. (A) Diversity index of the strain distribution within each species shown for all metagenomic samples. Samples are colored according to three groups: children who received antibiotics (red), children with low *Bacteroides* (blue), and children who received no antibiotics (green). (**B-C**) Strain distributions of two selected samples (y-axis is relative abundance of each strain), and their calculated diversity indices. The selected samples were E032966, month 24 (B) and E006091, month 23 (C). (D) Partial phylogenetic trees based on the mutation distance between all strains of *B. fragilis* (left) and *B. vulgatus* (right). Strains are colored by the child in whom they were detected, and colored nodes represent the most recent common ancestor of the strains found in that child (scale bars of 1,000 mutations are shown per tree; Full trees appear in fig. S8). (E) Distributions of the mutation distance for all pairwise comparisons of *B. fragilis* (left) and *B. vulgatus* (right) strains, within (blue) or across (gray) individuals. (F) Distributions of mutation distances within individuals, colored as Antibiotics⁻ (green), or Antibiotics⁺ (red), with P values for the separation of these two distributions (Kolmogorov-Smirnov (KS)-test) for *B. fragilis* (left) and *B. vulgatus* (right) strains.

**Fig. 3**
Stability of the infant gut microbiota. (A to C) Individual plots depicting the stability of the gut microbiome over time for three children (see figure S9 for plots for all children). All sample pairs from the same child were compared using the Jaccard index and plotted as a function of their age. Child identifiers are colored as Abx⁻ (green, A), low *Bacteroides* (blue, B), or Abx⁺ (red, C). (D) Median of stability was calculated for all consecutive samples and plotted for Abx⁺ and Abx⁻ children. Box boundaries are the 25th and 75th percentiles, and the median is highlighted. P = 3.3 × 10⁻⁵, KS-test. Inset shows the estimated variance of measurements for each group (mean + standard error). The median values for subjects presented in panels (A) and (C) are shown as green and red stars, respectively.

**Fig. 4**
Antibiotic resistance gene profiles. (A) Abundance of antibiotic resistance (AR) gene products (mostly chromosomally encoded, rpkm values) in three children over time, together with the timing of individual antibiotic courses (colored dots; see figure S10 for plots for all children). (B) Relative abundance of species most correlating with the resistance gene profiles of panel (A). (C) Examples as in (A) of AR genes found on mobile elements and that are present in the gut for longer time periods. In (A) and (C), the number and order of antibiotic courses are shown with each antibiotic class indicated by color.

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Comment in

Gut microbiota: First steps in the infant gut microbiota.
Ray K. Ray K. Nat Rev Gastroenterol Hepatol. 2016 Aug;13(8):437. doi: 10.1038/nrgastro.2016.108. Epub 2016 Jul 6. Nat Rev Gastroenterol Hepatol. 2016. PMID: 27381073 No abstract available.

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References

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[1] Human Microbiome Project Consortium, Structure, function and diversity of the healthy human microbiome. Nature. 2012;486:207–214. - PMC - PubMed

[2] Human Microbiome Project Consortium, Structure, function and diversity of the healthy human microbiome. Nature. 2012;486:207–214. - PMC - PubMed

[3] Morgan XC, Tickle TL, Sokol H, Gevers D, Devaney KL, Ward DV, Reyes JA, Shah SA, LeLeiko N, Snapper SB, Bousvaros A, Korzenik J, Sands BE, Xavier RJ, Huttenhower C. Dysfunction of the intestinal microbiome in inflammatory bowel disease and treatment. Genome Biol. 2012;13:R79. - PMC - PubMed

[4] Morgan XC, Tickle TL, Sokol H, Gevers D, Devaney KL, Ward DV, Reyes JA, Shah SA, LeLeiko N, Snapper SB, Bousvaros A, Korzenik J, Sands BE, Xavier RJ, Huttenhower C. Dysfunction of the intestinal microbiome in inflammatory bowel disease and treatment. Genome Biol. 2012;13:R79. - PMC - PubMed

[5] Gevers D, Kugathasan S, Denson LA, Vazquez-Baeza Y, Van Treuren W, Ren B, Schwager E, Knights D, Song SJ, Yassour M, Morgan XC, Kostic AD, Luo C, Gonzalez A, McDonald D, Haberman Y, Walters T, Baker S, Rosh J, Stephens M, Heyman M, Markowitz J, Baldassano R, Griffiths A, Sylvester F, Mack D, Kim S, Crandall W, Hyams J, Huttenhower C, Knight R, Xavier RJ. The treatment-naive microbiome in new-onset Crohn's disease. Cell Host Microbe. 2014;15:382–392. - PMC - PubMed

[6] Gevers D, Kugathasan S, Denson LA, Vazquez-Baeza Y, Van Treuren W, Ren B, Schwager E, Knights D, Song SJ, Yassour M, Morgan XC, Kostic AD, Luo C, Gonzalez A, McDonald D, Haberman Y, Walters T, Baker S, Rosh J, Stephens M, Heyman M, Markowitz J, Baldassano R, Griffiths A, Sylvester F, Mack D, Kim S, Crandall W, Hyams J, Huttenhower C, Knight R, Xavier RJ. The treatment-naive microbiome in new-onset Crohn's disease. Cell Host Microbe. 2014;15:382–392. - PMC - PubMed

[7] Qin J, Li Y, Cai Z, Li S, Zhu J, Zhang F, Liang S, Zhang W, Guan Y, Shen D, Peng Y, Zhang D, Jie Z, Wu W, Qin Y, Xue W, Li J, Han L, Lu D, Wu P, Dai Y, Sun X, Li Z, Tang A, Zhong S, Li X, Chen W, Xu R, Wang M, Feng Q, Gong M, Yu J, Zhang Y, Zhang M, Hansen T, Sanchez G, Raes J, Falony G, Okuda S, Almeida M, LeChatelier E, Renault P, Pons N, Batto JM, Zhang Z, Chen H, Yang R, Zheng W, Li S, Yang H, Wang J, Ehrlich SD, Nielsen R, Pedersen O, Kristiansen K, Wang J. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature. 2012;490:55–60. - PubMed

[8] Qin J, Li Y, Cai Z, Li S, Zhu J, Zhang F, Liang S, Zhang W, Guan Y, Shen D, Peng Y, Zhang D, Jie Z, Wu W, Qin Y, Xue W, Li J, Han L, Lu D, Wu P, Dai Y, Sun X, Li Z, Tang A, Zhong S, Li X, Chen W, Xu R, Wang M, Feng Q, Gong M, Yu J, Zhang Y, Zhang M, Hansen T, Sanchez G, Raes J, Falony G, Okuda S, Almeida M, LeChatelier E, Renault P, Pons N, Batto JM, Zhang Z, Chen H, Yang R, Zheng W, Li S, Yang H, Wang J, Ehrlich SD, Nielsen R, Pedersen O, Kristiansen K, Wang J. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature. 2012;490:55–60. - PubMed

[9] Finucane MM, Sharpton TJ, Laurent TJ, Pollard KS. A taxonomic signature of obesity in the microbiome? Getting to the guts of the matter. PLoS ONE. 2014;9:e84689. - PMC - PubMed

[10] Finucane MM, Sharpton TJ, Laurent TJ, Pollard KS. A taxonomic signature of obesity in the microbiome? Getting to the guts of the matter. PLoS ONE. 2014;9:e84689. - PMC - PubMed

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Natural history of the infant gut microbiome and impact of antibiotic treatment on bacterial strain diversity and stability

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Natural history of the infant gut microbiome and impact of antibiotic treatment on bacterial strain diversity and stability

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