Succession of microbial consortia in the developing infant gut microbiome

Proceedings of the National Academy of Sciences of the United States of America - Tập 108 Số supplement_1 - Trang 4578-4585 - 2011
Jeremy E. Koenig1, Aymé Spor2, Nicholas B. Scalfone2, Ashwana D. Fricker2, Jesse Stombaugh3, Rob Knight4,5, Largus T. Angenent6, Ruth E. Ley2
1Department of Microbiology, Cornell University, Ithaca, NY 14853, USA
2aDepartment of Microbiology, Cornell University, Ithaca, NY 14853;
3bDepartment of Chemistry and Biochemistry, University of Colorado, Boulder, CO 80309;
4Department of Chemistry and Biochemistry, University of Colorado, Boulder, CO 80309;
5Howard Hughes Medical Institute, University of Colorado, Boulder, CO 80309; and
6dDepartment of Biological and Environmental Engineering, Cornell University, Ithaca, NY 14850

Tóm tắt

The colonization process of the infant gut microbiome has been called chaotic, but this view could reflect insufficient documentation of the factors affecting the microbiome. We performed a 2.5-y case study of the assembly of the human infant gut microbiome, to relate life events to microbiome composition and function. Sixty fecal samples were collected from a healthy infant along with a diary of diet and health status. Analysis of >300,000 16S rRNA genes indicated that the phylogenetic diversity of the microbiome increased gradually over time and that changes in community composition conformed to a smooth temporal gradient. In contrast, major taxonomic groups showed abrupt shifts in abundance corresponding to changes in diet or health. Community assembly was nonrandom: we observed discrete steps of bacterial succession punctuated by life events. Furthermore, analysis of ≈500,000 DNA metagenomic reads from 12 fecal samples revealed that the earliest microbiome was enriched in genes facilitating lactate utilization, and that functional genes involved in plant polysaccharide metabolism were present before the introduction of solid food, priming the infant gut for an adult diet. However, ingestion of table foods caused a sustained increase in the abundance of Bacteroidetes, elevated fecal short chain fatty acid levels, enrichment of genes associated with carbohydrate utilization, vitamin biosynthesis, and xenobiotic degradation, and a more stable community composition, all of which are characteristic of the adult microbiome. This study revealed that seemingly chaotic shifts in the microbiome are associated with life events; however, additional experiments ought to be conducted to assess how different infants respond to similar life events.

Từ khóa


Tài liệu tham khảo

M Gueimonde, et al., Effect of maternal consumption of lactobacillus GG on transfer and establishment of fecal bifidobacterial microbiota in neonates. J Ped Gastroenterol Nutr 42, 166–170 (2006).

PA Vaishampayan, et al., Comparative metagenomics and population dynamics of the gut microbiota in mother and infant. Genome Biol Evol 2010, 53–66 (2010).

DA Sela, 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).

C Palmer, EM Bik, DB DiGiulio, DA Relman, PO Brown, Development of the human infant intestinal microbiota. PLoS Biol 5, e177, 10.1371/journal.pbio.0050177. (2007).

K Kurokawa, et al., Comparative metagenomics revealed commonly enriched gene sets in human gut microbiomes. DNA Res 14, 169–181 (2007).

L Dethlefsen, PB Eckburg, EM Bik, DA Relman, Assembly of the human intestinal microbiota. Trends Ecol Evol 21, 517–523 (2006).

DP Faith, Conservation evaluation and phylogenetic diversity. Biol Conserv 61, 1–10 (1992).

C Lozupone, R Knight, UniFrac: A new phylogenetic method for comparing microbial communities. Appl Environ Microbiol 71, 8228–8235 (2005).

L Stone, A Roberts, The checkerboard score and species distributions. Oecologia 85, 74–79 (1990).

JM Diamond, Assembly of species community. Ecology and Evolution of Communities, eds ML Cody, JM Diamond (Harvard Univ Press, Cambridge, MA), pp. 342–444 (1975).

SF Altschul, et al., Gapped BLAST and PSI-BLAST: A new generation of protein database search programs. Nucleic Acids Res 25, 3389–3402 (1997).

F Meyer, et al., The metagenomics RAST server—a public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC Bioinformatics 9, 386, 10.1186/1471-2105-9-386. (2008).

PJ Turnbaugh, et al., The human microbiome project. Nature 449, 804–810 (2007).

PJ Turnbaugh, et al., A core gut microbiome in obese and lean twins. Nature 457, 480–484 (2009).

RE Ley, et al., Obesity alters gut microbial ecology. Proc Natl Acad Sci USA 102, 11070–11075 (2005).

PJ Turnbaugh, et al., An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 444, 1027–1031 (2006).

L Wen, et al., Innate immunity and intestinal microbiota in the development of Type 1 diabetes. Nature 455, 1109–1113 (2008).

WS Garrett, et al., Communicable ulcerative colitis induced by T-bet deficiency in the innate immune system. Cell 131, 33–45 (2007).

M Vijay-Kumar, et al., Altered gut microbiota in toll-like receptor-5 deficient mice results in metabolic syndrome. Science 328, 228–231 (2010).

J Zaneveld, et al., Host-bacterial coevolution and the search for new drug targets. Curr Opin Chem Biol 12, 109–114 (2008).

W Jia, H Li, L Zhao, JK Nicholson, Gut microbiota: A potential new territory for drug targeting. Nat Rev Drug Discov 7, 123–129 (2008).

S Rautava, M Kalliomäki, E Isolauri, New therapeutic strategy for combating the increasing burden of allergic disease: Probiotics—a Nutrition, Allergy, Mucosal Immunology and Intestinal Microbiota (NAMI) Research Group report. J Allergy Clin Immunol 116, 31–37 (2005).

RE Ley, PJ Turnbaugh, S Klein, JI Gordon, Microbial ecology: Human gut microbes associated with obesity. Nature 444, 1022–1023 (2006).

L Dethlefsen, S Huse, ML Sogin, DA Relman, The pervasive effects of an antibiotic on the human gut microbiota, as revealed by deep 16S rRNA sequencing. PLoS Biol 6, e280, 10.1371/journal.pbio.0060280. (2008).

GR Gibson, GT Macfarlane, JH Cummings, Sulphate reducing bacteria and hydrogen metabolism in the human large intestine. Gut 34, 437–439 (1993).

SH Duncan, et al., Effects of alternative dietary substrates on competition between human colonic bacteria in an anaerobic fermentor system. Appl Environ Microbiol 69, 1136–1142 (2003).

HJ Flint, SH Duncan, KP Scott, P Louis, Interactions and competition within the microbial community of the human colon: Links between diet and health. Environ Microbiol 9, 1101–1111 (2007).

SH Duncan, et al., Reduced dietary intake of carbohydrates by obese subjects results in decreased concentrations of butyrate and butyrate-producing bacteria in feces. Appl Environ Microbiol 73, 1073–1078 (2007).

J Xu, et al., A genomic view of the human-Bacteroides thetaiotaomicron symbiosis. Science 299, 2074–2076 (2003).

PJ Turnbaugh, et al., The effect of diet on the human gut microbiome: A metagenomic analysis in humanized gnotobiotic mice. Science Transl Med 1, 16ra14 (2009).

EK Costello, et al., Bacterial community variation in human body habitats across space and time. Science 326, 1694–1697 (2009).

M Hamady, JJ Walker, JK Harris, NJ Gold, R Knight, Error-correcting barcoded primers for pyrosequencing hundreds of samples in multiplex. Nat Methods 5, 235–237 (2008).

W Li, A Godzik, Cd-hit: A fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658–1659 (2006).

Q Wang, GM Garrity, JM Tiedje, JR Cole, Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol 73, 5261–5267 (2007).

JG Caporaso, et al., PyNAST: A flexible tool for aligning sequences to a template alignment. Bioinformatics 26, 266–267 (2010).

M Hamady, C Lozupone, R Knight, Fast UniFrac: Facilitating high-throughput phylogenetic analyses of microbial communities including analysis of pyrosequencing and PhyloChip data. ISME J 4, 17–27 (2010).

AJ Saldanha, Java Treeview—extensible visualization of microarray data. Bioinformatics 20, 3246–3248 (2004).

V Gomez-Alvarez, TK Teal, TM Schmidt, Systematic artifacts in metagenomes from complex microbial communities. ISME J 3, 1314–1317 (2009).

DH Huson, AF Auch, J Qi, SC Schuster, MEGAN analysis of metagenomic data. Genome Res 17, 377–386 (2007).

I Gonzlez, S Déjean, PGP Martin, A Baccini, CCA: An R package to extend canonical correlation analysis. J Stat Softw 23, 1–14 (2008).

S Leurgans, R Moyeed, B Silverman, Canonical correlation analysis when the data are curves. J R Stat Soc Ser B 55, 725–740 (1993).

Thông tin
Thông tin xuất bản

Proceedings of the National Academy of Sciences of the United States of America

Tập 108 Số supplement_1

4578-4585

Thông tin tác giả