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. 2007 Oct 18;449(7164):804-10.
doi: 10.1038/nature06244.

The human microbiome project

Peter J Turnbaugh  1 , Ruth E Ley, Micah Hamady, Claire M Fraser-Liggett, Rob Knight, Jeffrey I Gordon
Affiliations

The human microbiome project

Peter J Turnbaugh et al. Nature. .

Abstract

A strategy to understand the microbial components of the human genetic and metabolic landscape and how they contribute to normal physiology and predisposition to disease.

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Figures

Figure 1
Figure 1. The concept of a core human microbiome
The core (orange) is viewed as a set of shared genes found in a given habitat (e.g. gut, mouth, skin) in all humans. The core is surrounded by a set of variably represented genes (blue): this variation could be influenced by a combination of factors (arrows) including transient populations of microbes that are not able to persistently colonize (allochthonous organisms), lifestyle (including diet), various environmental exposures (place of residency or work), host genotype, host physiologic status including the properties of the innate and adaptive immune system, and disease. The hazy line surrounding the core indicates the possibility that over the course of human ‘micro-evolution’ new genes may be added to the core microbiome while others may be lost.
Figure 2
Figure 2. Functional comparisons of the gut microbiome versus other sequenced microbiomes,–
(A) Relative abundance of predicted genes assigned to KEGG categories for metabolism. Analyses were performed on the combined mouse gut dataset (n=5 animals), both human gut datasets, and three ‘environmental’ datasets: the combined Whale fall dataset (n=3 samples), agricultural soil, and the combined Sargasso Sea dataset (n=7 samples). Forward sequencing reads were culled from each dataset and mapped onto reference microbial and eukaryotic genomes from the KEGG database (version 40; BLASTX best-blast-hit e-value<10−5). Asterisks indicate categories that are significantly enriched or depleted in the combined gut dataset versus the combined environmental dataset (the distribution of ~15,000 KEGG pathway assignments across each of the six datasets was used to construct two combined datasets of ~45,000 KEGG pathway assignments each; Χ2 test using the Bonferroni correction for multiple hypotheses, p<10−4). (B) Hierarchical clustering based on the relative abundance of predicted proteins assigned to KEGG pathways reveals specific differences between gut (green) and environmental (red) microbiomes. The relative abundance of pathways that exceeded a threshold of >0.6% (assignments to a given pathway divided by assignments to all pathways) in at least two environments was transformed into a z-score (yellow=enrichment; blue=depletion), and clustered by environments and pathways using a Euclidean distance metric (Cluster 3.0). The results were visualized in Treeview. Environmental clustering was consistent using multiple distance metrics, including Pearson Correlation (centered/uncentered), Spearman Rank Correlation, Kendall’s tau, and City-block distance. The twelve most discriminating KEGG pathways are listed (based on the ratio of average gut relative abudance versus average environmental relative abundance). Metabolic pathway names are colored based on KEGG category [pathways not colored include sporulation (cell growth/death) and phosphotranferase system (membrane transport)]. The gut microbiome is enriched for pathways involved in importing and degrading polysaccharides and simple sugars (‘starch/sucrose metabolism’, ‘galactose metabolism’, ‘N-glycan degradation’, and ‘phosphotransferase system’). The gut microbiome is also enriched for genes involved in ‘sporulation’, reflecting the high relative abundance of Gram-positive Firmicutes.
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