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. 2018 Jan;3(1):8-16.
doi: 10.1038/s41564-017-0072-8. Epub 2017 Dec 18.

Enterotypes in the Landscape of Gut Microbial Community Composition

Free PMC article

Enterotypes in the Landscape of Gut Microbial Community Composition

Paul I Costea et al. Nat Microbiol. .
Free PMC article

Erratum in

  • Publisher Correction: Enterotypes in the landscape of gut microbial community composition.
    Costea PI, Hildebrand F, Arumugam M, Bäckhed F, Blaser MJ, Bushman FD, de Vos WM, Ehrlich SD, Fraser CM, Hattori M, Huttenhower C, Jeffery IB, Knights D, Lewis JD, Ley RE, Ochman H, O'Toole PW, Quince C, Relman DA, Shanahan F, Sunagawa S, Wang J, Weinstock GM, Wu GD, Zeller G, Zhao L, Raes J, Knight R, Bork P. Costea PI, et al. Nat Microbiol. 2018 Mar;3(3):388. doi: 10.1038/s41564-018-0114-x. Nat Microbiol. 2018. PMID: 29440750


Population stratification is a useful approach for a better understanding of complex biological problems in human health and wellbeing. The proposal that such stratification applies to the human gut microbiome, in the form of distinct community composition types termed enterotypes, has been met with both excitement and controversy. In view of accumulated data and re-analyses since the original work, we revisit the concept of enterotypes, discuss different methods of dividing up the landscape of possible microbiome configurations, and put these concepts into functional, ecological and medical contexts. As enterotypes are of use in describing the gut microbial community landscape and may become relevant in clinical practice, we aim to reconcile differing views and encourage a balanced application of the concept.


Figure 1
Figure 1. The microbiota of distinct body locations within the healthy human is separable at the genus level
Using 2381 HMP samples profiled with 16S rRNA, we illustrate the degree of separation between body-sites using different distance measures and taxonomic resolutions: (A) unweighted UniFrac on OTU level, (B) Jensen-Shannon divergence on genus level (OTUs belonging to the same genus are added up together) and (C) Jensen-Shannon divergence on OTU level. Shown are the first two principal coordinates of a PCoA analysis for each, as well as a summary of the within and between body-site distances in the top left. Median inter-sample distances (error bars ranging from the 25th to the 75th quantile) compared to the median between all body-sites (red line) illustrate the ability to capture similarities and differences between these biomes, albeit with different effectiveness. We note that the Silhouette Index (a measure of clustering strength) in the case of unweighted UniFrac suggests a clustering into only three types, with an absolute value of ∼0.2 (Suppl. Fig.4).
Figure 2
Figure 2. Stratification of the microbial composition landscape of the human gut microbiome
(A) Abundance distributions of prevalent microbial genera of the human gut are often complex. Theoretical beta distributions (left panels) were compared with observed distributions (middle panel) and the observed abundance plotted in enterotype space (right panel) of key enterotype tax a or ratios thereof, based on 278 MetaHIT samples. While Bacteroides abundance distribution is close to log-normal in the three large-scale datasets studied, that of Prevotella is bimodal, suggesting that the observed values are perhaps better explained by a mixture of two distributions, generated by two distinct processes, one of which corresponds to a dominating role in the community, while the other to a low abundance state. (B) Geographical distribution of studies that report enterotypes (Suppl. Table 1), colored according to the number of microbial clusters reported. Map locations indicate the country from which samples were collected. Links between locations represent samples belonging to a single study. Overrepresentation of “Western” countries is a well-known bias and probably misses a portion of variation in other human societies. (C) Schematic representation of the simulated microbial composition landscape with three density peaks, modeled as multivariate normal distributions, each representing an enterotype and drawn out of scale to make the concept more accessible. This figure illustrates how segmentation of this space by clustering with different parameters would result indifferent numbers of clusters (three and two here) and in differential coverage of individuals (represented by intersecting planes). Top-most overlay presents the discretizing segmentation, which splits the space into three zones. (D) Projection onto a set of 278 Danish samples of the three most frequent enterotype classification schemes based on different methods, including the Prevotella/Bacteroides gradient. This shows a split into a gradient and two, three (distance based clustering) or four enterotypes (Dirichlet multinomial mixture models). The local structure is preserved regardless of the method applied, and Prevotella (ET P) remains separated, suggesting the methods mostly differ in dividing the area between ET B and ET F. Additionally, the top right of each PCoA with a number of clusters greater than or equal to two shows the distance within a cluster (colored accordingly) compared to the median distance between the clusters (black line), showing that for all cases the distances within are smaller than between; bar height is the median distance and the whiskers represent the 25th and 75th quantile. It should be noted that a “horseshoe effect” can occur in ordinations, in particular if samples contain non-overlapping compositions, which is not the case in the datasets analyzed here.
Figure 3
Figure 3. Microbiota of human fecal samples has local substructure
Ordination of 278 MetaHIT samples on Jensen-Shannon distance transformed space. For orientation, a three-enterotype model is illustrated by color in A, B and D. (A) The log-transformed relative abundance of the most significantly differing genera. On the adjacent axis, the projected abundance changes between the respective community types are shown. Bimodal abundance profiles (dotted lines, dip test p-value < 0.05) as well as gradual abundance changes (solid lines) can be identified, supporting a gradient or cluster model, respectively. (B) Abundance changes of selected COG categories were projected onto the ordination, illustrating that functional composition differs between enterotypes. (C) mOTU level Shannon diversity index and gene richness (low gene count is considered for subjects with less than 480k genes according to ; all other subjects have high gene count) are significantly different between enterotypes (Suppl. Fig. 8), mostly following gradual changes over the whole enterotype space. (D) Summary of the diseases and dietary constituents that have been associated with Prevotella, Firmicutes or Bacteroides enrichedgut communities (Suppl. Table 5).Acronyms: CD: Crohn's disease, CRP: C-reactive protein, NASH: Nonalcoholic Steatohepatitis, ROS: reactive oxygen species.
Figure 4
Figure 4. Determination of Enterotype structure
Flow diagram of recommended steps for determining enterotype assignment based on microbial abundance data. Two main routes to obtain enterotype assignments are depicted: denovo identification (enterotype discovery) and enterotype assignment based on a reference dataset. The suitability of existing models imposed on the data to describe the composition landscape (1) can be assessed by either determining the existence of cluster structure, using one of the proposed clustering strength measures (Suppl. Fig. 4) or by using a DMM modeling framework. Other models might also be useful in capturing the structure in the data, although an exact implementation is not yet available. Determining whether samples are within the enterotype space (2) is based on similarity incomposition to adult human stool samples from the HMP and MetaHIT studies. This suitability check and a respective classifier are available at []. There are many explanations for the different compositional structures (3); for example, they may come from non-western individuals, or from infants. Technical issues such as DNA extraction, PCR primers, and/or bioinformatics preprocessing, may skew the analysis. The consistency of the separation (4) obtained from the classifier may be determined using a Silhouette index.

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