A Guide to Enterotypes across the Human Body: Meta-Analysis of Microbial Community Structures in Human Microbiome Datasets

By Omry Koren, Dan Knights, Antonio Gonzalez, Levi Waldron, Nicola Segata, Rob Knight, Curtis Huttenhower, Ruth E. Ley. PLoS Comput. Biol., 2013

499 citations recorded by the source corpus.

Recent analyses of human-associated bacterial diversity have categorized individuals into ‘enterotypes’ or clusters based on the abundances of key bacterial genera in the gut microbiota. There is a lack of consensus, however, on the analytical basis for enterotypes and on the interpretation of these results. We tested how the following factors influenced the detection of enterotypes: clustering methodology, distance metrics, OTU-picking approaches, sequencing depth, data type (whole genome shotgun (WGS) vs.16S rRNA gene sequence data), and 16S rRNA region. We included 16S rRNA gene sequences from the Human Microbiome Project (HMP) and from 16 additional studies and WGS sequences from the HMP and MetaHIT. In most body sites, we observed smooth abundance gradients of key genera without discrete clustering of samples. Some body habitats displayed bimodal (e.g., gut) or multimodal (e.g., vagina) distributions of sample abundances, but not all clustering methods and workflows accurately highlight such clusters. Because identifying enterotypes in datasets depends not only on the structure of the data but is also sensitive to the methods applied to identifying clustering strength, we recommend that multiple approaches be used and compared when testing for enterotypes.

Subfields: Molecular Biology, Physiology, Public Health, Environmental and Occupational Health

View Paper: https://www.semanticscholar.org/paper/0a1144d37d5478f15bfc7797363dd029941ed26b

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Citations in the corpus

Dietary Fiber-Induced Improvement in Glucose Metabolism Is Associated with Increased Abundance of Prevotella. (2015, background): “Subjects with high levels of Prevotella usually have lower levels of Bacteroides (Arumugam et al., 2011; Koren et al., 2013; Wu et al., 2011), which implies that taxa from these two genera compete for the same niche in the gut.”
Enterotypes in the landscape of gut microbial community composition (2017, unknown): “…(2012)19 Karlsson (2012)79 Zhu (2013)70 Mardanov (2013)87 Claesson (2012)30 Arumugam (2011)14 Holmes (2012)26 Quince (2013)27 Ding (2014)17 MacDonald (2012)90 Koren (2013)16 Eloe-Fadrosh (2013)93 Zhou (2014)18 Segata (2012)88 Zhang (2014)91 Roager (2014)28 Karlsson (2013)89 c Prevotella…”
The role of microbiome in central nervous system disorders (2013, unknown): “Yet, discrete enterotypes are still arguable as a later study revealed gradients of key bacterial genera (Koren et al., 2013).”
Microbial strain-level population structure and genetic diversity from metagenomes (2017, unknown): “(Koren et al. 2013) and has been strongly associated with the onset of rheumatoid arthritis (Scher et al. 2013; Zhang et al. 2015).”
How informative is the mouse for human gut microbiota research? (2015, background): “…founder effects A wide range of experiments in which gene-knockout mouse models have been created, yielding different genetic backgrounds, have highlighted that genetics is an important determinant of gut microbiota composition (Benson et al., 2010; Hildebrand et al., 2013; Kovacs et al., 2011).”
Microbiomes in light of traits: A phylogenetic perspective (2015, background): “, proteins, fats, and carbohydrates) in the human gut appear to be conserved at approximately the genus level, a resolution also associated with gut enterotypes (76).”
Sparse and Compositionally Robust Inference of Microbial Ecological Networks (2014, background): “Moreover, some microbial compositions appear to form distinct clusters, leading to the concept of enterotypes, or ecological steady states in the gut [6], but their existence has not been established with certainty [7].”
Waste Not, Want Not: Why Rarefying Microbiome Data Is Inadmissible (2013, background): “Instead, microbiome analysis workﬂows often begin with an ad hoc library size normalization by random subsampling without replacement, or so-called rarefy-ing [17–19].”
Conducting a Microbiome Study (2014, methodology): “Given the growing number of independent microbiome studies, there is great interest in being able to combine data frommultiple studies to increase power through meta-analysis (Koren et al., 2013).”