按照时间顺序，对肠型有关文章进行简单梳理

第一篇：Enterotypes of the human gut microbiome

杂志：nature

时间：2011

   此文是第一提出肠型（enterotypes）此概念的文章，文中对于肠型的主要内容在于：1）在不同的肠道样本中，均可以明显的类别存在；2）3类肠型中占主要的菌，在不同样本中是不同的。

   Multidimensional cluster analysis and Principal Component Analysis (PCA) revealed that the remaining 33 samples formed three distinct clusters which we designate enterotypes. Each of these three enterotypes are identifiable by the variation in the levels of one of three genera: Bacteroides (enterotype 1), Prevotella (enterotype 2) and Ruminococcus (enterotype 3), which was reproduced using independent array-based HITChip data in a subset of 22 European samples. The same analysis on two larger published gut microbiome datasets of different origins （16S pyrosequencing data from 154 American individuals and Illumina-based metagenomics data from 85 Danish individuals）, showes that these datasets could also be represented best by three clusters.

Two of these are also driven by Bacteroides and Prevotella, while the third cluster is mostly driven by related groups of the order Clostridiales, Blautia and unclassified Lachnospiraceae in the 16S rDNA and Illumina data, respectively.

结果具体如下图

第二篇：A Guide to Enterotypes across the Human Body: Meta-Analysis of Microbial Community Structures in Human Microbiome Datasets

杂志：PLOS Computational Biology

时间：2013

   研究目的：不同因素对肠型分析的影响，Because there is currently no community standard for testing for enterotypes, we explore how the detection of enterotypes is affected by the following: clustering methodology, distance metrics, OTU-picking approaches, sequencing depth (i.e., rarefaction), data type (16S rRNA vs. WGS), and the specific region of the 16S rRNA gene sequenced.

  研究结果：

   1）不同聚类方法、距离计算方式最终得到的肠型会有不同，This analysis revealed strong support for 4 clusters using PS for the BC, JSD and rJSD distance metrics, but SI provided no support for clustering using BC and rJSD, and only weak support for 3–5 clusters using the JSD distance metric. The CH index supported 4 clusters using only the JSD distance metric. we observed at best moderate support for 2 fecal enterotypes in the HMP data using weighted UniFrac, but little or no support using other distance metrics.

   2）不同OTU的层级得到的肠型不同，We used the abundances of both species and genus-level OTUs from the Ravel et al. dataset to test for enterotypes. Our analysis shows strong support for two genus-level enterotypes using 4 of 5 distance metrics (i.e., unweighted UniFrac had moderate support) for the Ravel et al. dataset when using the PS to test the strength of the clustering. We also observed strong support for genus-level mid-vaginal enterotypes using 3 of 5 distance metrics (BC, JSD and rJSD) for the HMP dataset. Additionally, using a species-level analysis, we obtained moderate support for five enterotypes using BC and JSD in the Ravel et al. data (we also scored strong support for 2 enterotypes with weighted UniFrac), and moderate to strong support for 2 clusters (i.e., little or no support for five clusters) in the HMP data.

   3）不同测序区域得到的肠型不同，Data from the V3–V5 region yielded moderate support for two fecal enterotypes, but no enterotypes were detected using data from the V1–V3 region.

   4）OTU的计算策略得到的肠型不同，the reference-based OTU picking approach may result in over-confidence in enterotype discovery.

   5）测序深度对肠型分析影响不大，Rarefaction depth did not seem to strongly affect the results of the clustering.

   6）测序策略得到的肠型不同，with the exception of the moderate support for two enterotypes in the buccal mucosa WGS data, and lack of consistent support for enterotypes in the posterior fornix.