Department of Cell Physiology and Metabolism, Faculty of Medicine, University of Geneva, Centre Medical Universitaire, Geneva, Switzerland.
Diabetes Center, Faculty of Medicine, University of Geneva, Geneva, Switzerland.
PLoS Comput Biol. 2022 Mar 8;18(3):e1009947. doi: 10.1371/journal.pcbi.1009947. eCollection 2022 Mar.
Mouse is the most used model for studying the impact of microbiota on its host, but the repertoire of species from the mouse gut microbiome remains largely unknown. Accordingly, the similarity between human and mouse microbiomes at a low taxonomic level is not clear. We construct a comprehensive mouse microbiota genome (CMMG) catalog by assembling all currently available mouse gut metagenomes and combining them with published reference and metagenome-assembled genomes. The 41'798 genomes cluster into 1'573 species, of which 78.1% are uncultured, and we discovered 226 new genera, seven new families, and one new order. CMMG enables an unprecedented coverage of the mouse gut microbiome exceeding 86%, increases the mapping rate over four-fold, and allows functional microbiota analyses of human and mouse linking them to the driver species. Comparing CMMG to microbiota from the unified human gastrointestinal genomes shows an overlap of 62% at the genus but only 10% at the species level, demonstrating that human and mouse gut microbiota are largely distinct. CMMG contains the most comprehensive collection of consistently functionally annotated species of the mouse and human microbiome to date, setting the ground for analysis of new and reanalysis of existing datasets at an unprecedented depth.
小鼠是研究微生物组对其宿主影响的最常用模型,但小鼠肠道微生物组的物种组成在很大程度上仍不清楚。因此,在低分类水平上,人类和小鼠微生物组之间的相似性尚不清楚。我们通过组装所有现有的小鼠肠道宏基因组,并将其与已发表的参考和宏基因组组装基因组相结合,构建了一个全面的小鼠微生物组基因组(CMMG)目录。这 41798 个基因组聚类为 1573 个物种,其中 78.1%是未培养的,我们发现了 226 个新属、7 个新科和 1 个新目。CMMG 实现了对小鼠肠道微生物组的前所未有的覆盖,超过 86%,将映射率提高了四倍以上,并允许对人类和小鼠的功能微生物组进行分析,将它们与驱动物种联系起来。将 CMMG 与统一的人类胃肠道基因组中的微生物组进行比较,发现属之间的重叠率为 62%,而种之间的重叠率仅为 10%,这表明人类和小鼠肠道微生物组在很大程度上是不同的。CMMG 包含了迄今为止最全面的、一致功能注释的小鼠和人类微生物组物种集合,为在前所未有的深度上对新数据集进行分析和重新分析奠定了基础。