Ma Zhanshan Sam
Computational Biology and Medical Ecology Lab, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China.
Center for Excellence in Animal Genetics and Evolution, Chinese Academy of Sciences, Kunming, China.
Front Microbiol. 2022 Jul 19;13:914429. doi: 10.3389/fmicb.2022.914429. eCollection 2022.
Diversity analysis is a standard procedure for most existing microbiome studies. Nevertheless, diversity metrics can be insensitive to changes in community composition (identities). For example, if species A (., a beneficial microbe) is replaced by equal number of species B (., an opportunistic pathogen), the diversity metric may not change, but the community composition has changed. The shared species analysis (SSA) is a computational technique that can discern changes of community composition by detecting the increase/decrease of shared species between two sets of microbiome samples, and it should be more sensitive than standard diversity analysis in discerning changes in microbiome structures. Here, we investigated the effects of ethnicity and lifestyles in China on the structure of Chinese gut microbiomes by reanalyzing the datasets of a large Chinese cohort with 300+ individuals covering 7 biggest Chinese ethnic groups (>95% Chinese population). We found: () Regarding lifestyles, SSA revealed significant differences between 100% of pair-wise comparisons in community compositions across all but phylum taxon levels (phylum level = 29%), but diversity analysis only revealed 14-29% pair-wise differences in community diversity across all four taxon levels. () Regarding ethnicities, SSA revealed 100% pair-wise differences in community compositions across all but phylum (phylum level = 48-62%) levels, but diversity analysis only revealed 5-57% differences in community diversity across all four taxon levels. () Ethnicity seems to have more prevalent effects on community structures than lifestyle does () Community structures of the gut microbiomes are more stable at the phylum level than at the other three levels. () SSA is more powerful than diversity analysis in detecting the changes of community structures; furthermore, SSA can produce lists of unique and shared OTUs. () Finally, we performed analysis to mechanistically interpret the observed differences revealed by the SSA and diversity analyses.
多样性分析是大多数现有微生物组研究的标准程序。然而，多样性指标可能对群落组成（种类）的变化不敏感。例如，如果物种A（如有益微生物）被相同数量的物种B（如机会致病菌）取代，多样性指标可能不会改变，但群落组成已经发生了变化。共享物种分析（SSA）是一种计算技术，它可以通过检测两组微生物组样本之间共享物种的增加/减少来识别群落组成的变化，并且在识别微生物组结构变化方面应该比标准多样性分析更敏感。在这里，我们通过重新分析一个涵盖7个中国最大民族（>95%中国人口）的300多名个体的大型中国队列的数据集，研究了中国的种族和生活方式对中国肠道微生物组结构的影响。我们发现：（1）关于生活方式，SSA显示，除门分类水平外（门水平=29%），所有分类水平上群落组成的成对比较中有100%存在显著差异，但多样性分析仅显示在所有四个分类水平上群落多样性的成对差异为14 - 29%。（2）关于种族，SSA显示，除门（门水平=48 - 62%）水平外，所有分类水平上群落组成的成对差异为100%，但多样性分析仅显示在所有四个分类水平上群落多样性的差异为5 - 57%。（3）种族似乎比生活方式对群落结构的影响更普遍。（4）肠道微生物组的群落结构在门水平比在其他三个水平更稳定。（5）SSA在检测群落结构变化方面比多样性分析更强大；此外，SSA可以生成独特和共享的OTU列表。（6）最后，我们进行了分析，从机制上解释了SSA和多样性分析所揭示的观察到的差异。
