Garcia-Mazcorro Jose F, Kawas Jorge R, Licona Cassani Cuauhtemoc, Mertens-Talcott Susanne, Noratto Giuliana
Research and Development, MNA de Mexico, San Nicolas de los Garza, Nuevo Leon, Mexico.
Faculty of Agronomy, Universidad Autónoma de Nuevo León, General Escobedo, Nuevo Leon, Mexico.
PeerJ. 2020 Nov 17;8:e10372. doi: 10.7717/peerj.10372. eCollection 2020.
One of the main functions of diet is to nurture the gut microbiota and this relationship affects the health of the host. However, different analysis strategies can generate different views on the relative abundance of each microbial taxon, which can affect our conclusions about the significance of diet to gut health in lean and obese subjects. Here we explored the impact of using different analysis strategies to study the gut microbiota in a context of diet, health and obesity.
Over 15 million 16S rRNA gene sequences from published studies involving dietary interventions in obese laboratory rodents were analyzed. Three strategies were used to assign the 16S sequences to Operational Taxonomic Units (OTUs) based on the GreenGenes reference OTU sequence files clustered at 97% and 99% similarity.
Different strategies to select OTUs influenced the relative abundance of all bacterial taxa, but the magnitude of this phenomenon showed a strong study effect. Different taxa showed up to 20% difference in relative abundance within the same study, depending on the analysis strategy. Very few OTUs were shared among the samples. ANOSIM test on unweighted UniFrac distances showed that study, sequencing technique, animal model, and dietary treatment (in that order) were the most important factors explaining the differences in bacterial communities. Except for obesity status, the contribution of diet and other factors to explain the variability in bacterial communities was lower when using weighted UniFrac distances. Predicted functional profile and high-level phenotypes of the microbiota showed that each study was associated with unique features and patterns.
The results confirm previous findings showing a strong study effect on gut microbial composition and raise concerns about the impact of analysis strategies on the membership and composition of the gut microbiota. This study may be helpful to guide future research aiming to investigate the relationship between diet, health, and the gut microbiota.
饮食的主要功能之一是滋养肠道微生物群,这种关系会影响宿主的健康。然而,不同的分析策略可能会对每个微生物分类群的相对丰度产生不同的看法,这可能会影响我们对饮食对瘦人和肥胖者肠道健康重要性的结论。在此,我们探讨了在饮食、健康和肥胖背景下使用不同分析策略研究肠道微生物群的影响。
分析了来自已发表研究的超过1500万个16S rRNA基因序列,这些研究涉及对肥胖实验啮齿动物的饮食干预。基于在97%和99%相似性水平聚类的GreenGenes参考OTU序列文件,使用三种策略将16S序列分配到操作分类单元(OTU)。
选择OTU的不同策略影响了所有细菌分类群的相对丰度,但这种现象的程度显示出很强的研究效应。根据分析策略的不同,同一研究中不同分类群的相对丰度差异高达20%。样本间共享的OTU非常少。对未加权UniFrac距离进行的ANOSIM检验表明,研究、测序技术、动物模型和饮食处理(按此顺序)是解释细菌群落差异的最重要因素。使用加权UniFrac距离时,除肥胖状态外,饮食和其他因素对解释细菌群落变异性的贡献较低。微生物群的预测功能概况和高级表型表明,每项研究都与独特的特征和模式相关。
结果证实了先前的发现,即研究对肠道微生物组成有很强的影响,并引发了对分析策略对肠道微生物群成员和组成影响的担忧。本研究可能有助于指导未来旨在研究饮食、健康与肠道微生物群之间关系的研究。