肠道微生物功能宏基因组学与代谢组学关联分析方法研究进展

近年来基于高通量基因测序的微生物组学研究极大加深了人们对微生物与健康和疾病关系的认识。然而基因测序方法不能直接测定微生物的功能活性,难以鉴定微生物中的关键功能分子,单独使用无法回答肠道微生物何种成员通过何种方式影响宿主等关键科学问题。单一组学研究弊端尽显,多组学联用势在必行。肠道微生物代谢组学以微生物群落所有小分子代谢物为研究对象,可发现肠道微生物随宿主病理生理变化的关键代谢物,为微生物组-宿主互作机制研究提供线索,成为微生物组学研究的重要补充。肠道微生物功能基因组学与代谢组学关联分析在宿主生理、疾病病理、药物药理等方面取得众多进展,展现良好应用前景。然而目前肠道微生物功能基因组学与代谢组学关联分析存在方法滥用、相关性结论与生物学知识相悖等突出问题。为帮助正确应用肠道微生物功能宏基因组学与代谢组学关联分析,本文综述了各种多组学数据整合分析方法的原理、优缺点与适用范围,并给出了应用建议。

Advances in research on association analysis methods between gut functional metagenomics and metabolomics

In recent years, microbiome study based on high-throughput gene sequencing has greatly deepened the understanding of the relationship between gut microbiota and health. However, gene sequencing method cannot directly determine the functional activity of microorganisms. It is difficult to identify the key functional molecules in microorganisms. Application of this single technology cannot answer some key scientific questions which members of gut microbiota and how they affect the host. The disadvantages of singles-omics research are obvious, and it is imperative to perform more multi-omics studies. Metabolomics of the gut microbiota takes all small molecular metabolites of microbial community as the research object. It can discover the key metabolites of intestinal microorganisms with the pathophysiological changes of host. It provides clues for the study of microbial-host interaction mechanism and becomes an important complement to gut functional metagenomics. Integration study of gut functional metagenomics and metabolomics have made a lot of progress in host physiology, disease pathology and other aspects, showing a promising future. However, there are some crucial problems in multi-omics study of gut functional metagenomics and metabolomics, such as misapplication of various methods, resulting in inconsistency between relevant conclusions and biological knowledge. In order to help the proper application of multi-omics study on gut functional metagenomics and metabolomics, this paper reviewed the principles, advantages, disadvantages and application scope of various multi-omics data integration analysis methods, and gave some application suggestions.