Wang Lidan, Liang Xiao, Chen Hao, Cao Lijie, Liu Lan, Zhu Feng, Ding Yubin, Tang Jing, Xie Youlong
School of Basic Medicine, Chongqing Medical University, Chongqing 400016, China.
Department of Obstetrics and Gynecology, Women and Children's Hospital of Chongqing Medical University, Chongqing 401147, China.
Comput Struct Biotechnol J. 2023 Mar 25;21:2502-2513. doi: 10.1016/j.csbj.2023.03.044. eCollection 2023.
Microbial communities influence host phenotypes through microbiota-derived metabolites and interactions between exogenous active substances (EASs) and the microbiota. Owing to the high dynamics of microbial community composition and difficulty in microbial functional analysis, the identification of mechanistic links between individual microbes and host phenotypes is complex. Thus, it is important to characterize variations in microbial composition across various conditions (for example, topographical locations, times, physiological and pathological conditions, and populations of different ethnicities) in microbiome studies. However, no web server is currently available to facilitate such characterization. Moreover, accurately annotating the functions of microbes and investigating the possible factors that shape microbial function are critical for discovering links between microbes and host phenotypes. Herein, an online tool, CDEMI, is introduced to discover microbial composition variations across different conditions, and five types of microbe libraries are provided to comprehensively characterize the functionality of microbes from different perspectives. These collective microbe libraries include (1) microbial functional pathways, (2) disease associations with microbes, (3) EASs associations with microbes, (4) bioactive microbial metabolites, and (5) human body habitats. In summary, CDEMI is unique in that it can reveal microbial patterns in distributions/compositions across different conditions and facilitate biological interpretations based on diverse microbe libraries. CDEMI is accessible at http://rdblab.cn/cdemi/.
微生物群落通过微生物衍生的代谢产物以及外源性活性物质(EASs)与微生物群之间的相互作用来影响宿主表型。由于微生物群落组成的高度动态性以及微生物功能分析的困难,确定单个微生物与宿主表型之间的机制联系很复杂。因此,在微生物组研究中,表征不同条件下(例如地理位置、时间、生理和病理状况以及不同种族人群)微生物组成的变化很重要。然而,目前没有可用的网络服务器来促进这种表征。此外,准确注释微生物的功能并研究影响微生物功能的可能因素对于发现微生物与宿主表型之间的联系至关重要。在此,介绍一种在线工具CDEMI,用于发现不同条件下的微生物组成变化,并提供五种类型的微生物库,从不同角度全面表征微生物的功能。这些综合微生物库包括:(1)微生物功能途径,(2)微生物与疾病的关联,(3)EASs与微生物的关联,(4)生物活性微生物代谢产物,以及(5)人体栖息地。总之,CDEMI的独特之处在于它可以揭示不同条件下微生物分布/组成的模式,并基于多样的微生物库促进生物学解释。可通过http://rdblab.cn/cdemi/访问CDEMI。