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微生物组描述中的多变量分析:人类肠道蛋白质降解剂、代谢物与预测代谢功能的相关性

Multivariate Analysis in Microbiome Description: Correlation of Human Gut Protein Degraders, Metabolites, and Predicted Metabolic Functions.

作者信息

Raimondi Stefano, Calvini Rosalba, Candeliere Francesco, Leonardi Alan, Ulrici Alessandro, Rossi Maddalena, Amaretti Alberto

机构信息

Department of Life Sciences, University of Modena and Reggio Emilia, Modena, Italy.

BIOGEST-SITEIA, University of Modena and Reggio Emilia, Modena, Italy.

出版信息

Front Microbiol. 2021 Sep 17;12:723479. doi: 10.3389/fmicb.2021.723479. eCollection 2021.

Abstract

Protein catabolism by intestinal bacteria is infamous for releasing many harmful compounds, negatively affecting the health status, both locally and systemically. In a previous study, we enriched in protein degraders the fecal microbiota of five subjects, utilizing a medium containing protein and peptides as sole fermentable substrates and we monitored their evolution by 16S rRNA gene profiling. In the present study, we fused the microbiome data and the data obtained by the analysis of the volatile organic compounds (VOCs) in the headspace of the cultures. Then, we utilized ANOVA simultaneous component analysis (ASCA) to establish a relationship between metabolites and bacteria. In particular, ASCA allowed to separately assess the effect of subject, time, inoculum concentration, and their binary interactions on both microbiome and volatilome data. All the ASCA submodels pointed out a consistent association between indole and , and the relationship of butyric, 3-methyl butanoic, and benzenepropanoic acids with some bacterial taxa that were major determinants of cultures at 6 h, such as Lachnoclostridiaceae (), Clostridiaceae (), and Sutterellaceae ( and ). The metagenome reconstruction with PICRUSt2 and its functional annotation indicated that enrichment in a protein-based medium affected the richness and diversity of functional profiles, in the face of a decrease of richness and evenness of the microbial community. Linear discriminant analysis (LDA) effect size indicated a positive differential abundance ( < 0.05) for the modules of amino acid catabolism that may be at the basis of the changes of VOC profile. In particular, predicted genes encoding functions belonging to the superpathways of ornithine, arginine, and putrescine transformation to GABA and eventually to succinyl-CoA, of methionine degradation, and various routes of breakdown of aromatic compounds yielding succinyl-CoA or acetyl-CoA became significantly more abundant in the metagenome of the bacterial community.

摘要

肠道细菌引起的蛋白质分解代谢因释放许多有害化合物而声名狼藉,会对局部和全身的健康状况产生负面影响。在之前的一项研究中,我们利用一种以蛋白质和肽作为唯一可发酵底物的培养基,富集了五名受试者粪便微生物群中的蛋白质降解菌,并通过16S rRNA基因分析监测了它们的进化。在本研究中,我们将微生物组数据与通过分析培养物顶空中的挥发性有机化合物(VOC)获得的数据进行了融合。然后,我们利用方差分析同步成分分析(ASCA)来建立代谢物与细菌之间的关系。特别是,ASCA能够分别评估受试者、时间、接种物浓度及其二元相互作用对微生物组和挥发物组数据的影响。所有ASCA子模型都指出吲哚与[具体物质未给出]之间存在一致的关联,以及丁酸、3-甲基丁酸和苯丙酸与一些细菌分类群之间的关系,这些细菌分类群是6小时培养物的主要决定因素,如毛螺菌科([具体属未给出])、梭菌科([具体属未给出])和萨特氏菌科([具体属未给出]和[具体属未给出])。使用PICRUSt2进行宏基因组重建及其功能注释表明,在基于蛋白质的培养基中富集影响了功能谱的丰富度和多样性,尽管微生物群落的丰富度和均匀度有所下降。线性判别分析(LDA)效应大小表明,氨基酸分解代谢模块存在正差异丰度(<0.05),这可能是VOC谱变化的基础。特别是,预测编码属于鸟氨酸、精氨酸和腐胺转化为GABA并最终转化为琥珀酰辅酶A的超途径、蛋氨酸降解以及产生琥珀酰辅酶A或乙酰辅酶A的各种芳香化合物分解途径的基因,在细菌群落的宏基因组中变得明显更为丰富。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ec0/8484906/a87c3077e1fe/fmicb-12-723479-g001.jpg

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