Free-Flow Isoelectric Focusing for Comprehensive Separation and Analysis of Human Salivary Microbiome for Lung Cancer

微生物群 化学 唾液 等电聚焦 分馏 肺癌 16S核糖体RNA 微生物学 色谱法 分子生物学 生物 生物化学 生物信息学 基因 肿瘤科 医学
作者
Xiaoteng Jiang,Sha Liu,Yan Zhang,Yin Ji,Amir Sohail,Chengxi Cao,Peng Wang,Hua Xiao
出处
期刊:Analytical Chemistry [American Chemical Society]
卷期号:92 (17): 12017-12025 被引量:19
标识
DOI:10.1021/acs.analchem.0c02627
摘要

Human microbiome contains billions of microorganisms that play important roles in the biological system and different diseases. Due to its complexity, conventional culture-independent technology may underestimate the value of low-abundance bacteria, which calls for a highly efficient method for its enrichment and comprehensive analysis. In this study, we developed a recycling free-flow isoelectric focusing (RFFIEF) method-based electrophoresis method to separate salivary microbiome. First, we used Escherichia coli (DH5α) as a model for RFFIEF method development, which was focused in a narrow pH range (0.38 pH unit). The recovery rate was 80.81% with 5.85% relative standard deviation (n = 5). The optimized method was then adopted to separate the human salivary microbiome into 32 fractions, followed by 16S rRNA gene sequencing and metaproteomics analysis. After RFFIEF fractionation, we identified 508 bacterial genera, which increased by 225% on average (n = 3) when compared to the results before fractionation. We further compared the compositional change of microbiome in the saliva of lung cancer group (n = 22) and control group (n = 21) through RFFIEF. Quantitative results demonstrated that six bacterial genera were upregulated dramatically in the lung cancer group, while two genera were downregulated. Through qPCR verification in an independent sample set (n = 48), we confirmed that genus Granulicatella was significantly upregulated in the lung cancer group, whereas Pseudomonas was remarkably downregulated (p < 0.001). RFFIEF is an efficient and reproducible technology to fractionate the microbiome for its comprehensive analysis, which can be further applied to the in-depth study of the complex microbiomes and contribute to the discovery of disease-associated bacteria.
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