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Culture-enriched metagenomic sequencing enables in-depth profiling of the cystic fibrosis lung microbiota

基因组 放大器 人类微生物组计划 扩增子测序 生物 计算生物学 微生物群 深度测序 康蒂格 基因组 DNA测序 16S核糖体RNA 遗传学 基因 聚合酶链反应
作者
Fiona Whelan,Barbara Waddell,Saad A. Syed,Shahrokh Shekarriz,Harvey Rabin,Michael D. Parkins,Michael G. Surette
出处
期刊:Nature microbiology [Nature Portfolio]
卷期号:5 (2): 379-390 被引量:64
标识
DOI:10.1038/s41564-019-0643-y
摘要

Amplicon sequencing (for example, of the 16S rRNA gene) identifies the presence and relative abundance of microbial community members. However, metagenomic sequencing is needed to identify the genetic content and functional potential of a community. Metagenomics is challenging in samples dominated by host DNA, such as those from the skin, tissue and respiratory tract. Here, we combine advances in amplicon and metagenomic sequencing with culture-enriched molecular profiling to study the human microbiota. Using the cystic fibrosis lung as an example, we cultured an average of 82.13% of the operational taxonomic units representing 99.3% of the relative abundance identified in direct sequencing of sputum samples; importantly, culture enrichment identified 63.3% more operational taxonomic units than direct sequencing. We developed the PLate Coverage Algorithm (PLCA) to determine a representative subset of culture plates on which to conduct culture-enriched metagenomics, resulting in the recovery of greater taxonomic diversity—including of low-abundance taxa—with better metagenome-assembled genomes, longer contigs and better functional annotations when compared to culture-independent methods. The PLCA is also applied as a proof of principle to a previously published gut microbiota dataset. Culture-enriched molecular profiling can be used to better understand the role of the human microbiota in health and disease. The PLate Coverage Algorithm (PLCA) determines the culture plates required for culture-enriched metagenomics and enables the recovery of greater taxonomic diversity, better quality metagenome-assembled genomes and improved functional annotations compared to metagenomics alone, indicating its utility for other microbiomes, especially those dominated by host DNA.
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