Department of Microbiology & Immunology, Centers for Genomic Sciences and Advanced Microbial Processing, Drexel University College of Medicine, 245 N 15th Street, Philadelphia, PA, 19102, USA.
Veteran's Administration Medical Center, Department of Otorhinolaryngology: Head and Neck Surgery, University of Pennsylvania Perelman School of Medicine, 3400 Spruce Street, 5 Ravdin, Philadelphia, PA, 19104-4283, USA.
Microbiome. 2018 Oct 23;6(1):190. doi: 10.1186/s40168-018-0569-2.
Pan-bacterial 16S rRNA microbiome surveys performed with massively parallel DNA sequencing technologies have transformed community microbiological studies. Current 16S profiling methods, however, fail to provide sufficient taxonomic resolution and accuracy to adequately perform species-level associative studies for specific conditions. This is due to the amplification and sequencing of only short 16S rRNA gene regions, typically providing for only family- or genus-level taxonomy. Moreover, sequencing errors often inflate the number of taxa present. Pacific Biosciences' (PacBio's) long-read technology in particular suffers from high error rates per base. Herein, we present a microbiome analysis pipeline that takes advantage of PacBio circular consensus sequencing (CCS) technology to sequence and error correct full-length bacterial 16S rRNA genes, which provides high-fidelity species-level microbiome data.
Analysis of a mock community with 20 bacterial species demonstrated 100% specificity and sensitivity with regard to taxonomic classification. Examination of a 250-plus species mock community demonstrated correct species-level classification of > 90% of taxa, and relative abundances were accurately captured. The majority of the remaining taxa were demonstrated to be multiply, incorrectly, or incompletely classified. Using this methodology, we examined the microgeographic variation present among the microbiomes of six sinonasal sites, by both swab and biopsy, from the anterior nasal cavity to the sphenoid sinus from 12 subjects undergoing trans-sphenoidal hypophysectomy. We found greater variation among subjects than among sites within a subject, although significant within-individual differences were also observed. Propiniobacterium acnes (recently renamed Cutibacterium acnes) was the predominant species throughout, but was found at distinct relative abundances by site.
Our microbial composition analysis pipeline for single-molecule real-time 16S rRNA gene sequencing (MCSMRT, https://github.com/jpearl01/mcsmrt ) overcomes deficits of standard marker gene-based microbiome analyses by using CCS of entire 16S rRNA genes to provide increased taxonomic and phylogenetic resolution. Extensions of this approach to other marker genes could help refine taxonomic assignments of microbial species and improve reference databases, as well as strengthen the specificity of associations between microbial communities and dysbiotic states.
通过大规模平行 DNA 测序技术进行的全细菌 16S rRNA 微生物组调查改变了群落微生物学研究。然而，当前的 16S 分析方法未能提供足够的分类分辨率和准确性，无法充分进行特定条件下的种级关联研究。这是因为仅扩增和测序短的 16S rRNA 基因区域，通常只能提供家族或属级别的分类。此外，测序错误通常会增加存在的分类单元数量。Pacific Biosciences（PacBio）的长读长技术尤其存在每个碱基高错误率的问题。本文介绍了一种微生物组分析流程，该流程利用 PacBio 环形一致性测序（CCS）技术对全长细菌 16S rRNA 基因进行测序和纠错，从而提供高保真度的种级微生物组数据。
用 20 种细菌的模拟群落进行分析，在分类学分类方面具有 100%的特异性和灵敏度。对一个 250 多种模拟群落的检查表明，>90%的分类单元的物种水平分类正确，并且相对丰度被准确捕获。其余大部分分类单元被证明是多次、错误或不完整分类。使用这种方法，我们通过 12 例接受经蝶窦垂体切除术的患者的前鼻腔到蝶窦的 6 个鼻窦部位的拭子和活检样本，检查了 6 个鼻窦部位微生物组的微地域变异。我们发现个体内的变异性大于个体内的变异性，尽管也观察到了显著的个体内差异。Propiniobacterium acnes（最近更名为 Cutibacterium acnes）在整个过程中都是主要物种，但在不同部位的相对丰度不同。
我们用于单分子实时 16S rRNA 基因测序（MCSMRT，https://github.com/jpearl01/mcsmrt）的微生物组成分析流程通过使用 CCS 对整个 16S rRNA 基因进行测序，克服了基于标准标记基因的微生物组分析的缺陷，从而提高了分类学和系统发育分辨率。将此方法扩展到其他标记基因可以帮助改进微生物物种的分类学分配，并改进参考数据库，同时增强微生物群落与生态失调状态之间的关联的特异性。
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