Species-level bacterial community profiling of the healthy sinonasal microbiome using Pacific Biosciences sequencing of full-length 16S rRNA genes.
Species-level bacterial community profiling of the healthy sinonasal microbiome using Pacific Biosciences sequencing of full-length 16S rRNA genes.
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DOI：
10.1186/s40168-018-0569-2
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发表时间：
2018-10-23
期刊：
影响因子：
15.5
通讯作者：
Mell JC
中科院分区：
文献类型：
--
作者：
Earl JP;Adappa ND;Krol J;Bhat AS;Balashov S;Ehrlich RL;Palmer JN;Workman AD;Blasetti M;Sen B;Hammond J;Cohen NA;Ehrlich GD;Mell JC
利用大规模并行DNA测序技术进行的泛细菌16 S rRNA微生物组调查改变了社区微生物学研究。然而，目前的16 S分析方法无法提供足够的分类分辨率和准确性，无法针对特定条件进行物种水平的关联研究。这是由于仅对短的16 S rRNA基因区域进行扩增和测序，通常仅提供科或属水平的分类。此外，测序错误往往夸大了存在的分类群的数量。太平洋生物科学公司（PacBio）的长读技术尤其受到每个碱基高错误率的困扰。在本文中，我们提出了一种微生物组分析管道，该管道利用PacBio环状共有序列（CCS）技术对全长细菌16 S rRNA基因进行测序和纠错，从而提供高保真的物种水平微生物组数据。模拟社区与20种细菌的分析表明，100%的特异性和灵敏度方面的分类分类。对250多个物种的模拟群落的检查表明，> 90%的分类群的物种水平分类是正确的，并且准确地捕获了相对丰度。大多数剩余的类群被证明是多，不正确，或不完全分类。使用这种方法，我们研究了存在于6个鼻窦部位的微生物群之间的微地理变异，通过拭子和活检，从前鼻腔到蝶窦，来自12名接受经蝶垂体切除术的受试者。我们发现受试者之间的差异大于受试者内的网站之间的差异，但也观察到显着的个体内差异。痤疮丙酸杆菌（最近更名为痤疮皮肤杆菌）是整个主要菌种，但发现不同的相对丰度的网站。我们用于单分子实时16 S rRNA基因测序的微生物组成分析管道（MCSMRT，https：//github.com/jpearl01/mcsmrt）通过使用整个16 S rRNA基因的CCS来提供更高的分类和系统发育分辨率，从而克服了基于标准标记基因的微生物组分析的缺陷。将这种方法扩展到其他标记基因可以帮助完善微生物物种的分类分配，改善参考数据库，以及加强微生物群落和生态失调状态之间的关联的特异性。本文的在线版本（10.1186/s40168-018-0569-2）包含补充材料，可供授权用户使用。
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. The online version of this article (10.1186/s40168-018-0569-2) contains supplementary material, which is available to authorized users.
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影响因子：
6.4
作者：
Adappa ND;Truesdale CM;Workman AD;Doghramji L;Mansfield C;Kennedy DW;Palmer JN;Cowart BJ;Cohen NA
通讯作者：
Cohen NA
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48
作者：
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影响因子：
5.8
作者：
Edgar, Robert C.
通讯作者：
Edgar, Robert C.
影响因子：
2.2
作者：
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通讯作者：
Reid, Gregor
影响因子：
3.7
作者：
Boase S;Foreman A;Cleland E;Tan L;Melton-Kreft R;Pant H;Hu FZ;Ehrlich GD;Wormald PJ
通讯作者：
Wormald PJ