CVTree在454高通量测序分析菌群结构中的应用

目的探究将基于短串频度的CVTree方法用于反映菌群结构的16S rRNA基因的454高通量测序数据分析的可行性,为快速分析高通量菌群结构数据提供新的方法。方法对一个四世同堂的中国家庭7名成员肠道菌群和不同基因型及饮食类型的小鼠肠道菌群用454高通量方法获得16S rRNA基因的V3区的测序数据,用CVTree的方法进行菌群结构的比较分析。结果通过选取合适的短串长度,CVTree的方法能准确检测到各样本间的聚类关系,其结果与之前文献报道的基于Unifrac算法的结果相一致。结论CVTree能快速、有效地处理16S rRNA基因的454高通量测序数据,实现对不同菌群结构相似性的比较分析。     

分析方法对细菌群落16S rRNA基因扩增测序分析结果的影响

细菌16S rRNA基因扩增测序是当前环境微生物组学研究中应用最为广泛的方法之一。然而,测序序列最小分类单元的划分有多种方式,其对微生物多样性下游分析结果的影响还有待进一步探究。本研究通过提取5组环境样本(森林、农田、湿地土壤、湖泊沉积物和水体)的DNA进行16S rRNA基因扩增测序,对测序结果同时采用5种最小分类单元的划分方式(基于97%、98%、99%和100%序列相似性聚类的OTU以及基于DADA2算法得到的ASV)进行划分,比较分析最小分类单元划分方法对微生物群落多样性、组成、以及其与环境因子关联性分析造成的影响。结果表明,提高分类分辨率,能够获得更高的群落α多样性(Chao1和Shannon)和β多样性(P < 0.05),而相对于按序列相似性聚类的OTU,ASV方法会在一定程度上降低Chao1和PD指数。对于群落组成,分类单元的划分方式主要影响微生物组一些低丰度属(< 0.2%)的占比,而对较高的分类学水平(门水平)组成的影响较小。此外,冗余分析的结果表明,提高分类分辨率水平,能够使得环境因子对微生物群落能够获得更高的解释度,同时也会影响各环境因子对群落组成的...     

石棉地区藏族、彝族和汉族人群肠道微生物菌群构成与分布

目的 探讨石棉地区藏族、彝族和汉族人群肠道微生物菌群构成分布情况。 方法 将2017年7月至2019年7月在我院进行健康体检的并符合纳入和排除标准的125例健康人作为研究对象,其中汉族65例,彝族32例,藏族28例。收集受试健康人群清晨空腹粪便标本,利用高通量测序技术测定石棉地区藏族、彝族和汉族人群粪便样本16S rRNA序列并建立分类操作单元(OTU),分析肠道微生物结构特征及优势菌群,并对各民族人群肠道微生物进行聚类分析。 结果 共获得514 069条高质量16S rRNA序列,每个样品平均生成序列数为(4 112.55±1 258.67)条;97%相似度归并共获得68 232个OTU,每个样品平均OTU为(545.86±157.49)个;石棉地区汉族人群粪便标本中占优势细菌门为拟杆菌门和厚壁菌门,彝族及藏族人群粪便标本占优势细菌门为厚壁菌门和拟杆菌门,菌门水平上丰富度差异存在统计学意义(F=13.810,P48%。 结论 石棉地区藏族和彝族与汉族人群间肠道微生物优势细菌门及细菌属虽相似但在丰富度上存在差异,汉族健康人群肠道微生物菌群呈现不规律、分散聚类现象,彝族、藏族健康人群肠道微生物菌群呈现集中聚类现象,临床应根据种族差异对人群进行膳食指导以调控肠道微生态平衡。     

Windows下16S rRNA基因扩增子测序数据分析的简易流程

微生物组数据分析需要掌握Linux系统操作,这对缺乏计算机知识的生物研究人员是一个很大的障碍。为此我们设计了一套在Windows的Linux子系统(WSL)下分析16S rRNA基因扩增子高通量测序数据的简易流程。本流程整合常用的开源软件VSEARCH与QIIME等,能对16S rRNA测序数据进行质量控制、OTU聚类、多样性分析及结果可视化呈现。以唾液微生物组分析为例,详细介绍从原始数据到多样性统计分析过程的参数和命令,及结果解读。教学实践证明,此流程易于学习,并有助于掌握微生物组的基本概念与方法。利用Windows系统最新的WSL功能,本流程方便Windows用户使用大量在Linux上运行的生物信息工具,有助于促进微生物组研究的发展。流程的安装程序与测序数据可从网址(http://www. ligene. cn/win16s/)免费下载使用。     

16S rRNA基因两个可变区对反映肠道菌群多样性与物种鉴定能力的比较分析

【摘 要】 目的 目前基于新一代测序技术开展的人类肠道元基因组学研究已成为微生物学乃至整个生物学中最活跃和最有潜力的学科方向。现阶段绝大部分以肠道菌群为靶标的研究主要基于16S rRNA基因可变区测序。本研究关注的是测序技术发展使得序列的读长能力延长后,选择16S rRNA基因V1-V3区与V3-V5区进行测序所反映的多样性与物种组成信息的异同点。方法 以两个真实的16S rRNA基因全长Sanger测序数据为基础,对其中的V1-V3和V3-V5两种片段进行了模拟数据的分析,将它们分别与16S rRNA基因的全长片段在OTU多样性水平和物种组成信息方面进行比较。结果 结果显示V1-V3区在OTU多样性上较V3-V5区更为接近全长序列;在物种组成表现上,两个可变区鉴定出的大部分的属的丰度与全长序列分析结果一致,但是各自有少数属的丰度结果与全长序列丰度结果存在差异。结论 在多样性分析上,选择V1-V3区片段能得到与全长更为接近的结果;而具体到菌种的组成分析中,V1-V3区和V3-V5区都有其局限性。     

基于口腔菌群平衡研究鱼胶多糖治疗口腔溃疡的作用机制

研究鱼胶多糖对大鼠口腔溃疡的治疗效果以及鱼胶多糖治疗后大鼠口腔菌群的变化,旨在研究鱼胶多糖治疗口腔溃疡的效果及其原理。

用90%石碳酸溶液诱导大鼠口腔溃疡,将模型大鼠随机分为空白组（不做溃疡处理）,阳性对照组（西瓜霜涂抹）,鱼胶组（鱼胶涂抹）,自愈组（不用药）。阳性对照组和鱼胶组大鼠用药均为每天1次。于实验第4天,每组大鼠随机取7只进行口腔菌群样品分离并送测序公司进行16S rRNA基因测序,观察大鼠试验期间口腔溃疡创面的大小,体质量变化,耗粮量变化以及口腔菌群变化。

阳性对照组大鼠口腔溃疡痊愈时间为（6.70±0.68）d,鱼胶组口腔溃疡痊愈时间为（6.70±0.57）d,自愈组口腔溃疡痊愈时间为（12.00±0.89）d,鱼胶组与自愈组相比差异有统计学意义（P＜0.001）。16S rRNA基因测序结果显示,鱼胶组与自愈组之间差异菌群为黄单胞菌目（Xanthomonadales）、β变形菌目（Betaproteobacteriales）、厚壁菌门（Firmicutes）、乳杆菌目（Lactobacillales）、鞘脂杆菌目（Sphingobacteriales）、微球菌目（Micrococcales）、肠杆菌目（Enterobacteriales）、假单胞菌目（Pseudomonadales）、放线菌目（Actinomycetales）。

鱼胶多糖可通过调节口腔菌群从而起到治疗口腔溃疡的作用。

     

胃息肉患者胃液菌群结构分析

目的 分析幽门螺杆菌（H. pylori）在胃息肉患者中的感染情况,利用高通量测序技术分析胃息肉患者的胃液菌群组成,探究整体菌群变化与息肉发生的关系。方法 收集7例胃息肉患者的胃液（GP组）,7例胃体黏膜未见异常体检者胃液为对照组（C组）,统计H. pylori感染情况。提取细菌总DNA,采用高通量测序技术对16S rRNA基因的V3‒V4高变区测序,分析比较菌群结构。结果 （1）7例胃息肉患者中5例H. pylori阳性,H. pylori感染率为71.4% ;对照组与胃息肉组H. pylori感染率无差异。（2）两组之间菌群α多样性差异无统计学意义,β多样性有显著区别。门水平上,两组之间菌群差异无统计学意义;属水平上,胃息肉组胃液奈瑟菌属（P＜0.05）、嗜血杆菌属（P＜0.05）、Parvimonas属（P＜0.05）比例显著增加。结论 胃息肉患者胃液菌群发生紊乱,以奈瑟菌属、嗜血杆菌属、Parvimonas属显著增加为特征。     

高脂饮食中添加短链菊粉对小鼠肠道菌群的影响

目的:探究高脂饮食中添加短链菊粉对小鼠肠道菌群的影响。方法:选择8周龄雄性小鼠,5只喂食高脂饲料,5只喂食10%菊粉复合型高脂饲料,喂食8周后收集小鼠粪便,检测小鼠粪便中三种主要的短链脂肪酸。同时,提取小鼠粪便中的细菌基因组,对菌群基因组16S rRNA基因V4高变区进行测序,对数据进行PCoA分析、Alpha多样性分析、LEfSe分析和16S功能预测。结果:菊粉添加后,小鼠粪便中含有的细菌DNA量增多,短链脂肪酸增加。菊粉组和对照组PCoA图可以看到明显聚类。菊粉组物种多样性低于对照组。菊粉组小鼠粪便中S24_7菌科丰度上升;Lachnospiraceae(毛螺菌科),Ruminococcaceae(瘤胃菌科)和Deferribacteraceae(脱铁杆菌科)丰度下降。16S基因功能预测发现22个第二层级的KEGG通路发生变化。结论:高脂饮食情况下短链菊粉的添加会改变小鼠肠道菌群,继而影响肠道菌群的功能。     

甘肃省武威市胃癌患者肠道菌群分布

目的 通过比较甘肃省武威市胃癌患者和健康对照人群肠道菌群的分布,探讨胃癌患者肠道菌群的变化与胃癌发生发展的关系,并寻找可能作为该地区胃癌患者的潜在生物标志物.方法 收集24例胃癌患者和24例健康对照人群的粪便样本,提取DNA,采用16S rRNA基因高通量测序进行肠道菌群分析.结果 分析2组研究对象肠道菌群的Alpha...     

去卵巢小鼠肠道菌群和血脂的变化

目的 探讨去卵巢对小鼠肠道菌群和血脂的影响。 方法 12只10周龄C57BL/6小鼠随机分为2组:假手术组(SHAM组)和去卵巢组(OVX组),每组6只,进行12周的喂养。每2周测定小鼠体质量,12周后测肝脏指数、血清三酰甘油水平和游离脂肪酸水平,小肠进行病理学检查,收集小鼠粪便并在Illumina MiSeq测序平台进行16S rRNA基因测序检测。 结果 与SHAM组相比,OVX组小鼠体质量、肝脏指数、血清三酰甘油水平和游离脂肪酸水平明显增加(t=4.745,t=15.090,t=11.140,t=4.038,均P结论 去卵巢小鼠血脂升高和肠道菌群失衡,提示肠道菌群可能是预防和治疗雌激素缺乏后脂质代谢异常的潜在靶点。     

16S rRNA基因在微生物生态学中的应用

16S rRNA(Small subunit ribosomal RNA)基因是对原核微生物进行系统进化分类研究时最常用的分子标志物(Biomarker),广泛应用于微生物生态学研究中。近些年来随着高通量测序技术及数据分析方法等的不断进步,大量基于16S rRNA基因的研究使得微生物生态学得到了快速发展,然而使用16S rRNA基因作为分子标志物时也存在诸多问题,比如水平基因转移、多拷贝的异质性、基因扩增效率的差异、数据分析方法的选择等,这些问题影响了微生物群落组成和多样性分析时的准确性。对当前使用16S rRNA基因分析微生物群落组成和多样性的进展情况做一总结,重点讨论当前存在的主要问题以及各种分析方法的发展,尤其是与高通量测序技术有关的实验和数据处理问题。     

Benchmarking of 16S rRNA gene databases using known strain sequences

16S rRNA gene analysis is the most convenient and robust method for microbiome studies. Inaccurate taxonomic assignment of bacterial strains could have deleterious effects as all downstream analyses rely heavily on the accurate assessment of microbial taxonomy. The use of mock communities to check the reliability of the results has been suggested. However, often the mock communities used in most of the studies represent only a small fraction of taxa and are used mostly as validation of sequencing run to estimate sequencing artifacts. Moreover, a large number of databases and tools available for classification and taxonomic assignment of the 16S rRNA gene make it challenging to select the best-suited method for a particular dataset. In the present study, we used authentic and validly published 16S rRNA gene type strain sequences (full length, V3-V4 region) and analyzed them using a widely used QIIME pipeline along with different parameters of OTU clustering and QIIME compatible databases. Data Analysis Measures (DAM) revealed a high discrepancy in ratifying the taxonomy at different taxonomic hierarchies. Beta diversity analysis showed clear segregation of different DAMs. Limited differences were observed in reference data set analysis using partial (V3-V4) and full-length 16S rRNA gene sequences, which signify the reliability of partial 16S rRNA gene sequences in microbiome studies. Our analysis also highlights common discrepancies observed at various taxonomic levels using various methods and databases.     

Analysis,Optimization and Verification of Illumina-Generated 16S rRNA Gene Amplicon Surveys

The exploration of microbial communities by sequencing 16S rRNA genes has expanded with low-cost, high-throughput sequencing instruments. Illumina-based 16S rRNA gene sequencing has recently gained popularity over 454 pyrosequencing due to its lower costs, higher accuracy and greater throughput. Although recent reports suggest that Illumina and 454 pyrosequencing provide similar beta diversity measures, it remains to be demonstrated that pre-existing 454 pyrosequencing workflows can transfer directly from 454 to Illumina MiSeq sequencing by simply changing the sequencing adapters of the primers. In this study, we modified 454 pyrosequencing primers targeting the V4-V5 hyper-variable regions of the 16S rRNA gene to be compatible with Illumina sequencers. Microbial communities from cows, humans, leeches, mice, sewage, and termites and a mock community were analyzed by 454 and MiSeq sequencing of the V4-V5 region and MiSeq sequencing of the V4 region. Our analysis revealed that reference-based OTU clustering alone introduced biases compared to de novo clustering, preventing certain taxa from being observed in some samples. Based on this we devised and recommend an analysis pipeline that includes read merging, contaminant filtering, and reference-based clustering followed by de novo OTU clustering, which produces diversity measures consistent with de novo OTU clustering analysis. Low levels of dataset contamination with Illumina sequencing were discovered that could affect analyses that require highly sensitive approaches. While moving to Illumina-based sequencing platforms promises to provide deeper insights into the breadth and function of microbial diversity, our results show that care must be taken to ensure that sequencing and processing artifacts do not obscure true microbial diversity.     

OTU Analysis Using Metagenomic Shotgun Sequencing Data

Because of technological limitations, the primer and amplification biases in targeted sequencing of 16S rRNA genes have veiled the true microbial diversity underlying environmental samples. However, the protocol of metagenomic shotgun sequencing provides 16S rRNA gene fragment data with natural immunity against the biases raised during priming and thus the potential of uncovering the true structure of microbial community by giving more accurate predictions of operational taxonomic units (OTUs). Nonetheless, the lack of statistically rigorous comparison between 16S rRNA gene fragments and other data types makes it difficult to interpret previously reported results using 16S rRNA gene fragments. Therefore, in the present work, we established a standard analysis pipeline that would help confirm if the differences in the data are true or are just due to potential technical bias. This pipeline is built by using simulated data to find optimal mapping and OTU prediction methods. The comparison between simulated datasets revealed a relationship between 16S rRNA gene fragments and full-length 16S rRNA sequences that a 16S rRNA gene fragment having a length >150 bp provides the same accuracy as a full-length 16S rRNA sequence using our proposed pipeline, which could serve as a good starting point for experimental design and making the comparison between 16S rRNA gene fragment-based and targeted 16S rRNA sequencing-based surveys possible.     

A novel method for determining microflora composition using dynamic phylogenetic analysis of 16S ribosomal RNA deep sequencing data

Deep sequencing of the 16S rRNA gene provides a comprehensive view of bacterial communities in a particular environment and has expanded our ability to study the impact of the microflora on human health and disease. Current analysis methods rely on comparisons of the sequences generated with an expanding but limited set of annotated 16S rRNA sequences or phylogenic clustering of sequences based on arbitrary similarity cutoffs. We describe a novel approach to characterize bacterial composition using deep sequencing of 16S rRNA gene. Our method defines operational taxonomic units based on phylogenetic tree reconstruction and dynamic clustering of sequences using solely sequencing data. These OTUs can be used to identify differences in bacteria abundance between environments. This approach can perform better than previous phylogenetic methods and will significantly improve our understanding of the microfloral role on human diseases by providing a comprehensive analysis of the microbial composition from various bacterial communities.     

Robust computational analysis of rRNA hypervariable tag datasets

Next-generation DNA sequencing is increasingly being utilized to probe microbial communities, such as gastrointestinal microbiomes, where it is important to be able to quantify measures of abundance and diversity. The fragmented nature of the 16S rRNA datasets obtained, coupled with their unprecedented size, has led to the recognition that the results of such analyses are potentially contaminated by a variety of artifacts, both experimental and computational. Here we quantify how multiple alignment and clustering errors contribute to overestimates of abundance and diversity, reflected by incorrect OTU assignment, corrupted phylogenies, inaccurate species diversity estimators, and rank abundance distribution functions. We show that straightforward procedural optimizations, combining preexisting tools, are effective in handling large (10(5)-10(6)) 16S rRNA datasets, and we describe metrics to measure the effectiveness and quality of the estimators obtained. We introduce two metrics to ascertain the quality of clustering of pyrosequenced rRNA data, and show that complete linkage clustering greatly outperforms other widely used methods.     

Effects of OTU Clustering and PCR Artifacts on Microbial Diversity Estimates

Next-generation sequencing has increased the coverage of microbial diversity surveys by orders of magnitude, but differentiating artifacts from rare environmental sequences remains a challenge. Clustering 16S rRNA sequences into operational taxonomic units (OTUs) organizes sequence data into groups of 97 % identity, helping to reduce data volumes and avoid analyzing sequencing artifacts by grouping them with real sequences. Here, we analyze sequence abundance distributions across environmental samples and show that 16S rRNA sequences of >99 % identity can represent functionally distinct microorganisms, rendering OTU clustering problematic when the goal is an accurate analysis of organism distribution. Strict postsequencing quality control (QC) filters eliminated the most prevalent artifacts without clustering. Further experiments proved that DNA polymerase errors in polymerase chain reaction (PCR) generate a significant number of substitution errors, most of which pass QC filters. Based on our findings, we recommend minimizing the number of PCR cycles in DNA library preparation and applying strict postsequencing QC filters to reduce the most prevalent artifacts while maintaining a high level of accuracy in diversity estimates. We further recommend correlating rare and abundant sequences across environmental samples, rather than clustering into OTUs, to identify remaining sequence artifacts without losing the resolution afforded by high-throughput sequencing.     

基于amoA基因扩增子高通量测序的氨氧化古菌多样性分析方法

【背景】对于环境样品中氨氧化古菌(Ammonia-oxidizing archaea,AOA)多样性的研究,利用amoA功能基因作为分子标记会比16SrRNA基因有更强的特异性和更高的分辨率,能更准确地反映环境样品中氨氧化古菌的种群结构和分布特征。然而,目前对amoA基因扩增子高通量测序的分析存在两大限制因素:一是缺乏相应的amoA基因参考数据库;二是AOA amoA基因在种水平上的相似性阈值未知,分析过程中没有明确的划分种水平操作分类单元(Operational taxonomic unit,OTU)的阈值。【目的】构建基于amoA功能基因序列分析氨氧化古菌多样性的方法,为基于高通量测序的功能微生物多样性分析提供参考。【方法】基于目前已通过分离纯化或富集培养获得的34株氨氧化古菌及功能基因数据库中收录的环境样品amoA基因序列,构建氨氧化古菌amoA基因参考数据库。通过菌株间两两比对获得的amoA基因相似度与16SrRNA基因相似度的相关性分析,确定amoA基因在种水平上的相似性阈值。基于MOTHUR软件平台,利用建立的参考数据库和确定的阈值对南海一个垂直水体剖面样品的amoA基因序列进行多样性分析。【结果】构建了含有26 091条序列信息的古菌amoA基因参考数据库,确定了89%作为分析过程中古菌amoA基因划分种水平OTU的阈值,对南海水体样品氨氧化古菌的多样性分析结果很好地显示了南海不同深度水层水体中氨氧化古菌的种群结构和系统发育关系,有效揭示了南海氨氧化古菌的垂直分布差异。【结论】建立了基于amoA基因高通量测序的氨氧化古菌多样性分析方法,此方法可以有效分析环境样品中氨氧化古菌的多样性。     

Clustering 16S rRNA for OTU prediction: a method of unsupervised Bayesian clustering

MOTIVATION: With the advancements of next-generation sequencing technology, it is now possible to study samples directly obtained from the environment. Particularly, 16S rRNA gene sequences have been frequently used to profile the diversity of organisms in a sample. However, such studies are still taxed to determine both the number of operational taxonomic units (OTUs) and their relative abundance in a sample. RESULTS: To address these challenges, we propose an unsupervised Bayesian clustering method termed Clustering 16S rRNA for OTU Prediction (CROP). CROP can find clusters based on the natural organization of data without setting a hard cut-off threshold (3%/5%) as required by hierarchical clustering methods. By applying our method to several datasets, we demonstrate that CROP is robust against sequencing errors and that it produces more accurate results than conventional hierarchical clustering methods. Availability and Implementation: Source code freely available at the following URL: http://code.google.com/p/crop-tingchenlab/, implemented in C++ and supported on Linux and MS Windows.