Name: next-generation sequencing of 16s ribosomal rna gene amplicons
Uploaded: 2014-08-29T14:00:00.000+00:00
Duration: 10 min 24 s
Description: Watch this Scientific Journal Video about Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons at JoVE.com

Development of the method presented here has been carried out with various sources of funding, including Genome Canada and Genome Quebec, Environment Canada STAGE program and internal NRC funds.

1，16S rRNA基因扩增子文库制备的融合方法<ol><li>解冻的引物（正向，F343-IONA-MIDXX:5&#39;-CCA TCT CAT CCC TGC GTG TCT CCG交流 牛逼的CAG XXX XXX XXX XTA CGG鲁尔GCA GCA的G-3&#39;;反转，R533-IonP1:5&#39;-CCT四氯化碳达GGG CAG TCG GTG AT A TTA CCG CGG CTG CTG的GC-3&#39;;大胆:离子激流专用适配器;斜体字:排序键校准信号强度的测序运行的开始;字体:模板的特异性引物，X ... ×:10碱基对的条形码， 如表1一些条形码序列），2倍HotStartTaq加上主混合物和样品。使用前调匀，除了主结构应该轻轻混合。 </li><li>准备PCR混合物（20微升反应）与0.5微米的反向引物，0.4毫克/毫升牛血清白蛋白（BSA）和1个HotStartTaq加上主结构。加入预混的18.5微升至eACH PCR管。 </li><li>添加一个正向引物（0.5微升20毫米），不同的条形码为每个将要一起测序样品。加入1μl样品（1-10纳克/微升）的PCR管。 </li><li>发生在PCR仪的管子，然后运行以下程序:5分钟预变性95℃; 95℃，25个循环，30秒，55℃30秒，72℃45秒;在72℃下进行10分钟的最终延伸。 PCR产物可以保持在4℃的CO / N或冷冻直至使用。 </li></ol> 2，扩增子的分离纯化，定量和池<ol><li>使用最大可用梳子准备了2％琼脂糖凝胶。加入5微升的6倍的凝胶上样染料对凝胶反应和负载，每个样品通过一个空的分离良好。运行在70 V凝胶50分钟。 </li><li>以凝胶的图片和削减波段（预计规模约250基点:190 bp的产品加63个基点适配器和条形码）用干净的手术刀。 </li><lI&gt;称量切除乐队，并继续与凝胶的提取和纯化的PCR产物（ 如与QIAquick凝胶提取试剂盒）。 </li><li>量化每个PCR产物用的PicoGreen和使稀释液，得到储备液以5×10 9个分子/微升（约1毫微克/微升）。如果一些样品显示出较低的放大，调整稀释至较低浓度，牢记适于后续程序的最低浓度是1.57×10 7个分子/μl的（26点）。 </li><li>池10微升的每个稀释的PCR产物以获得样本的等摩尔池。准备为每个​​计划的测序运行的一个独立的游泳池。汇集的PCR产物可以保存在冰箱中直到使用。 </li></ol> 3，乳液PCR和测序<ol><li>执行乳液PCR方法: <ol><li>淡化汇集PCR产物26日下午在25微升的体积。解冻剂从离子PGM模板套件。 </li><li>准备乳液PCR混合物（试剂盒提供的试剂）在PCR罩和板凳上添加汇集PCR产物与球体颗粒（提供）。调匀，并在过滤器滤芯插入混合物中，用乳化油仔细TOP（提供）。 </li><li>缓慢反向滤筒和负载上的自动乳剂PCR设备（ 例如，离子单键2仪器）。选择合适的方案，并启动程序。 </li></ol></li><li>自动化的乳液PCR完成后，除去从集合管的上清液，并在管的底部取回球体颗粒。悬浮在100μl的洗涤液（试剂盒提供）的领域和需要2.0微升等分图书馆量化。 </li><li>执行库量化。 <ol><li>混合100μl的SSPE缓冲液（150 mM氯化钠，10mM的磷酸钠4，1mM的娜2 -EDTA）与2微升适配器B&#39;秘境（5&#39;-FAM -CTğAGA CTG CCA AGG CAC ACA GGG GAT AGG-3&#39;）探头和2微升适配器A-Cy5信号（5&#39;-Cy5的 -CCA TCT CAT CCC TGC GTG TCT CCG ACT CAG-3&#39;）的探针。加入52微升该混合物到在步骤3.2所采取的2.0微升等分试样，并添加2微升的洗涤溶液中（从模板试剂盒）对剩余的52微升作为空白进行定量。 </li><li>孵育在95℃下2分钟，然后在37℃下持续2分钟。转让1.5 ml离心管的混合物。 </li><li>添加1.0 TEX缓冲液（10毫米的Tris，1 mM的EDTA，0.01％的Triton X-100，pH值8.0）毫升，混匀并离心15,500 xg离心3分钟在室温。除去上清液，留下20微升在每个管中。重复此洗涤步骤两次。 </li><li>加入180微升TEX缓冲区，悬浮沉淀的颗粒转移到500微升量子比特管。 </li><li>使用量子比特装置，测量荧光的FAM和Cy5和计算使用可用的离子社群网站上的计算器正球体的比例。 </li></ol></li><li>丰富的球体颗粒使用自动化富集系统（ 例如，离子一键捕获系统）正球（含扩增DNA）的剩余部分。 <ol><li>移液管的试剂在所提供的8孔条中的适当的孔中:好1:从步骤3.2球形颗粒;以及2:预洗MyOne链霉亲和C1珠（13微升）;以及3，4和5:洗涤溶液（在模板试剂盒中提供）;以及6:空;以及7:熔断溶液（125mM的氢氧化钠，0.1％吐温20）;以及8:空。 </li><li>安装在移液臂尖和0.2 ml管的样品采集。启动仪器。在浓缩的结尾，加10微升中和液（提供）。富集的颗粒可以保存4℃下15天。 </li></ol></li><li>初始化测序仪: <ol><li>连接到测序仪，并准备测序运行计划，指定测序运行的详细信息。 </li><li> INSTAL升的洗涤溶液＃2（在Ion PGM测序试剂盒提供）中，洗涤溶液＃1（350微升的100mM NaOH）和自动pH缓冲溶液（设置）。 </li><li>启动初始化程序，并在出现提示时在各自的50ml试管中添加的dATP，三磷酸dCTP和dTTP的核苷酸（提供）。拧管到测序仪，并继续初始化程序。当初始化完成后，立即开始运行程序。 </li></ol></li><li>制备的样品进行测序，并加载芯片: <ol><li>通过在15,500 XG离心1.5分钟收集来自步骤3.4在管底部的富氧球体。除去上清液离开正好3微升。 </li><li>加入3μL测序引物（在测序试剂盒提供），吹打起来调匀上下悬浮颗粒。孵育在95℃下2分钟，然后在37℃下持续2分钟。 </li><li>加入1μl聚合酶（提供），拌匀incub的吃在RT 5分钟。继续加载在30分钟内对测序芯片酶球体混合物。 </li><li>在培养过程中，加载的编曲测序芯片进行芯片的质量检查。通过离心除去从芯片的液体和通过缓慢移液向下酶球体混合物（7微升），并避免在芯片中引入的气泡的加载在芯片中的样本。 </li><li>离心芯片三次，在微量离心1分钟。改变芯片在每个离心的方向和通过上下吹打在每次运行之间混合。 </li><li>装载仪器的芯片，并开始运行。 </li></ol></li></ol> 4，基本序列数据分析<ol><li>连接到定序的数据服务器并下载fastq文件。创建一个制表符分隔的"寡"含底漆和条形码信息（参见示例见表1）文件。从MOT下载Greengenes参考文件许网站（ <a href="http://www.mothur.org/wiki/Taxonomy_outline">http://www.mothur.org/wiki/Taxonomy_outline</a> ）。 </li><li>启动Mothur并进行分析: <ol><li>使用下面的命令转换为fastq文件到FASTA和质量文件:fastq.info（fastq = test.fastq） </li><li>确定每个序列的组成员，并使用以下命令修剪的顺序:trim.seqs（FASTA = test.fasta，寡= barcode.txt，QFile时= test.qual，qwindowaverage = 20，qwindowsize = 50，= MINLENGTH 150， keepforward = T） </li><li>各种选项可根据所需的严格性来修改</li><li>使用下面的命令进行分类的greengenes分类序列:classify.seqs（FASTA = test.trim.fasta，方法=旺，组= test.groups，模板= gg_99_otus.fasta，分类= gg_99_otus.tax，截止= 50） </li></ol></li></ol>

<ol>
	<li>Sipos, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Addressing+PCR+biases+in+environmental+microbiology+studies.">Addressing PCR biases in environmental microbiology studies.</a> Methods Mol. Biol. 599, 37-58 (2010).</li><li>Schloss, P. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Reducing+the+effects+of+PCR+amplification+and+sequencing+artifacts+on+16S+rRNA-based+studies.">Reducing the effects of PCR amplification and sequencing artifacts on 16S rRNA-based studies.</a> PLoS ONE. 6, e27310 (2011).</li><li>Tringe, S. G., Hugenholtz, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+renaissance+for+the+pioneering+16S+rRNA+gene.">A renaissance for the pioneering 16S rRNA gene.</a> Curr. Opin. Microbiol. 11, 442-446 (2008).</li><li>Margulies, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genome+sequencing+in+microfabricated+high-density+picolitre+reactors.">Genome sequencing in microfabricated high-density picolitre reactors.</a> Nature. 437, 376-380 (2005).</li><li>Kuczynski, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Microbial+community+resemblance+methods+differ+in+their+ability+to+detect+biologically+relevant+patterns.">Microbial community resemblance methods differ in their ability to detect biologically relevant patterns.</a> Nat. Methods. 7, 813-819 (2010).</li><li>Bondici, V. F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Microbial+communities+in+low-permeability,+high+pH+uranium+mine+tailings:+characterization+and+potential+effects.">Microbial communities in low-permeability, high pH uranium mine tailings: characterization and potential effects.</a> J. Appl. Microbiol. 113, 1671-1686 (2013).</li><li>Auffret, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Impact+of+water+quality+on+the+bacterial+populations+and+off-flavours+in+recirculating+aquaculture+systems.">Impact of water quality on the bacterial populations and off-flavours in recirculating aquaculture systems.</a> FEMS Microbiology Ecology. 84, 235-247 (2013).</li><li>Bell, T. H., Yergeau, E., Juck, D., Whyte, L. G., Greer, C. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Alteration+of+microbial+community+structure+affects+diesel+biodegradation+in+an+Arctic+soil.">Alteration of microbial community structure affects diesel biodegradation in an Arctic soil.</a> FEMS Microbiol. Ecol. 85, 51-61 (2013).</li><li>Bell, T. H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Predictable+bacterial+composition+and+hydrocarbon+degradation+in+Arctic+soils+following+diesel+and+nutrient+disturbance.">Predictable bacterial composition and hydrocarbon degradation in Arctic soils following diesel and nutrient disturbance.</a> ISME J. 7, 1200-1210 (2013).</li><li>Yergeau, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Next-generation+sequencing+of+microbial+communities+in+the+Athabasca+River+and+its+tributaries+in+relation+to+oil+sands+mining+activities.">Next-generation sequencing of microbial communities in the Athabasca River and its tributaries in relation to oil sands mining activities.</a> Appl. Environ. Microbiol. 78, 7626-7637 (2012).</li><li>Yergeau, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Aerobic+biofilms+grown+from+Athabasca+watershed+sediments+are+inhibited+by+increasing+bituminous+compounds+concentrations.">Aerobic biofilms grown from Athabasca watershed sediments are inhibited by increasing bituminous compounds concentrations.</a> Appl. Environ. Microbiol. 79, 7398-7412 (2013).</li><li>Yergeau, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Microbial+expression+profiles+in+the+rhizosphere+of+willows+depend+on+soil+contamination.">Microbial expression profiles in the rhizosphere of willows depend on soil contamination.</a> ISME J. 8, 344-358 (2013).</li><li>Deagle, B. E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantifying+sequence+proportions+in+a+DNA-based+diet+study+using+Ion+Torrent+amplicon+sequencing:+which+counts+count.">Quantifying sequence proportions in a DNA-based diet study using Ion Torrent amplicon sequencing: which counts count.</a> Mol. Ecol. Resour. 13, 620-633 (2013).</li><li>Milani, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Assessing+the+fecal+microbiota:+an+optimized+Ion+Torrent+16S+rRNA+gene-based+analysis+protocol.">Assessing the fecal microbiota: an optimized Ion Torrent 16S rRNA gene-based analysis protocol.</a> PLoS ONE. 8, e68739 (2013).</li><li>J&#252;nemann, S., Prior, P., Szczepanowski, R., Harks, I., Ehmke, B., Goesmann, A., Stoye, J., Dag Harmsen, . <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Bacterial+community+shift+in+treated+periodontitis+patients+revealed+by+Ion+Torrent+16S+rRNA+gene+amplicon+sequencing.">Bacterial community shift in treated periodontitis patients revealed by Ion Torrent 16S rRNA gene amplicon sequencing.</a> PLoS ONE. 7, e41606 (2012).</li><li>Petrof, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Stool+substitute+transplant+therapy+for+the+eradication+of+Clostridium+difficile+infection:+'RePOOPulating'+the+gut.">Stool substitute transplant therapy for the eradication of Clostridium difficile infection: 'RePOOPulating' the gut.</a> Microbiome. 1, 3 (2013).</li><li>Whiteley, A. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Microbial+16S+rRNA+Ion+Tag+and+community+metagenome+sequencing+using+the+Ion+Torrent+(PGM)+Platform.">Microbial 16S rRNA Ion Tag and community metagenome sequencing using the Ion Torrent (PGM) Platform.</a> J. Microbiol. Meth. 91, 80-88 (2012).</li><li>Caporaso, J. G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Ultra-high-throughput+microbial+community+analysis+on+the+Illumina+HiSeq+and+MiSeq+platforms.">Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms.</a> ISME J. 6, 1621-1624 (2012).</li><li>Kozich, J. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Development+of+a+dual-index+sequencing+strategy+and+curation+pipeline+for+analyzing+amplicon+sequence+data+on+the+MiSeq+Illumina+sequencing+platform.">Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform.</a> Appl. Environ. Microbiol. 79, 5112-5120 (2013).</li><li>Hamady, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fast+UniFrac:+facilitating+high-throughput+phylogenetic+analyses+of+microbial+communities+including+analysis+of+pyrosequencing+and+PhyloChip+data.">Fast UniFrac: facilitating high-throughput phylogenetic analyses of microbial communities including analysis of pyrosequencing and PhyloChip data.</a> ISME J. 4, 17-27 (2010).</li><li>Quince, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Removing+noise+from+pyrosequenced+amplicons.">Removing noise from pyrosequenced amplicons.</a> BMC Bioinformatics. 12, 38 (2011).</li><li>Huse, S. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Ironing+out+the+wrinkles+in+the+rare+biosphere+through+improved+OTU+clustering.">Ironing out the wrinkles in the rare biosphere through improved OTU clustering.</a> Environ. Microbiol. 12, 1889-1898 (2010).</li><li>Edwards, J. E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Characterization+of+the+dynamics+of+initial+bacterial+colonization+of+nonconserved+forage+in+the+bovine+rumen.">Characterization of the dynamics of initial bacterial colonization of nonconserved forage in the bovine rumen.</a> FEMS Microbiol. Ecol. 62, 323-335 (2007).</li><li>Rastogi, G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+PCR-based+toolbox+for+the+culture-independent+quantification+of+total+bacterial+abundances+in+plant+environments.">A PCR-based toolbox for the culture-independent quantification of total bacterial abundances in plant environments.</a> J. Microbiol. Methods. 83, 127-132 (2010).</li><li>Baker, G. C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Review+and+re-analysis+of+domain-specific+16S+primers.">Review and re-analysis of domain-specific 16S primers.</a> J. Microbiol. Methods. 55, 541-555 (2003).</li><li>Schloss, P. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+effects+of+alignment+quality,+distance+calculation+method,+sequence+filtering,+and+region+on+the+analysis+of+16S+rRNA+gene-based+studies.">The effects of alignment quality, distance calculation method, sequence filtering, and region on the analysis of 16S rRNA gene-based studies.</a> PLoS Comput. Biol. 6, e1000844 (2010).</li><li>Links, M. G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+chaperonin-60+universal+target+Is+a+barcode+for+bacteria+that+enables+de+novo+assembly+of+metagenomic+sequence+data.">The chaperonin-60 universal target Is a barcode for bacteria that enables de novo assembly of metagenomic sequence data.</a> PLoS ONE. 7, e49755 (2012).</li><li>Vos, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparison+of+rpoB+and+16S+rRNA+as+markers+in+pyrosequencing+studies+of+bacterial+diversity.">Comparison of rpoB and 16S rRNA as markers in pyrosequencing studies of bacterial diversity.</a> PLoS ONE. 7, e30600 (2012).</li></ol>

The authors have nothing to disclose.

<html>这里介绍的方法是简单的和便宜的，并允许许多实验室访问宏基因组测序的能力。虽然它取决于所采用的测序平台，一旦文库构建很少的手工操作时间是必需的，与大多数的过程中被自动化。对于这里所用的测序平台（离子洪流PGM），该完整的程序可以在工作两天进行。在编写（2013年9月）的那一刻，与上文所详述的例子中，试剂成本如下:PCR检测36个样品的扩增:25元，凝胶净化和的PicoGreen的DNA的36个样品定量:125美元，乳液PCR方法一池扩增子样品:$ 150测序试剂:250元，共计550元或15元的样品或0.0015美元每质量过滤读取。此价格不包含仪器的服务合同，仪器折旧，技术人员的工资和实验室空间使用情况。 帐篷"&gt;其中最重要的步骤是泳池的所有产品的等摩尔比，以便检索相似数目的读取为各样品。的PicoGreen定量在这里使用的，但其他的方法可能是合适的，但较不准确（ 例如，紫外定量，基于凝胶的量化），即使这样做是最精确的定量和池，有在每个样品中读取数一些可变性，并在典型的运行在表2中详述的，其范围是从4380到32750读出，以平均10338读取，如果处理大量样品（大于40-50）的，单柱凝胶纯化可以通过凝胶纯化来代替在使用平板或纯化珠用严格的尺寸切断（ 例如，AMPure珠） 。 到目前为止，最常用的下一代测序技术的16S rRNA基因是454在这个协议中所使用的离子激流测序技术是概念上非常类似到454和这两种技术都容易产生相同类型的排序错误。不足为奇的是，它表明，离子激流测序导致测序结果非常相似，454测序10。近年来，许多研究人员研究利用Illumina的技术的16S rRNA基因扩增子测序18,19。在任何情况下，这将是很容易通过改变融合引物的序列以匹配所需要的这些测序技术的适配器和条形码一样，在该方法中最近描述的，以适应当前的协议，用于其他的台式测序仪一样的Illumina MiSeq或454 GS小型对于Illumina的MiSeq 19。另外，研究人员可以按照步骤1和2在这里详细的协议，并发送汇集扩增到乳液PCR和测序将进行测序中心。 16S rRNA基因的读取进行修整和归类使用Mothur，但许多其它的分析，可以执行上16S rRNA基因的扩增子。例如，β多样性可以通过计算在使用中列出的步骤每个样本对之间的Unifrac距离进行评估<a href="http://unifrac.colorado.edu/">http://unifrac.colorado.edu/</a> 20。阿尔法多样性指数和各样品的业务分类学单元的数目可以使用工具内QIIME像AmpliconNoise 21或使用由Huse 等22和可用的内Mothur概述的过程来计算。 这里所用的引物扩增的可变区3和4的16S rRNA基因，但是许多其他区域可以被定位的。在本研究中，16S rRNA基因是从植物材料中扩增和引物的选择是为了避免叶绿体16S rRNA基因23,24的扩增。有各种各样的变化是在产品长度，分类能力和有效性25,26的术语可用的其它引物。然而，在所有的情况下200-400 bp的读出的16S rRNA基因不能可靠归类在种的水平，并分析仅限于属和更高的分类学水平。如果需要物种水平上的信息，如cpn60和的rpoB基因27,28其它基因可能是更合适的。未来大幅下降的测序，并增加对分析工具的权力的成本可能会使得人们能够通过元基因组鸟枪取代16S rRNA基因序列，但在那之前的16S rRNA基因测序仍然环境微生物的黄金标准。

宏基因组测序是一个非常强大的技术，因为它的目标包含了环境样品中的遗传信息的全部内容。有不同口味的宏基因组测序，包括鸟枪法测序，大插入库和扩增子测序。扩增子的测序提供了相对便宜，快速，能够产生读取从可大致排列的单个基因组区域的优点。此外，数据分析的工作流程的扩增子测序大多标准化。然而，因为它是基于PCR技术，它具有所有相关的不完全特异性的，不完整的覆盖和引物的偏见施力1,2，这使得该方法的半定量的最好的。几个基因组区域可以有针对性的扩增子测序，包括功能基因，但最流行的选择是使用标记基因如16S rRNA基因生成的社区形象。传统上，16S rRNA基因的扩增子sequencing进行了使用劳动力密集的技术，其中包括克隆在大肠杆菌大肠杆菌 ，菌落采摘和质粒提取，随后用Sanger测序对分离的质粒，并因此，大多数研究分析每个样品少于100个克隆。下一代测序带来两个主要的进步:大规模并行化的测序反应，而最重要的是，对模板的克隆分离，而无需插入的基因片段在宿主中。这极大地简化了16S rRNA基因扩增子测序，这是现在回来许多环境微生物学研究的常规功能，造成了"复兴"的16S rRNA基因扩增子测序3。 由于罗氏454测序于2005年4问世，其他几个新一代测序技术已经出现在市场上（ 如 Illumina公司，实心，PacBio）。最近，推出台式序列RS带到小型实验室的测序能力以前仅限于大测序中心。五台式机是目前可供选择:454 GS少年，离子激流个人基因组机（PGM）和质子，以及Illumina的MiSeq和NextSeq 500虽然所有这些音序器提供每少跑和每美元减少基地读取比大多数全大规模测序仪，它们更灵活，快速，而且其低购置和运行成本，使他们负担得起的小型学术实验室。台式测序仪特别适合于扩增子，小基因组和低复杂度的宏基因组测序在环境微生物学研究好，因为这种类型的研究，一般不需要测序的一个极端的深度。例如，人们普遍认为，对于16S rRNA基因测序研究的读取次数，每个样品是不是最重要的，因为〜1,000读取可以生成相同的图案作为多亿读取数据集5。话虽如此，台面下generatio<html> Ñ​​音序器仍然会产生大量的序列数据，以最大产量〜35 MBP（454 GS初级）〜2 GBP（离子洪流PGM），〜10-15 GBP（离子激流腾）〜10 GBP（Illumina的MiSeq）和〜100 GBP（Illumina公司下一步SEQ 500），这是以上对于大多数环境微生物学研究不够。 使用台式测序仪16S rRNA基因扩增新一代测序最近已经应用于各种环境中。例如，所述离子洪流PGM已用于社会铀尾矿该过再循环油砂开采影响沉积物的水产养殖系统的烃污染北极土壤8,9 7，特别高的pH值和低渗透性6，分析和从阿萨巴斯卡河10,11生物膜柳树种植的人类和动物的尸体13-16的污染土壤12和厌氧沼气池17的根际。 jove_content"&gt;在这方面的贡献，我们详细我们的方法使用台式下一代定序（离子激流PGM）进行排序内部16S rRNA基因扩增的DNA提取后，16S rRNA基因使用包含域级细菌引物扩增测序接头和独特，样本特异性序列（条码）的扩增子进行纯化，定量并汇集在等摩尔比的混合样品然后被克隆扩增以乳液PCR和测序。结果序列是使用公开可用的生物信息学工具（分析例如，Mothur）。

<table><tbody><tr><td>Ion 314 Chip Kit v2</td><td>Life Technologies</td><td>4482261</td><td></td></tr><tr><td>Ion PGM Sequencing 200 Kit v2</td><td>Life Technologies</td><td>4482006</td><td></td></tr><tr><td>Ion PGM Template OT2 200 Kit</td><td>Life Technologies</td><td>4480974</td><td></td></tr><tr><td>HotStarTaq Plus Master Mix Kit</td><td>Qiagen</td><td>203646</td><td></td></tr><tr><td>Primers and probes</td><td>IDT</td><td>NA</td><td></td></tr><tr><td>Qiaquick Gel Extraction Kit</td><td>Qiagen</td><td>28704</td><td></td></tr><tr><td>BSA 20 mg/ml</td><td>Roche</td><td>10,711,454,001</td><td></td></tr><tr><td>Dynabeads MyOne Streptavidin C1</td><td>Life Technologies</td><td>65001</td><td></td></tr></tbody></table>

next-generation sequencing of 16s ribosomal rna gene amplicons

其中在微生物生态学中的主要问题是"谁在那里？"这个问题可以使用各种工具来回答，但对长期的黄金标准之一，是由序列域级别的PCR产生16S核糖体RNA（rRNA）的基因扩增反应，从基因组DNA扩增。传统上，这是通过克隆和桑格（毛细管电泳）的PCR扩增子的测序进行。新一代测序技术的出现，极大地简化并提高了测序深度16S rRNA基因测序。台式测序仪的引入，现在允许小型实验室进行内部的16S rRNA基因序列在几天之内。这里，使用台式下一代定序为16S rRNA基因的扩增子测序的方法详述。对环境的DNA首先通过PCR，使用含有序列适配器和条形码引物进行扩增。然后它们被耦合到通过乳液PCR方法的球形颗粒。颗粒是升oaded一次性芯片上和芯片插入后进行测序的测序机。的序列的检索fastq格式，过滤，条形码是用来建立样本成员的读取。该过滤和分级读取，然后使用公开可用的工具进一步分析。一个例子的分析，其中读出进行分类的软件包Mothur被赋予在一个分类寻找算法。这里介绍的方法简单，价格低廉，简单，应该帮助小型实验室，以利用从正在进行的基因组革命。

纯化的凝胶后，用PCR扩增25个循环，扩增产物通常是在0.2-10.0的浓度纳克于50μl水中。这可能会发生变化很大，这取决于起始的DNA浓度，样品的类型和所用的纯化试剂盒上。它建议保持PCR循环数，以最低可能的，以避免嵌合体的形成，并降低扩增偏倚，牢记，所有样品应使用周期相同数量的扩增。以最小化多克隆读取数和空球和最大化的读取次数，量子比特率应介于0.1和0.3和FAM荧光应高于200使用314芯片上的离子洪流PGM，平均输出大约是结果在Mothur的滤波后0.3-0.5拿手质量的读取。 表2示出了一些典型的故障的过程的每个步骤中为含36多路复用ENVIRON运行后读取心理样品扩增引物靶向16S的V3-4地区和分析使用Mothur。在Mothur的trim.seqs程序生成一个包含通过质量过滤器和一个"* .scrap.fasta"包含该序列没有随着原因通过质量过滤器的序列的"* .trim.fasta"文件为抑制在序列标题。当条形码中的"寡"的文件提供，该命令也将产生一个"* .groups"文件，该文件包含基于条形码序列中的每个序列的组成员。该classify.seqs过程生成一个".tax.summary"，可以在Excel中打开。此文件包含的分类单位（以行为单位）对每个样品的摘要（中柱）。此文件可用于下游的统计分析和可视化的群落组成在不同分类级别。在".taxonomy"文件中包含了详细的分类归属于每个序列。在所有36个样品的语系/一流水平的平均社会组成如图1所示。 <img alt="图1" fo:content-width="5in" src="/files/ftp_upload/51709/51709fig1highres.jpg" width="500" /> 图1:平均群落组成，在所有样品中的动物门/一流水平。 <table border="0" cellpadding="0" cellspacing="0" width="248"><colgroup><col /><col /><col /></colgroup><tbody><tr height="20"><td height="20" style="height:20px;width:64px;">前进</td><td style="width:139px;"> TACGGRAGGCAGCAG </td><td style="width:47px;"></td></tr><tr height="20"><td height="20" style="height:20px;">条形码</td><td> CTAAGGTAAC </td><td> Sample01 </td></tr><tr height="20"><td height="20" style="height:20px;">条形码</td><td> TAAGGAGAAC </td><td> Sample02 </td></tr><tr heighT ="20"&gt; <td height="20" style="height:20px;">条形码</td><td> AAGAGGATTC </td><td> Sample03 </td></tr><tr height="20"><td height="20" style="height:20px;">条形码</td><td> TACCAAGATC </td><td> Sample04 </td></tr><tr height="20"><td height="20" style="height:20px;">条形码</td><td> CAGAAGGAAC </td><td> Sample05 </td></tr><tr height="20"><td height="20" style="height:20px;">条形码</td><td> CTGCAAGTTC </td><td> Sample06 </td></tr><tr height="20"><td height="20" style="height:20px;">条形码</td><td> TTCGTGATTC </td><td> Sample07 </td></tr><tr height="20"><td height="20" style="height:20px;">条形码</td><td> TTCCGATAAC </td><td> Sample08 </td></tr><tr height="20"><td height="20" style="height:20px;">条形码</td><td> TGAGCGGAAC </td><td> Sample09 </td></tr><tr height="20"><td height="20" style="height:20px;">条形码</td><td> CTGACCGAAC </td><td> Sample10 </td></tr></tbody></table> 表1。实施"寡"的文件中Mothur使用 。 <table border="0" cellpadding="0" cellspacing="0" width="660"><colgroup><col /><col /><col /><col /></colgroup><tbody><tr height="20"><td height="20" style="height:20px;width:363px;"></td><td style="width:67px;"> ＃的读取</td><td style="width:123px;"> ％上一步</td><td style="width:107px;">平均。每个样品</td></tr><tr height="20"><td height="20" style="height:20px;width:363px;">井数</td><td align="center"> 1262519 </td><td> - </td><td align="center"> 35070 </td></tr><tr height="20"><td height="20" style="height:20px;width:363px;">井珠</td><td align="center"> 1114108 </td><td align="center"> 88.20％ </td><td align="center"> 30947 </td></tr><tr height="20"><td height="20" style="height:20px;width:363px;">珠模板</td><td align="center"> 1112746 </td><td align="center"> 99.90％ </td><td align="center"> 30910 </td></tr><tr height="20"><td height="20" style="height:20px;width:363px;">单克隆珠</td><td align="center"> 826805 </td><td align="center"> 74.30％ </td><td align="center"> 22967 </td></tr><tr height="20"><td height="20" style="height:20px;width:363px;">质量好读（输出的音序） </td><td align="center"> 782204 </td><td align="center"> 94.60％ </td><td align="center"> 21728 </td></tr><tr height="40"><td height="40" style="height:40px;width:363px;">通Mothur过滤器（分平均20质量分数超过50个基点的窗口，最小150个基点的长度） </td><td align="center"> 372168 </td><td align="center"> 47.60％ </td><td align="center"> 10,338 </td></tr><tr height="40"><td height="40" style="height:40px;width:363px;">在GreenGenes的动物门级分类（50％置信度阈值） </td><td align="center"> 342171 </td><td align="center"> 91。90％ </td><td align="center"> 9505 </td></tr><tr height="40"><td height="40" style="height:40px;width:363px;">在GreenGenes家庭一级分类（50％置信度阈值） </td><td align="center"> 316512 </td><td align="center"> 92.50％ </td><td align="center"> 8792 </td></tr><tr height="40"><td height="40" style="height:40px;width:363px;">在GreenGenes属级分类（50％置信度阈值） </td><td align="center"> 289899 </td><td align="center"> 91.60％ </td><td align="center"> 8053 </td></tr></tbody></table> 读取从一个典型的奔跑为36环境样品复用在一个离子洪流314芯片产生的表2编号。

Watch this Scientific Journal Video about Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons at JoVE.com

Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons

Characterizing microbial community has been a longstanding goal in environmental microbiology. Next-generation sequencing methods now allow for the characterization of microbial communities at an unprecedented depth with minimal cost and labor. We detail here our approach to sequence bacterial 16S ribosomal RNA genes using a benchtop sequencer.

的16S核糖体RNA基因的扩增子的新一代测序

One of the major questions in microbial ecology is &#8220;who is there?&#8221; This question can be answered using various tools, but one of the ...

next-generation-sequencing-of-16s-ribosomal-rna-gene-amplicons

Nanyang Technological University

Research

JoVE Journal

Biology

83.3K 次观看.  National Research Council Canada. Characterizing microbial community has been a longstanding goal in environmental microbiology. Next-generation sequencing methods now allow for the characterization of microbial communities at an unprecedented depth with minimal cost and labor. We detail here our approach to sequence bacterial 16S ribosomal RNA genes using a benchtop sequencer. 

视频: Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons

Metagenomic Analysis of Silage

Metagenomics is defined as the direct analysis of deoxyribonucleic acid (DNA) purified from environmental samples and enables taxonomic identification of the microbial communities present within them. Two main metagenomic approaches exist; sequencing the 16S rRNA gene coding region, which exhibits sufficient variation between taxa for identification, and shotgun sequencing, in which genomes of the organisms that are present in the sample are analyzed and ascribed to "operational taxonomic units"; species, genera or families depending on the extent of sequencing coverage. 
In this study, shotgun sequencing was used to analyze the microbial community present in cattle silage and, coupled with a range of bioinformatics tools to quality check and filter the DNA sequence reads, perform taxonomic classification of the microbial populations present within the sampled silage, and achieve functional annotation of the sequences. These methods were employed to identify potentially harmful bacteria that existed within the silage, an indication of silage spoilage. If spoiled silage is not remediated, then upon ingestion it could be potentially fatal to the livestock.

Metagenomics was used to investigate the microbiome of silage cattle feed. Analysis was performed by shotgun sequencing. This approach was used to characterize the composition of the microbial community within the cattle feed.

青贮的宏基因组学分析

Metagenomics is defined as the direct analysis of deoxyribonucleic acid (DNA) purified from environmental samples and enables taxonomic identification of ...

科研

遗传学

Simultaneous DNA-RNA Extraction from Coastal Sediments and Quantification of 16S rRNA Genes and Transcripts by Real-time PCR

Real Time Polymerase Chain Reaction also known as quantitative PCR (q-PCR) is a widely used tool in microbial ecology to quantify gene abundances of taxonomic and functional groups in environmental samples. Used in combination with a reverse transcriptase reaction (RT-q-PCR), it can also be employed to quantify gene transcripts. q-PCR makes use of highly sensitive fluorescent detection chemistries that allow quantification of PCR amplicons during the exponential phase of the reaction. Therefore, the biases associated with &#39;end-point&#39; PCR detected in the plateau phase of the PCR reaction are avoided. A protocol to quantify bacterial 16S rRNA genes and transcripts from coastal sediments via real-time PCR is provided. First, a method for the co-extraction of DNA and RNA from coastal sediments, including the additional steps required for the preparation of DNA-free RNA, is outlined. Second, a step-by-step guide for the quantification of 16S rRNA genes and transcripts from the extracted nucleic acids via q-PCR and RT-q-PCR is outlined. This includes details for the construction of DNA and RNA standard curves. Key considerations for the use of RT-q-PCR assays in microbial ecology are included.

A protocol to quantify bacterial 16S rRNA genes and transcripts from coastal sediments via real-time PCR is provided. The methodology includes the co-extraction of DNA and RNA; preparation of DNA-free RNA; and 16S rRNA gene and transcript quantification via RT-q-PCR, including standard curve construction.

从海岸沉积物和16S量化的rRNA基因和转录同时DNA-RNA提取采用Real-time PCR检测

Real Time Polymerase Chain Reaction also known as quantitative PCR (q-PCR) is a widely used tool in microbial ecology to quantify gene abundances of ...

环境科学

Exploring the Root Microbiome: Extracting Bacterial Community Data from the Soil, Rhizosphere, and Root Endosphere

The intimate interaction between plant host and associated microorganisms is crucial in determining plant fitness, and can foster improved tolerance to abiotic stresses and diseases. As the plant microbiome can be highly complex, low-cost, high-throughput methods such as amplicon-based sequencing of the 16S rRNA gene are often preferred for characterizing its microbial composition and diversity. However, the selection of appropriate methodology when conducting such experiments is critical for reducing biases that can make analysis and comparisons between samples and studies difficult. This protocol describes in detail a standardized methodology for the collection and extraction of DNA from soil, rhizosphere, and root samples. Additionally, we highlight a well-established 16S rRNA amplicon sequencing pipeline that allows for the exploration of the composition of bacterial communities in these samples, and can easily be adapted for other marker genes. This pipeline has been validated for a variety of plant species, including sorghum, maize, wheat, strawberry, and agave, and can help overcome issues associated with the contamination from plant organelles.

Here, we describe a protocol to obtain amplicon sequence data of soil, rhizosphere, and root endosphere microbiomes. This information can be used to investigate the composition and diversity of plant-associated microbial communities, and is suitable for the use with a wide range of plant species.

从土壤、根际和根 Endosphere 提取细菌群落数据的根菌群探析

The intimate interaction between plant host and associated microorganisms is crucial in determining plant fitness, and can foster improved tolerance to ...

RIBO-seq in Bacteria: a Sample Collection and Library Preparation Protocol for NGS Sequencing

The ribosome profiling technique (RIBO-seq) is currently the most effective tool for studying the process of protein synthesis in vivo. The advantage of this method, in comparison to other approaches, is its ability to monitor translation by precisely mapping the position and number of ribosomes on a mRNA transcript.
In this article, we describe the consecutive stages of sample collection and preparation for RIBO-seq method in bacteria, highlighting the details relevant to the planning and execution of the experiment.
Since the RIBO-seq relies on intact ribosomes and related mRNAs, the key step is rapid inhibition of translation and adequate disintegration of cells. Thus, we suggest filtration and flash-freezing in liquid nitrogen for cell harvesting with an optional pretreatment with chloramphenicol to arrest translation in bacteria. For the disintegration, we propose grinding frozen cells with mortar and pestle in the presence of aluminum oxide to mechanically disrupt the cell wall. In this protocol, sucrose cushion or a sucrose gradient ultracentrifugation for monosome purification is not required. Instead, mRNA separation using polyacrylamide gel electrophoresis (PAGE) followed by the ribosomal footprint excision (28-30 nt band) is applied and provides satisfactory results. This largely simplifies the method as well as reduces the time and equipment requirements for the procedure. For library preparation, we recommend using the commercially available small RNA kit for Illumina sequencing from New England Biolabs, following manufacturer&#39;s guidelines with some degree of optimization.
The resulting cDNA libraries present appropriate quantity and quality required for next generation sequencing (NGS). Sequencing of the libraries prepared according to the described protocol results in 2 to 10 mln uniquely mapped reads per sample providing sufficient data for comprehensive bioinformatic analysis. The protocol we present is quick and relatively easy and can be performed with standard laboratory equipment.

Here we describe the stages of sample collection and preparation for RIBO-seq in bacteria. Sequencing of the libraries prepared according to these guidelines results in sufficient data for comprehensive bioinformatic analysis. The protocol we present is simple, uses standard laboratory equipment and takes seven days from lysis to obtaining libraries.

细菌中的RIBO-seq:NGS测序样本收集和库准备协议

The ribosome profiling technique (RIBO-seq) is currently the most effective tool for studying the process of protein synthesis in vivo. The advantage of ...

生物化学

Identification of Rare Bacterial Pathogens by 16S rRNA Gene Sequencing and MALDI-TOF MS

There are a number of rare and, therefore, insufficiently described bacterial pathogens which are reported to cause severe infections especially in immunocompromised patients. In most cases only few data, mostly published as case reports, are available which investigate the role of such pathogens as an infectious agent. Therefore, in order to clarify the pathogenic character of such microorganisms, it is necessary to conduct epidemiologic studies which include large numbers of these bacteria. The methods used in such a surveillance study have to meet the following criteria: the identification of the strains has to be accurate according to the valid nomenclature, they should be easy to handle (robustness), economical in routine diagnostics and they have to generate comparable results among different laboratories. Generally, there are three strategies for identifying bacterial strains in a routine setting: 1) phenotypic identification characterizing the biochemical and metabolic properties of the bacteria, 2) molecular techniques such as 16S rRNA gene sequencing and 3) mass spectrometry as a novel proteome based approach. Since mass spectrometry and molecular approaches are the most promising tools for identifying a large variety of bacterial species, these two methods are described. Advances, limitations and potential problems when using these techniques are discussed.

Matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS) and molecular techniques (16S rRNA gene sequencing) permit the identification of rare bacterial pathogens in routine diagnostics. The goal of this protocol lies in the combination of both techniques which leads to more accurate and reliable data.

由16S rRNA基因测序和MALDI-TOF MS罕见的细菌病原鉴定

There are a number of rare and, therefore, insufficiently described bacterial pathogens which are reported to cause severe infections especially in ...

免疫与感染

Pyrosequencing for Microbial Identification and Characterization

Pyrosequencing is a versatile technique that facilitates microbial genome sequencing that can be used to identify bacterial species, discriminate bacterial strains and detect genetic mutations that confer resistance to anti-microbial agents. The advantages of pyrosequencing for microbiology applications include rapid and reliable high-throughput screening and accurate identification of microbes and microbial genome mutations. Pyrosequencing involves sequencing of DNA by synthesizing the complementary strand a single base at a time, while determining the specific nucleotide being incorporated during the synthesis reaction. The reaction occurs on immobilized single stranded template DNA where the four deoxyribonucleotides (dNTP) are added sequentially and the unincorporated dNTPs are enzymatically degraded before addition of the next dNTP to the synthesis reaction. Detection of the specific base incorporated into the template is monitored by generation of chemiluminescent signals. The order of dNTPs that produce the chemiluminescent signals determines the DNA sequence of the template. The real-time sequencing capability of pyrosequencing technology enables rapid microbial identification in a single assay. In addition, the pyrosequencing instrument, can analyze the full genetic diversity of anti-microbial drug resistance, including typing of SNPs, point mutations, insertions, and deletions, as well as quantification of multiple gene copies that may occur in some anti-microbial resistance patterns.

Pyrosequencing is a versatile technique that facilitates microbial genome sequencing that can be used to identify bacterial species, discriminate bacterial strains, and detect genetic mutations that confer resistance to anti-microbial agents. In this video, the procedure for microbial amplicon generation, amplicon pyrosequencing, and DNA sequence analysis will be demonstrated.

焦磷酸测序的微生物鉴定和表征

Pyrosequencing is a versatile technique that facilitates microbial genome sequencing that can be used to identify bacterial species, discriminate ...

生物学

Efficient Nucleic Acid Extraction and 16S rRNA Gene Sequencing for Bacterial Community Characterization

There is a growing appreciation for the role of microbial communities as critical modulators of human health and disease. High throughput sequencing technologies have allowed for the rapid and efficient characterization of bacterial communities using 16S rRNA gene sequencing from a variety of sources. Although readily available tools for 16S rRNA sequence analysis have standardized computational workflows, sample processing for DNA extraction remains a continued source of variability across studies. Here we describe an efficient, robust, and cost effective method for extracting nucleic acid from swabs. We also delineate downstream methods for 16S rRNA gene sequencing, including generation of sequencing libraries, data quality control, and sequence analysis. The workflow can accommodate multiple samples types, including stool and swabs collected from a variety of anatomical locations and host species. Additionally, recovered DNA and RNA can be separated and used for other applications, including whole genome sequencing or RNA-seq. The method described allows for a common processing approach for multiple sample types and accommodates downstream analysis of genomic, metagenomic and transcriptional information.

We describe an efficient, robust, and cost effective method for extracting nucleic acid from swabs for characterization of bacterial communities using 16S rRNA gene amplicon sequencing. The method allows for a common processing approach for multiple sample types and accommodates a number of downstream analytic processes.

高效的核酸提取和16S rRNA基因序列的细菌群落特征

There is a growing appreciation for the role of microbial communities as critical modulators of human health and disease. High throughput sequencing ...

Guided Protocol for Fecal Microbial Characterization by 16S rRNA-Amplicon Sequencing

The human intestinal microbiome plays a central role in protecting cells from injury, in processing energy and nutrients, and in promoting immunity. Deviations from what is considered a healthy microbiota composition (dysbiosis) may impair vital functions leading to pathologic conditions. Recent and ongoing research efforts have been directed toward the characterization of associations between microbial composition and human health and disease.
Advances in high-throughput sequencing technologies enable characterization of the gut microbial composition. These methods include 16S rRNA-amplicon sequencing and shotgun sequencing. 16S rRNA-amplicon sequencing is used to profile taxonomical composition, while shotgun sequencing provides additional information about gene predictions and functional annotation. An advantage in using a targeted sequencing method of the 16S rRNA gene variable region is its substantially lower cost compared to shotgun sequencing. Sequence differences in the 16S rRNA gene are used as a microbial fingerprint to identify and quantify different taxa within an individual sample.
Major international efforts have enlisted standards for 16S rRNA-amplicon sequencing. However, several studies report a common source of variation caused by batch effect. To minimize this effect, uniformed protocols for sample collection, processing, and sequencing must be implemented. This protocol proposes the integration of broadly used protocols starting from fecal sample collection to data analyses. This protocol includes a column-free, direct-PCR approach that enables simultaneous handling and DNA extraction of large numbers of fecal samples, along with PCR amplification of the V4 region. In addition, the protocol describes the analysis pipeline and provides a script using the latest version of QIIME (QIIME 2 version 2017.7.0 and DADA2). This step-by-step protocol is aimed to guide those interested in initiating the use of 16S rRNA-amplicon sequencing in a robust, reproductive, easy to use, detailed way.

This manuscript describes a detailed standardized protocol of high-throughput 16S rRNA-amplicon sequencing. The protocol introduces an integrated, uniformed, feasible, and inexpensive protocol starting from fecal sample collection through data analyses. This protocol enables analysis of large numbers of samples with rigorous standards and several controls.

16S rRNA-扩增子测序法对粪便微生物特性的指导协议

The human intestinal microbiome plays a central role in protecting cells from injury, in processing energy and nutrients, and in promoting immunity. ...

Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing

In recent decades, vector-borne diseases have re-emerged and expanded at alarming rates, causing considerable morbidity and mortality worldwide. Effective and widely available vaccines are lacking for a majority of these diseases, necessitating the development of novel disease mitigation strategies. To this end, a promising avenue of disease control involves targeting the vector microbiome, the community of microbes inhabiting the vector. The vector microbiome plays a pivotal role in pathogen dynamics, and manipulations of the microbiome have led to reduced vector abundance or pathogen transmission for a handful of vector-borne diseases. However, translating these findings into disease control applications requires a thorough understanding of vector microbial ecology, historically limited by insufficient technology in this field. The advent of next-generation sequencing approaches has enabled rapid, highly parallel sequencing of diverse microbial communities. Targeting the highly-conserved 16S rRNA gene has facilitated characterizations of microbes present within vectors under varying ecological and experimental conditions. This technique involves amplification of the 16S rRNA gene, sample barcoding via PCR, loading samples onto a flow cell for sequencing, and bioinformatics approaches to match sequence data with phylogenetic information. Species or genus-level identification for a high number of replicates can typically be achieved through this approach, thus circumventing challenges of low detection, resolution, and output from traditional culturing, microscopy, or histological staining techniques. Therefore, this method is well-suited for characterizing vector microbes under diverse conditions but cannot currently provide information on microbial function, location within the vector, or response to antibiotic treatment. Overall, 16S next-generation sequencing is a powerful technique for better understanding the identity and role of vector microbes in disease dynamics.

Here we present a next-generation sequencing protocol for 16S rRNA sequencing which enables identification and characterization of microbial communities within vectors. This method involves DNA extraction, amplification and barcoding of samples through PCR, sequencing on a flow-cell, and bioinformatics to match sequence data to phylogenetic information.

下一代 16S rRNA 扩增子测序的蜱菌群鉴定

In recent decades, vector-borne diseases have re-emerged and expanded at alarming rates, causing considerable morbidity and mortality worldwide. Effective ...

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

The human gut is colonized by trillions of bacteria that support physiologic functions such as food metabolism, energy harvesting, and regulation of the immune system. Perturbation of the healthy gut microbiome has been suggested to play a role in the development of inflammatory diseases, including multiple sclerosis (MS). Environmental and genetic factors can influence the composition of the microbiome; therefore, identification of microbial communities linked with a disease phenotype has become the first step towards defining the microbiome&#8217;s role in health and disease. Use of 16S rRNA metagenomic sequencing for profiling bacterial community has helped in advancing microbiome research. Despite its wide use, there is no uniform protocol for 16S rRNA-based taxonomic profiling analysis. Another limitation is the low resolution of taxonomic assignment due to technical difficulties such as smaller sequencing reads, as well as use of only forward (R1) reads in the final analysis due to low quality of reverse (R2) reads. There is need for a simplified method with high resolution to characterize bacterial diversity in a given biospecimen. Advancements in sequencing technology with the ability to sequence longer reads at high resolution have helped to overcome some of these challenges. Present sequencing technology combined with a publicly available metagenomic analysis pipeline such as R-based Divisive Amplicon Denoising Algorithm-2 (DADA2) has helped advance microbial profiling at high resolution, as DADA2 can assign sequence at the genus and species levels. Described here is a guide for performing bacterial profiling using two-step amplification of the V3-V4 region of the 16S rRNA gene, followed by analysis using freely available analysis tools (i.e., DADA2, Phyloseq, and METAGENassist). It is believed that this simple and complete workflow will serve as an excellent tool for researchers interested in performing microbiome profiling studies.

Described here is a simplified standard operating procedure for microbiome profiling using 16S rRNA metagenomic sequencing and analysis using freely available tools. This protocol will help researchers who are new to the microbiome field as well as those requiring updates on methods to achieve bacterial profiling at a higher resolution.

使用两步PCR和下一代16S rRNA基因测序进行微生物群分析

The human gut is colonized by trillions of bacteria that support physiologic functions such as food metabolism, energy harvesting, and regulation of the ...