这项工作的一部分是由核心项目 (赠款 41/11)、以色列科学基金会 (908/15 号赠款) 和欧洲克罗恩病组织 (ECCO) 支持。

该研究的伦理批准是由示巴地方研究伦理学委员会批准的, 所有方法都是按照相关的指导方针和条例进行的。该议定书收到了当地道德审查委员会的病人同意例外, 因为使用的粪便材料已经作为临床检查的一部分提交给微生物学核心, 而没有其他人的年龄以外的可识别的病人信息,性别和微生物结果。从健康志愿者那里获得书面、知情的同意, 机构审查委员会批准了这项研究。其中一些示例已包含在以前的分析1中。
1. 样品处理
<ol><li>收集一个大约5毫米2涂片 (大致大小的铅笔橡皮擦) 从一个新鲜的粪便样本与无菌拭子在试管 (见材料表)。将含有粪便样品的所有拭子贮存在24小时内-80 摄氏度。粪便拭子可以留在那里, 直到进一步处理。</li>
</ol>2. DNA 提取
<ol><li>在室温下解冻萃取和稀释液 (请参阅材料表)。</li>
	<li>将粪便拭子转移到空的2毫升收集管 (请参阅材料表)。通过使用干净的剪刀来调整拭子棒的尺寸, 以使管道闭合时具有最小的交叉污染。加入 250 ul 的萃取液, 每个收集管含有粪便拭子和漩涡混合。</li>
	<li>在沸腾的水浴中加热样品10分钟 (95–100°c).添加 250 ul 稀释溶液的每个样品和涡流混合。</li>
	<li>储存2毫升的管含有提取的 DNA 和拭子在4摄氏度。</li>
</ol>3. PCR 和图书馆准备
对于步骤3.1 和 3.2, 在提供干净、模板和扩增子自由环境的 PCR 工作站中工作。
<ol><li>根据其条码标记底漆 (表 1)。将双蒸馏水 (DDW) 中的每一底漆稀释至50微米浓度, 并贮存在-20 摄氏度。</li>
	<li>用96井板进行 PCR 反应。每个盘子可以包含32不同的样品, 由32个不同的索引引物标记。在室温下解冻前底漆和32反向底漆, 稀释至5微米。</li>
	<li>准备 pcr 反应混合100反应 (每个井的最终体积将是 20 ul) 混合 100 ul 的5微米前底漆, 1 毫升 2X PCR 主混合, 和 400 ul DDW。把这个 PCR 混合在每个井 (总共96口井) 15 ul。将每5微米反向索引底漆的 1 ul 添加到3个不同的井 (triplicates 中32种不同的引物, 总共提供96个井)。</li>
	<li>在预 PCR 专用区, 这意味着一个干净的长凳, 是模板和扩增子免费, 添加 4 ul 的每一个提取的 dna 样本到反应混合物 (每个提取的 dna 样本被放大32个样本每 96-井板)。</li>
	<li>运行 PCR 与以下设置: 初始变性94°c 3 分钟;其次35周期变性在94°c 为1分钟, 退火在55°c 为1分钟和延长在72°c 为1分钟;和最后的延伸在72°c 为10分钟。</li>
	<li>在一个不同的长凳上, 这将被定义为后 pcr 专用区, 结合每三个 pcr 反应成一个单一的容积管 (60 ul 每样品)。</li>
	<li>为了评估 pcr amplicons 的质量, 在溴溴染色的1% 琼脂糖凝胶上, 对每个联合 pcr 反应运行 4 ul。在紫外波长为 260 nm 的情况下, 正 amplicons 将出现在预期的 375–425 bp 波段大小。只有这些 amplicons 将包含在后续步骤中。 注: 每样样品都有三份放大, 这意味着每个样品在3种不同的 PCR 反应中被放大。不要放大。</li>
</ol>4. 图书馆量化和清洁
<ol><li>为了获得所有 PCR 样品的摩尔浓度池, 用双链 DNA (dsDNA 定量试剂) 荧光灯核酸染色 (参见材料表) 量化每个扩增子, 适合于同时量化大型样本量。 注: 由于扩增子的尺寸范围是模棱两可的, 毫微克的实际用量用于加载摩尔浓度。</li>
	<li>将每个样品的 500 ng 组合成一个单一的无菌管。漩涡混合。</li>
	<li>在一个溴溴染色的1% 琼脂糖凝胶上运行 200 ul 的汇集图书馆。根据制造商的说明 (请参阅材料表) 并洗脱 80 ul DDW 中的汇集库, 从凝胶中提取 375–425 bp 带。 注: 池库应选择大小, 以减少来自宿主 DNA 的非特定放大产品。</li>
	<li>根据制造商的说明, 使用高度敏感的 dsDNA 检测套件测量最终库浓度 (请参阅材料表)。根据制造商的说明, 使用对 DNA 库的高度敏感的分离和分析工具包测量准确的库大小 (请参阅材料表)。</li>
</ol>5. 测序
<ol><li>根据顺序机协议, 将池库稀释到下午7点, 并添加20% 控制库 (参见材料表)。</li>
	<li>对于排序, 使用自定义的 V4 放大引物免费的读引物 (请参见表 1)。</li>
	<li>根据制造商的规范, 使用第三个自定义的测序底漆, 在附加周期中读取条形码 (请参见表 1), 并在每个方向上生成175个基的配对端读取。</li>
	<li>根据制造商的协议, 运行顺序机并获取 FASTQ 文件。</li>
</ol>6. 数据处理
<ol><li>在 QIIME 2 版 2017.7. 017中实现的数据精选管线中, 缝合并处理重叠的配对端 FASTQ 文件。根据示例特定的条形码解读取。</li>
	<li>使用 DADA218进行质量控制和序列变体 (SVs) 检测。将读取从 3 ' 端和15个基点从 5 ' 端截断13个基元, 丢弃超过2个预期错误的读取。使用协商一致方法识别和删除合体-合体分别在样本中检测到, 并且在样本的足够部分中发现嵌合体的序列被删除。</li>
	<li>使用朴素的贝叶斯拟合分类器执行 SVs 分类分类, 在 2013年8月99% 标识 Greengenes 数据库6上进行培训, 用于175长时间读取和正/反向底漆集。</li>
	<li>Rarefy 所有样本的深度为2146个序列。</li>
	<li>在稀薄的样品上使用加权 UniFrac 测量β多样性 (样品多样性19,20), 以避免样本大小的影响。</li>
	<li>使用所得到的距离矩阵来执行主坐标分析 (PCoA)。 注意: 数据处理脚本和映射文件作为补充材料提供 (辅助材料 1和2)。</li>
</ol>

<ol><li>Braun, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Fecal+microbial+characterization+of+hospitalized+patients+with+suspected+infectious+diarrhea+shows+significant+dysbiosis%5BTitle%5D)+AND+%22Scientific+Reports%22%5BJournal%5D)">Fecal microbial characterization of hospitalized patients with suspected infectious diarrhea shows significant dysbiosis.</a> Scientific Reports. 7, 1088(2017).</li>
<li>De Filippo, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Impact+of+diet+in+shaping+gut+microbiota+revealed+by+a+comparative+study+in+children+from+Europe+and+rural+Africa%5BTitle%5D)+AND+%22Proceedings+of+the+National+Academy+of+Sciences+of+the+United+States+of+America%22%5BJournal%5D)">Impact of diet in shaping gut microbiota revealed by a comparative study in children from Europe and rural Africa.</a> Proceedings of the National Academy of Sciences of the United States of America. 107, 14691-14696 (2010).</li>
<li>Yatsunenko, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Human+gut+microbiome+viewed+across+age+and+geography%5BTitle%5D)+AND+%22Nature%22%5BJournal%5D)">Human gut microbiome viewed across age and geography.</a> Nature. 486, 222-227 (2012).</li>
<li>Rodriguez, J. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+composition+of+the+gut+microbiota+throughout+life%2C+with+an+emphasis+on+early+life%5BTitle%5D)+AND+%22Microbial+Ecology+in+Health+and+Disease%22%5BJournal%5D)">The composition of the gut microbiota throughout life, with an emphasis on early life.</a> Microbial Ecology in Health and Disease. 26, 26050(2015).</li>
<li>Turnbaugh, P. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((An+obesity-associated+gut+microbiome+with+increased+capacity+for+energy+harvest%5BTitle%5D)+AND+%22Nature%22%5BJournal%5D)">An obesity-associated gut microbiome with increased capacity for energy harvest.</a> Nature. 444, 1027-1031 (2006).</li>
<li>McDonald, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((An+improved+Greengenes+taxonomy+with+explicit+ranks+for+ecological+and+evolutionary+analyses+of+bacteria+and+archaea%5BTitle%5D)+AND+%22The+ISME+Journal%22%5BJournal%5D)">An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea.</a> The ISME Journal. 6, 610-618 (2012).</li>
<li>Bakken, J. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Treating+Clostridium+difficile+infection+with+fecal+microbiota+transplantation%5BTitle%5D)+AND+%22Clinical+Gastroenterology+and+Hepatology%22%5BJournal%5D)">Treating Clostridium difficile infection with fecal microbiota transplantation.</a> Clinical Gastroenterology and Hepatology. 9, 1044-1049 (2011).</li>
<li>Khoruts, A., Dicksved, J., Jansson, J. K., Sadowsky, M. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Changes+in+the+composition+of+the+human+fecal+microbiome+after+bacteriotherapy+for+recurrent+Clostridium+difficile-associated+diarrhea%5BTitle%5D)+AND+%22Journal+of+Clinical+Gastroenterology%22%5BJournal%5D)">Changes in the composition of the human fecal microbiome after bacteriotherapy for recurrent Clostridium difficile-associated diarrhea.</a> Journal of Clinical Gastroenterology. 44, 354-360 (2010).</li>
<li>Nood, E. V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Duodenal+infusion+of+donor+feces+for+recurrent+Clostridium+difficile%5BTitle%5D)+AND+%22The+New+England+Journal+of+Medicine%22%5BJournal%5D)">Duodenal infusion of donor feces for recurrent Clostridium difficile.</a> The New England Journal of Medicine. 368, 407-415 (2013).</li>
<li>Koplan, J. P., Benfari Ferraro, M. J., Fineberg, H. V., Rosenberg, M. L. <a target="_blank" href="https://www.ncbi.nlm.nih.gov/pubmed/6105529">Value of stool cultures.</a> The Lancet. 316, 413-416 (1980).</li>
<li>Platts-Mills, J. A., Liu, J., Houpt, E. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((New+concepts+in+diagnostics+for+infectious+diarrhea%5BTitle%5D)+AND+%22Mucosal+Immunology%22%5BJournal%5D)">New concepts in diagnostics for infectious diarrhea.</a> Mucosal Immunology. 6, 876-885 (2013).</li>
<li>Bokulich, N. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Quality-filtering+vastly+improves+diversity+estimates+from+Illumina+amplicon+sequencing%5BTitle%5D)+AND+%22Nature+Methods%22%5BJournal%5D)">Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing.</a> Nature Methods. 10, 57-59 (2013).</li>
<li>Leek, J. T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Tackling+the+widespread+and+critical+impact+of+batch+effects+in+high-throughput+data%5BTitle%5D)+AND+%22Nature+Reviews+Genetics%22%5BJournal%5D)">Tackling the widespread and critical impact of batch effects in high-throughput data.</a> Nature Reviews Genetics. 11, (2010).</li>
<li>Dubilier, N., McFall-Ngai, M., Zhao, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Create+a+global+microbiome+effort%5BTitle%5D)+AND+%22Nature%22%5BJournal%5D)">Create a global microbiome effort.</a> Nature. 526, 631-634 (2015).</li>
<li>Caporaso, J. G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Global+patterns+of+16S+rRNA+diversity+at+a+depth+of+millions+of+sequences+per+sample%5BTitle%5D)+AND+%22PNAS%22%5BJournal%5D)">Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample.</a> PNAS. 108, 4516-4522 (2011).</li>
<li>Flores, G. E., Henley, J. B., Fierer, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+direct+PCR+approach+to+accelerate+analyses+of+human-associated+microbial+communities%5BTitle%5D)+AND+%22PloS+One%22%5BJournal%5D)">A direct PCR approach to accelerate analyses of human-associated microbial communities.</a> PloS One. 7, (2012).</li>
<li>Caporaso, J. G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((QIIME+allows+analysis+of+high-throughput+community+sequencing+data%5BTitle%5D)+AND+%22Nature+Methods%22%5BJournal%5D)">QIIME allows analysis of high-throughput community sequencing data.</a> Nature Methods. 7, 335-336 (2010).</li>
<li>Callahan, B. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((DADA2%3A+High-resolution+sample+inference+from+Illumina+amplicon+data%5BTitle%5D)+AND+%22Nature+Methods%22%5BJournal%5D)">DADA2: High-resolution sample inference from Illumina amplicon data.</a> Nature Methods. 13, 581-583 (2016).</li>
<li>Lozupone, C., Knight, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((UniFrac%3A+A+new+phylogenetic+method+for+comparing+microbial+communities%5BTitle%5D)+AND+%22Applied+and+Environmental+Microbiology%22%5BJournal%5D)">UniFrac: A new phylogenetic method for comparing microbial communities.</a> Applied and Environmental Microbiology. 71, 8228-8235 (2005).</li>
<li>Lozupone, C., Lladser, M. E., Knights, D., Stombaugh, J., Knight, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((UniFrac%3A+An+effective+distance+metric+for+microbial+community+comparison%5BTitle%5D)+AND+%22The+ISME+Journal%22%5BJournal%5D)">UniFrac: An effective distance metric for microbial community comparison.</a> The ISME Journal. 5, 169-172 (2011).</li>
<li>Srinivasan, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Use+of+16S+rRNA+gene+for+identification+of+a+broad+range+of+clinically+relevant+bacterial+pathogens%5BTitle%5D)+AND+%22PloS+One%22%5BJournal%5D)">Use of 16S rRNA gene for identification of a broad range of clinically relevant bacterial pathogens.</a> PloS One. 10, e0117617(2015).</li>
<li>Hiergeist, A., Gläsner, J., Reischl, U., Gessner, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Analyses+of+intestinal+microbiota%3A+culture+versus+sequencing%5BTitle%5D)+AND+%22ILAR+Journal%22%5BJournal%5D)">Analyses of intestinal microbiota: culture versus sequencing.</a> ILAR Journal. 56, 228-240 (2015).</li>
<li>Didelot, X., Bowden, R., Wilson, D. J., Peto, T. E. A., Crook, D. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Transforming+clinical+microbiology+with+bacterial+genome+sequencing%5BTitle%5D)+AND+%22Nature+Reviews+Genetics%22%5BJournal%5D)">Transforming clinical microbiology with bacterial genome sequencing.</a> Nature Reviews Genetics. 13, 601-612 (2012).</li>
<li>Salipante, S. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Rapid+16S+rRNA+next-generation+sequencing+of+polymicrobial+clinical+samples+for+diagnosis+of+complex+bacterial+infections%5BTitle%5D)+AND+%22PloS+One%22%5BJournal%5D)">Rapid 16S rRNA next-generation sequencing of polymicrobial clinical samples for diagnosis of complex bacterial infections.</a> PloS One. 8, e65226(2013).</li>
<li>Caporaso, J. G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Ultra-high-throughput+microbial+community+analysis+on+the+Illumina+HiSeq+and+MiSeq+platforms%5BTitle%5D)+AND+%22The+ISME+Journal%22%5BJournal%5D)">Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms.</a> The ISME Journal. 6, 1621-1624 (2012).</li>
</ol>

作者没有什么可透露的。

16S rRNA-扩增子和 metagenomics 猎枪测序在临床微生物学应用中得到了普及21, 2223.这些技术有利于提高捕获可培养和非可培养类群的能力, 提供有关致病接种的相对丰度的数据, 以及它们识别更精确的 polymicrobial 传染性的能力。指纹24。微生物研究领域的进展产生了大量的数据, 由于各种方法缺乏一致性, 因此难以比较。最近几年, 一些财团试图解决其中的一些问题。该议定书遵循类似的图书馆准备工作, 作为地球微生物群项目 (EMP)。然而, 添加的是一个详细的分步协议的 DNA 提取从粪便样品通过分析管道。
此前, 16S rRNA 测序已被用于描述健康非住院患者粪便样本的微生物组成模式, 以及疑似感染性腹泻的住院病人, 以及传统的文化结果。分析结果表明, 住院患者在 Proteobacteria 的分类中有很大的增加1。这里描述的是一个综合协议的广泛使用的方法 16S rRNA 基因扩增子排序使用直接 PCR 无柱方法, 最初设计用于提取植物DNA16。这种方法可以有效和快速地提取高质量的扩增 DNA 库, 针对 16S rRNA 基因的 V4 变量区域, 从大量适合高通量 16S rRNA 测序的样本。
由于这是一种基于 DNA 的测序方法, 因此获得了单个粪便样本中需氧生物和厌氧微生物群落的数据。该协议采用了最初设计为15的引物, 针对 16S rRNA 基因的 V4 区域。重要的是, 反向放大底漆包含十二个基本条码序列, 它支持在每个车道15中共用多达2167种不同的样本。正向放大底漆在适配器区域中包括九个额外的底座, 用于支持序列平台上的配对端排序 ( 表 1和辅助材料 3)。此方法启用了处理一个库, 由250个粪便样本组成, 在一条车道上排序, 其运行深度为 10622.57, 1211.34 (平均标准错误) 读取每个样本的成本为每样本30美元。
为确保质量控制, 建议使用以下负面控制;i) pcr 无 dna 模板, ii) pcr 的 dna 提取从清洁消毒拭子, 和 iii) pcr 的混合萃取和稀释溶液。至于积极的控制, 我们建议包括来自同一粪便样本的样本, 但通过不同的图书馆准备与不同的反向条形码底漆和样品从以往的运行作为内部质量控制。值得注意的是, 其他可选的积极控制是由 NIST 提供的微生物测量标准。相对于实际粪便样本 (表 1), 阴性对照组的平均读数覆盖率明显低于, 主要由支原体分类。我们进一步显示了使用样本所获得的测序结果的一致性, 它们分别来源于相同的粪便材料, 但经过了不同的库准备 (图 3)。这一结果还证实, 在使用我们的集成方法时, 样品之间没有交叉污染。
在本协议中, 我们引入了一个统一的、可行的协议来分析大量的样本。该议定书的目的是指导感兴趣的科学家开始使用 16S rRNA-扩增子测序的健壮, 生殖, 易于使用, 廉价, 详细的方式从粪便收集到数据分析, 使用重要的控制。使用此标准化的详细指导协议, 可以最小化批处理效果, 并允许在不同的实验室之间进行更可比较的排序结果。

为了更好地了解微生物的多样性和丰度, 已作出了集中努力, 作为在健康和病理条件下捕捉个体差异和相似性的另一个方面。年龄2,3, 地理4, 生活方式5,6, 疾病5被证明与肠道微生物组的组成有关, 但许多条件和人口尚未完全特点.最近据报道, 微生物菌群可以被修改为治疗性应用7,8,9。因此, 进一步了解各种生理条件和微生物组成之间的关系, 是迈向优化未来潜在的修改的第一步。
传统的微生物培养方法受低产量的限制10,11, 并被概念化为一个二进制状态, 其中细菌是存在于肠道或没有。高通量 DNA 测序已经彻底改变了微生物生态学, 使微生物群落的所有成员都能被捕获。但是, 序列读取长度和质量仍然是精确分类分配12的重要障碍。此外, 基于高通量的实验可能会受到批处理效果的影响, 其中测量受非生物或非科学变量13的影响。近年来, 已经建立了一些研究人类微生物菌群的方案, 包括美国肠道项目、美国 (美国) 人类微生物学项目和联合王国 (英国) MetaHIT 项目。这些倡议产生了大量的数据, 由于它们的做法缺乏连贯性, 因此难以比较。各种国际项目, 如国际人类微生物菌群联合会、国际人类微生物菌群标准项目以及国家标准和技术研究所 (NIST) 试图解决其中的一些问题14, 并制定了微生物菌群测量标准, 这将有助于实现可靠的生殖结果。这里描述的是一些广泛使用的方法15、16的集成协议, 用于 16S rRNA 高通量测序 (十六年代序列), 从粪便样本收集通过数据分析开始。该议定书描述了一种无柱 PCR 方法, 最初设计用于直接提取植物 DNA16, 以便在相对较短的时间内同时处理大量的粪便样本, 并以高质量的扩增 DNA 为靶向常规测序平台上微生物变量 V4 区域的测序。该议定书的目的是引导感兴趣的科学家开始使用 16S rRNA 扩增子测序, 在健壮, 生殖, 易于使用, 详细的方式, 使用重要的控制。具有引导和详细的分步协议可能会减少批处理效果, 从而允许在实验室之间进行更可比较的排序结果。

<table><tbody><tr><td>Primers</td><td>Integrated DNA Technologies (IDT)</td><td></td><td></td></tr><tr><td>Extraction solution</td><td>Sigma-Aldrich</td><td>E7526</td><td></td></tr><tr><td>Dilution solution</td><td>Sigma-Aldrich</td><td>D5688</td><td></td></tr><tr><td>Kapa HiFi HotStart ReadyMix PCR Kit</td><td>KAPABIOSYSTEMS</td><td>KK2601</td><td>PCR Master mix</td></tr><tr><td>Quant-iT PicoGreen dsDNA Reagent kit</td><td>Invitrogen</td><td>P7589</td><td>dsDNA quantify reagent</td></tr><tr><td>MinElute Gel extraction kit</td><td>Qiagen</td><td>28606</td><td></td></tr><tr><td>Agarose</td><td>Amresco</td><td>0710-250G</td><td></td></tr><tr><td>Ultra Pure Water Dnase and Rnase Free</td><td>Biological Industries</td><td>01-866-1A</td><td></td></tr><tr><td>Qubit dsDNA HS assay kit</td><td>Molecular probes</td><td>Q32854</td><td>dsDNA detecting kit</td></tr><tr><td>High Sensitivity D1000</td><td>Agilent Technologies</td><td>Screen Tape 5067-5582</td><td>separation and analysis</td></tr><tr><td>Screen Tape Assay</td><td>Agilent Technologies</td><td>Reagents 5067-5583</td><td>for DNA libraries</td></tr><tr><td>PhiX Control v3</td><td>Illumina</td><td>15017666</td><td>control library</td></tr><tr><td>MiSeq Reagent Kit v2 (500 cycle)</td><td>Illumina</td><td>MS-102-2003</td><td></td></tr><tr><td>Ethidium Bromide</td><td>Amresco</td><td>E406-10mL-TAM</td><td></td></tr><tr><td>2 mL collection tubes</td><td>SARSTEDT</td><td>72.695.400</td><td>Safe Seal collection tubes</td></tr><tr><td>Plastic stick swab in PP test tube</td><td>STERILE INTERIOR</td><td>23117</td><td></td></tr><tr><td>&lt;strong&gt;Name&lt;/strong&gt;</td><td>&lt;strong&gt;Company&lt;/strong&gt;</td><td>&lt;strong&gt;Catalog Number&lt;/strong&gt;</td><td>&lt;strong&gt;Comments&lt;/strong&gt;</td></tr><tr><td>&lt;strong&gt;Equipment&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>PCR Machine</td><td>Applied Biosystems</td><td>2720 Thermal Cycler</td><td></td></tr><tr><td>Sequncing Machine</td><td>Illumina</td><td>Miseq</td><td></td></tr><tr><td>PCR workstation</td><td>Biosan</td><td>UV-cleaner</td><td></td></tr><tr><td>scissors</td><td></td><td></td><td></td></tr><tr><td>vortexer</td><td>Scientific Industries</td><td>Vortex-Genie 2</td><td></td></tr></tbody></table>

guided protocol for fecal microbial characterization by 16s rrna-amplicon sequencing

人体肠道菌群在保护细胞免遭伤害、处理能量和养分以及促进免疫力方面起着重要作用。从被认为是健康的微生物组成 (dysbiosis) 的偏差可能会损害导致病理状况的重要功能。最近和正在进行的研究工作已被用于描述微生物组成与人类健康和疾病之间的关联。
高通量测序技术的进展使肠道微生物成分的特征得以表征。这些方法包括 16S rRNA 扩增子测序和猎枪测序。16S rRNA 扩增子测序用于分析分类成分, 而猎枪测序则提供有关基因预测和功能注释的附加信息。使用 16S rRNA 基因可变区的目标测序方法的优点是它的成本与猎枪测序相比大大降低。在 16S rRNA 基因中的序列差异被用作微生物指纹, 用来识别和量化个体样本中不同的分类群。
主要的国际努力已登记了 16S rRNA 扩增子测序标准。然而, 一些研究报告了一个常见的变异来源, 由间歇效应引起。为了最小化这种效果, 必须实现示例收集、处理和排序的统一协议。该协议提出了从粪便样本采集开始到数据分析的广泛应用协议的集成。该协议包括无列的直接 pcr 方法, 能够同时处理和 DNA 提取大量粪便样本, 以及 V4 区域的 PCR 扩增。此外, 该协议描述分析管道, 并提供一个脚本使用最新版本的 QIIME (QIIME 2 版 2017.7. 0 和 DADA2)。这一步步协议的目的是引导那些感兴趣的人开始使用 16S rRNA 扩增子测序的健壮, 繁殖, 易于使用, 详细的方式。

该协议的示意图说明显示在图 1中。
我们有前瞻性收集的粪便样本, 从住院病人疑似传染性腹泻。这些样本是在2月至2015年5月期间提交给示巴医疗中心的临床微生物学实验室的, 如前所述的1。粪便标本在临床微生物学实验室进行常规微生物培养, 并在十六年代平行的范围内进行广泛的高通量。此外, 健康成人的粪便样本进行了比较。
在此分析中, 重点是数据的质量控制, 并显示从 QIIME217获得的输出的代表性结果。首先, 回顾了6例阴性对照样品的测序结果, 包括: i. 无模板 pcr, ii.) 萃取稀释液中清洁无菌拭子的 pcr 方法及 iii.) 混合萃取和稀释的 pcr 技术解.表 2显示所有负控制样本都有≤118总读数 (平均29个序列), 主要来自支原体分类, 而所有其他样本分析显示平均总读数为 10622.57 (±1211.34 标准错误), 没有支原体读取通过了主要样品的质量控制。
十六个样本, 每起源于相同的粪便样本, 但通过不同的图书馆准备与不同的反向条形码底漆被用作积极的控制。这16个样本包括8个, 其中有阳性的区域性为弯曲杆菌、沙门氏菌或志贺菌, 临床微生物学实验室报告, 8 报告为有负文化。门级别的区域图显示在图 2中。来自同一粪便样本的样本, 但通过不同的图书馆准备不同的反向条形码引物显示非常一致的相对丰度 (在一级分类学相对丰度重复度之间的壁炉试验数值模拟 p 值 = 1 x 10-04基于9999复制)。
主坐标分析 (PCoA) 降低了输入数据的维数, 用于 UniFrac 矩阵直观地探索样本分离和相似性。在图 3A中, 来自同一原始粪便样本但经过不同库准备的序列样本颜色相同。的确, 那些位于相对地严密在 PCoA 剧情 (壁炉测试在重复 PC1 PC2 价值之间模拟 p 价值 = 1e-04 基于9999复制)。此外, 本研究中样本之间的平均加权 unifrac β多样性距离为 0.706, 而两个重复项之间的平均距离为 0.235 (魏氏秩和测试 p 值 = 4.205 x 10-11)。有趣的是, 对于弯曲杆菌、沙门氏菌或志贺氏菌enteropathogens 的区域性阳性样本, 与样品相比, PC1 值显著不同 (魏氏秩和测试 p 值 = 0.011)。消极文化结果。图 3B显示了相同的 PCoA 图, 但现在样本被 Proteobacteria 的相对丰度所着色。高 Proteobacteria 丰度的样品有显著不同的 PC1 值 (长矛相关 r =-0.533, p 值 = 0.000975)。这些结果与以前对这一数据的分析相一致, 在这里, 住院病人从 Proteobacteria1的分类中有了深刻的增加。在这里我们表明, PC1 与 Proteobacteria 丰度相关, 以文化正样本聚类为主的 PC1 轴的一侧和文化阴性样本聚类主要在另一边, 连同健康的样本。
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/56845/56845fig1.jpg"> 图 1: 从粪便样本到相对微生物丰度的流程图.1)示例处理: 左侧, 粪便样本在无菌拭子管中收集。正确的, 拭子棒尺寸通过切割来调整, 以使2ml 管的闭合和存储。2) dna 提取: 通过直接 PCR 无柱法提取 dna。3)库准备: 左侧, 提取的 DNA 的 4 ul 用正向/反向索引引物放大。每个反向底漆包含12基本条码。每个样品都有三份放大。一个96井板包含32不同的条形码。对, 将每一个条码三个组合成一个体积管。一个96井板包含96不同的条形码。4)库量化: 上板、阳性样品筛选、溴溴化染色1% 琼脂糖凝胶 amplicons 检测。下面板, 量化的积极 amplicons, 以达到摩尔浓度 500 ng 扩增子从每个样本到一个单一的图书馆池。5)库净化: 上部面板, 汇集库是凝胶提取的大小选择和纯化。下部面板, 测量库的精确大小和浓度。6)排序: 池库的高通量排序。7)对 16S rRNA-扩增子微生物生态学的生物信息学管线进行了测序数据分析。<a href="//cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56845/56845fig1large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/56845/56845fig2.jpg"> 图 2: 来自同一粪便样本的样本之间的一致性相对丰度, 但经过了不同的库准备.每样的分类相对丰度均按样品显示。相对丰度显示的16粪便样本, 从住院病人的疑似感染性腹泻, 每个通过不同的图书馆准备 (重复1和 2) 与不同的反向条形码引物和3健康控制。<a href="//cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56845/56845fig2large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/56845/56845fig3.jpg"> 图 3: 系统发育多样性显示了来自同一粪便样本的样本的一致结果, 但经过了不同的库准备.在 UniFrac 矩阵上, 利用主坐标分析 (PCoA) 降低输入数据的维数, 直观地探索样本分离和相似性。两个样本之间的距离代表了它们的微生物组成的差异。(A) 加权 UniFrac PCoA 图。每个点表示一个单序列采样。来自同一原始粪便样本的样本颜色相同。临床微生物学报告的住院病人阳性粪便培养样本1是用正方形标记的, 住院的患者与文化阴性在三角1, 并且健康成人从单独排序运行1标记为填充圆圈。(B) 与 A 中相同的 PCoA, 但根据所示, 现在样品的 Proteobacteria 相对丰度是有色的。<a href="//cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56845/56845fig3large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always"><tbody>
<tr>
<td>底漆名称</td>
			<td>底漆序列</td>
			<td>底漆说明</td>
		</tr>
<tr>
<td>前进底漆</td>
			<td>AATGATACGGCGACCACCGAGATCTACAC-TATGGTAATT-GT-GTGCCAGCMGCCGCGGTAA</td>
			<td>5 ' 适配器序列向前底漆垫向前底漆链接器向前底漆</td>
		</tr>
<tr>
<td>反向索引底漆</td>
			<td>CAAGCAGAAGACGGCATACGAGAT-XXXXXXXXXXXX-AGTCAGTCAG-CC-GACTACHVGGGTWTCTAAT</td>
			<td>3 ' 适配器序列的反向补充-Golay 条码-反向底漆垫-反向底漆链接器-反向底漆</td>
		</tr>
<tr>
<td>阅读1测序底漆</td>
			<td>TATGGTAATT-GT-GTGCCAGCMGCCGCGGTAA</td>
			<td>向前底漆垫向前底漆链接器向前底漆</td>
		</tr>
<tr>
<td>阅读2测序底漆</td>
			<td>AGTCAGTCAG-CC-GGACTACHVGGGTWTCTAAT</td>
			<td>反向底漆垫-反向底漆链接器-反向底漆</td>
		</tr>
<tr>
<td>索引序列底漆</td>
			<td>ATTAGAWACCCBDGTAGTCC-GG-CTGACTGACT</td>
			<td>反向底漆反向补体反向引物链接器反向补片反向底漆垫</td>
		</tr>
</tbody></table>表 1: 底漆清单。
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always"><tbody>
<tr>
<td>示例</td>
			<td>读帖子数 初始质量控制在 QIIME</td>
		</tr>
<tr>
<td>萃取稀释液中清洁拭子的 PCR 研究-1</td>
			<td>118</td>
		</tr>
<tr>
<td>萃取稀释液中清洁拭子的 PCR 研究-2</td>
			<td>39</td>
		</tr>
<tr>
<td>无模板的 PCR-1</td>
			<td>0</td>
		</tr>
<tr>
<td>无模板的 PCR-2</td>
			<td>0</td>
		</tr>
<tr>
<td>PCR 在萃取和稀释溶液混合物-1</td>
			<td>16</td>
		</tr>
<tr>
<td>PCR 在萃取和稀释溶液混合物-2</td>
			<td>0</td>
		</tr>
<tr>
<td>样品 (平均标准误差)</td>
			<td>10622.57 @ 1211.34</td>
		</tr>
</tbody></table>表 2: 阴性对照和粪便样本读取次数。
辅助材料 1: 数据处理脚本.<a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56845/56845_Suppl1_script.txt">请单击此处下载此文件.</a>
辅助材料 2: 映射文件.<a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56845/56845_Suppl2_map.txt">请单击此处下载此文件.</a>
补充材料 3: 底漆方案.<a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56845/56845_Suppl3_schematic_primers_representation.pptx">请单击此处下载此文件.</a>

Watch this Scientific Journal Video about Guided Protocol for Fecal Microbial Characterization by 16S rRNA-Amplicon Sequencing at JoVE.com

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

这篇手稿描述了一个详细的标准化协议高通量 16S rRNA 扩增子测序。该协议引入了一个集成的, 统一的, 可行的, 廉价的协议, 从粪便样本收集, 通过数据分析。该协议允许分析大量的样本, 严格的标准和几个控制。

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. ...

guided-protocol-for-fecal-microbial-characterization-16s-rrna

Nanyang Technological University

Research

JoVE Journal

Biology

19.6K 次观看.  Sheba Medical Center. 这篇手稿描述了一个详细的标准化协议高通量 16S rRNA 扩增子测序。该协议引入了一个集成的, 统一的, 可行的, 廉价的协议, 从粪便样本收集, 通过数据分析。该协议允许分析大量的样本, 严格的标准和几个控制。

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

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 ...

科研

遗传学

Isolation, Propagation, and Identification of Bacterial Species with Hydrocarbon Metabolizing Properties from Aquatic Habitats

Hydrocarbon pollutants are recalcitrant to degradation and their accumulation in the environment is toxic to all life forms. Bacteria encode numerous catalytic enzymes and are naturally capable of metabolizing hydrocarbons. Scientists harness biodiversity in aquatic ecosystems to isolate bacteria with biodegradation and bioremediation potential. Such isolates from the environment provide a rich set of metabolic pathways and enzymes, which can be further utilized to scale up the degradation process at an industrial scale. In this article, we outline the general process of isolation, propagation, and identification of bacterial species from aquatic habitats and screen their ability to utilize hydrocarbons as the sole carbon source in vitro using simple techniques. The present protocol describes the isolation of various bacterial species and their subsequent identification using the 16S rRNA analysis. The protocol also presents steps for characterizing the hydrocarbon degrading potential of bacterial isolates. This protocol will be useful for researchers trying to isolate bacterial species from environmental habitats for their biotechnological applications.

We present the process of isolating, propagating, and characterizing hydrocarbon-degrading bacteria from aquatic habitats. The protocol outlines bacterial isolation, identification by the 16S rRNA method, and testing of their hydrocarbon-degrading potential. This article would help researchers in characterizing microbial biodiversity in environmental samples, and specifically screen for microbes with bioremediation potential.

从水生栖息地分离、繁殖和鉴定具有碳氢化合物代谢特性的细菌种类

Hydrocarbon pollutants are recalcitrant to degradation and their accumulation in the environment is toxic to all life forms. Bacteria encode numerous ...

环境科学

Applying Advanced In Vitro Culturing Technology to Study the Human Gut Microbiota

The human gut microbiota plays a vital role in both human health and disease. Studying the gut microbiota using an in vivo model, is difficult due to its complex nature, and its diverse association with mammalian components. The goal of this protocol is to culture the gut microbiota in vitro, which allows for the study of the gut microbiota dynamics, without having to consider the contribution of the mammalian milieu. Using in vitro culturing technology, the physiological conditions of the gastro intestinal tract are simulated, including parameters such as pH, temperature, anaerobiosis, and transit time. The intestinal surface of the colon is simulated by adding mucin-coated carriers, creating a mucosal phase, and adding further dimension. The gut microbiota is introduced by inoculating with the human fecal material. Upon inoculation with this complex mixture of bacteria, specific microbes are enriched in the different longitudinal (ascending, transverse and descending colons) and transversal (luminal and mucosal) environments of the in vitro model. It is crucial to allow the system to reach a steady state, in which the community and the metabolites produced remain stable. The experimental results in this manuscript demonstrate how the inoculated gut microbiota community develops into a stable community over time. Once steady state is achieved, the system can be used to analyze bacterial interactions and community functions or to test the effects of any additives on the gut microbiota, such as food, food components, or pharmaceuticals.

Here, we present a protocol for culturing the gut microbiota of the colon in vitro, using a series of bioreactors that simulate the physiological conditions of the gastro intestinal tract.

应用先进的体外培养技术研究人肠道微生物群

The human gut microbiota plays a vital role in both human health and disease. Studying the gut microbiota using an in vivo model, is difficult due to its ...

生物工程

An In Vitro Batch-culture Model to Estimate the Effects of Interventional Regimens on Human Fecal Microbiota

The emerging role of the gut microbiome in several human diseases demands a breakthrough of new tools, techniques and technologies. Such improvements are needed to decipher the utilization of microbiome modulators for human health benefits. However, the large-scale screening and optimization of modulators to validate microbiome modulation and predict related health benefits may be practically difficult due to the need for large number of animals and/or human subjects. To this end, in vitro or ex vivo models can facilitate preliminary screening of microbiome modulators. Herein, it is optimized and demonstrated an ex vivo fecal microbiota culture system that can be used for examining the effects of various interventions of gut microbiome modulators including probiotics, prebiotics and other food ingredients, aside from nutraceuticals and drugs, on the diversity and composition of the human gut microbiota. Inulin, one of the most widely studied prebiotic compounds and microbiome modulators, is used as an example here to examine its effect on the healthy fecal microbiota composition and its metabolic activities, such as fecal pH and the fecal levels of organic acids including lactate and short-chain fatty acids (SCFAs). The protocol may be useful for studies aimed at estimating the effects of different interventions of modulators on fecal microbiota profiles and at predicting their health impacts.

This protocol describes an in vitro batch-culture fermentation system of human fecal microbiota, using inulin (a well-known prebiotic and one of the most widely studied microbiota modulators) to demonstrate the use of this system in estimating effects of specific interventions on fecal microbiota composition and metabolic activities.

评估介入疗法对人体粪便微生物群影响的体内批处理培养模型

The emerging role of the gut microbiome in several human diseases demands a breakthrough of new tools, techniques and technologies. Such improvements are ...

免疫与感染

Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons

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 long-lasting gold standards is to sequence 16S ribosomal RNA (rRNA) gene amplicons generated by domain-level PCR reactions amplifying from genomic DNA. Traditionally, this was performed by cloning and Sanger (capillary electrophoresis) sequencing of PCR amplicons. The advent of next-generation sequencing has tremendously simplified and increased the sequencing depth for 16S rRNA gene sequencing. The introduction of benchtop sequencers now allows small labs to perform their 16S rRNA sequencing in-house in a matter of days. Here, an approach for 16S rRNA gene amplicon sequencing using a benchtop next-generation sequencer is detailed. The environmental DNA is first amplified by PCR using primers that contain sequencing adapters and barcodes. They are then coupled to spherical particles via emulsion PCR. The particles are loaded on a disposable chip and the chip is inserted in the sequencing machine after which the sequencing is performed. The sequences are retrieved in fastq format, filtered and the barcodes are used to establish the sample membership of the reads. The filtered and binned reads are then further analyzed using publically available tools. An example analysis where the reads were classified with a taxonomy-finding algorithm within the software package Mothur is given. The method outlined here is simple, inexpensive and straightforward and should help smaller labs to take advantage from the ongoing genomic revolution.

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 ...

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 ...

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 ...

Fecal Microbiota Transplantation and Detection of Prevalence of IgA-Coated Bacteria in the Gut

Gut microbiota exert pleiotropic roles in human health and disease. Fecal microbiota transplantation (FMT) is an effective method to investigate the biological function of intestinal bacteria as a whole or at the species level. Several different FMT methods have been published. Here, we present an FMT protocol that successfully depletes gut microbiota in a matter of days, followed by transplantation of fecal microbiota from fresh or frozen donor intestinal contents to conventional mice. Real time-PCR is applied to test the efficacy of bacterial depletion. Sequencing of the 16S ribosomal RNA (rRNA) is then applied to test the relative abundance and identity of gut microbiota in recipient mice. We also present a flow cytometry-based detection method of immunoglobulin A (IgA)-coated bacteria in the gut.

<meta charset="utf8"></head><body>粪便微生物群移植和肠道中 IgA 包被细菌患病率的检测

We established an efficient way to deplete intestinal bacteria in three days, and subsequently transplant fecal microbiota via gavage of fecal fluid prepared from fresh or frozen intestinal contents in mice. We also present an optimized method to detect the frequency of IgA-coated bacteria in the gut.

粪便微生物群移植和肠道中 IgA 包被细菌患病率的检测

Gut microbiota exert pleiotropic roles in human health and disease. Fecal microbiota transplantation (FMT) is an effective method to investigate the ...

16S rRNA-扩增子测序法对粪便微生物特性的指导协议

摘要

探索更多视频

从土壤、根际和根 Endosphere 提取细菌群落数据的根菌群探析

从水生栖息地分离、繁殖和鉴定具有碳氢化合物代谢特性的细菌种类

应用先进的体外培养技术研究人肠道微生物群

评估介入疗法对人体粪便微生物群影响的体内批处理培养模型

的16S核糖体RNA基因的扩增子的新一代测序

高效的核酸提取和16S rRNA基因序列的细菌群落特征

下一代 16S rRNA 扩增子测序的蜱菌群鉴定

使用两步PCR和下一代16S rRNA基因测序进行微生物群分析

粪便微生物群移植和肠道中 IgA 包被细菌患病率的检测

粪便微生物群移植和肠道中 IgA 包被细菌患病率的检测