Name: profiling the bacterial community of fermenting traminette grapes during wine production using metagenomic amplicon sequencing
Uploaded: 2023-12-01T14:50:00.000+00:00
Duration: 7 min 34 s
Description: Watch this Scientific Journal Video about Profiling the Bacterial Community of Fermenting Traminette Grapes during Wine Production using Metagenomic Amplicon Sequencing at JoVE.com

感谢阿巴拉契亚州立大学研究理事会（URC）赠款和CAPES印刷旅行奖学金的资助，这些奖学金支持粮农组织访问巴西圣保罗里贝朗普雷图的圣保罗大学。 这项研究部分由Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil （CAPES） - Finance Code 001资助。ECPDM感谢支持她访问阿巴拉契亚州立大学的CAPES印刷旅行赠款。ECPDM 是巴西国家科学与技术委员会 （CNPq） 的研究员 2。

1. 实验性葡萄酒生产
<ol><li>从北卡罗来纳州琼斯维尔的 Dynamis Estate 葡萄酒中获取 Traminnette 葡萄，去梗并压碎以释放葡萄汁到两个独立的 600 升开顶发酵罐中，并在盖子上放置约 4 天。</li>
	<li>每天将瓶盖打孔一次，以保持皮肤湿润并限制挥发性酸 （VA） 的产生。 注意：这个想法是许多芳香前体（单萜烯）位于果皮中，并使果汁与果皮接触，以增加葡萄酒的芳香潜力。</li>
	<li>4 天后，投放酿酒 酵 母 var cerevisiae QA23 （Lallemand）。之所以选择这种酵母，是因为它是一种强大的发酵罐，并且与增强葡萄葡萄酒的品种特性有关。</li>
	<li>在一个罐Traminette R中，当白利糖度为17.7时加入20 g/hL Fermaid O，在13.1 g/hL时加入20 g/hL Fermaid O和12.5 g/hL Fermaid K，以实现发酵安全性，而另一个罐Traminette L，当白利糖度为17.1时，加入40 g/hL Stimula Sauvignon blanc。当白利糖度为13.4时，还可以添加20 g/hL Fermaid O，以增强葡萄酒的品种特性。</li>
	<li>取葡萄汁（第1天），添加酵母（第4天），发酵葡萄汁（第15天）和成品酒样品（第30天）的重复样品，并在DNA提取和宏基因组分析之前保持在-20°C。</li>
</ol>2. 宏基因组学的DNA提取
<ol><li>将上述获得的所有重复样品保存在冰上，直到第一次离心。</li>
	<li>将 20 mL 发酵样品转移到无菌的 50 mL 螺旋盖管中。</li>
	<li>加入 10 mL 冷水并匀浆（涡旋）。在4°C下以800× g 离心1分钟，并将上清液转移到新的50mL管中。 注意：此步骤将去除样品的固体。</li>
	<li>重复步骤2.3两次，并将上清液集中在同一管中。</li>
	<li>在4°C下以3,000× g 离心上清液20分钟以沉淀细胞。弃去上清液。</li>
	<li>将沉淀重悬于1mL PBS中，转移到2mL螺旋盖管中，并以14,000× g 离心2分钟。使用 PBS 再进行两次洗涤。</li>
	<li>在此步骤中，将颗粒冷冻在-20°C或按照步骤2.8进行操作。重要的是，所有样品都以相同的方式处理。</li>
	<li>加入 978 μL 磷酸钠缓冲液。制备磷酸钠缓冲液，加3.1gNaH2PO4·将H2O和10.9 g的Na2HPO4 （无水）蒸馏H2O制成体积为1 L，并调节pH至7.4。</li>
	<li>加入 122 μL DNA 缓冲液（DNA 离心试剂盒）并涡旋。</li>
	<li>让它在冰箱（4°C）中静置45分钟-1小时。每 15 分钟涡旋一次样品。此步骤可以放置过夜 （o/n） 或直到准备好继续使用 DNA 离心试剂盒。</li>
	<li>将 1 mL 样品转移到裂解液 1 试管（DNA 离心试剂盒）中并拧紧盖子（将样品编号写在试管侧面而不是盖子上;试剂盒试剂将去除盖子上写的任何内容）。</li>
	<li>在打珠研磨机（6.5 m/s，CY：24 x 2）中将样品均质化 60 秒三次。如果打珠研磨机有放冰的装置，请在管子下面放 50 克干冰。如果没有，则在每个均质步骤之间将样品放在湿冰上5分钟。</li>
	<li>以16800× g（或最大速度）离心裂解基质E管（DNA离心试剂盒）1分钟。</li>
	<li>将上清液转移到干净的微管中。然后，加入 250 μL 蛋白沉淀溶液 （PPS） 试剂（DNA 离心试剂盒），并用手摇动试管 10 次混合。</li>
	<li>以16800× g 离心5分钟以沉淀沉淀物。</li>
	<li>将上清液转移到无菌的 15 mL 管中。向上清液中加入 1 mL 结合基质悬浮液（DNA 离心试剂盒）。 注意：使用前重悬结合基质悬浮液，直至其均匀。</li>
	<li>用手将试管倒置2分钟，然后让试管在架子中静置3分钟（以允许二氧化硅基质沉降）。</li>
	<li>小心地取出 1 mL 上清液。将基质重悬于剩余的上清液中。</li>
	<li>将600μL混合物转移到离心过滤管（DNA离心试剂盒）中，并以14500× g离心1分钟。</li>
	<li>加入剩余的混合物并离心。</li>
	<li>倾析流通并将500μL洗涤液（DNA离心试剂盒）加入离心过滤管中，并以14500× g离心1分钟（确保将EtOH添加到洗涤液中;请参阅产品手册）。倒出流出物并重复洗涤两次（总共 3 次）。</li>
	<li>将流通液倾析并离心，以14,500× g 再离心2分钟，以干燥残留洗涤溶液的基质。</li>
	<li>取下离心过滤器并将其放入新鲜的捕集管（DNA 离心试剂盒）中。在室温（RT）下将离心过滤器风干5分钟。</li>
	<li>将不含DNAse的水（DNA离心试剂盒）在55°C加热5分钟。加入 50 μL 不含 DNAse 的水，并用移液管吸头轻轻搅拌过滤膜上的基质，以有效洗脱 DNA。让样品在室温下静置 1 分钟，然后以 14500 x g 离心 1 分钟以洗脱 DNA。</li>
	<li>将样品放在冰上。将样品混合物（2 μL 样品 + 2 μL 水 + 1 μL 上样缓冲液）上样到凝胶中。测量DNA浓度。</li>
	<li>将样品储存在-20°C。</li>
	<li>使用分光光度计量化提取的DNA。 注意：滴取 1 μL 体积的 DNA 并定量，并以 A260/A280 比例记录 DNA 的质量。</li>
</ol>3. DNA电泳
<ol><li>在 0.5 Tris/硼酸盐/EDTA （TBE） 缓冲液中制备 1% 琼脂糖凝胶（小型凝胶为 20 mL，中凝胶为 70-100 mL）。称取琼脂糖+缓冲液，在微波炉中煮沸以熔化琼脂糖，重新称取，并在原始重量中加入dH2O。将琼脂糖溶液冷却至55-60°C，然后用梳子组件倒入凝胶成型机中。让凝胶凝固。</li>
	<li>向样品 （10 μL） 中加入 1 μL 10x 凝胶上样缓冲液，并上样到分子量标记物旁边的凝胶上。在100-115V（大凝胶）或90V（迷你凝胶）下从负极（黑色）到正极（红色）运行，直到蓝色染料接近底部。</li>
	<li>在 1 μL/mL 溴化乙锭或 SYBR green（需要丁腈手套和护目镜）中染色凝胶 30 分钟，用水冲洗，并在紫外线 （UV） 光源下观察。从重复的样品中，选择最高产量进行进一步处理。</li>
</ol>4. 高通量测序
<ol><li>使用特异性引物 515F （5' -GTGYCAGCMGCCGCGGTAA-3'） 和 806R （5'-GGACTACNVGGGTWTCTAAT-3'） 扩增 16S rRNA 基因的 V4 高变区22。将聚合酶链反应（PCR）条件设置为94°C2分钟，94°C20秒，58°C20秒，65°C1分钟和65°C10分钟（最终延伸）的30个循环。</li>
	<li>使用高保真PCR预混液进行PCR反应（材料表）。</li>
	<li>使用 DNA 文库制备试剂盒生成文库，然后在测序平台上使用双端测序进行测序。 注意：在这里，使用NEBNext Ultra II DNA文库制备试剂盒生成的文库在novaseq6000平台上使用双端Illumina测序（2×250 bp）进行测序。</li>
	<li>按照排序步骤操作：<ol><li>第 1 步：用酶剪切 DNA，通常采用选择一般尺寸的方法。<ol><li>将 3.5 μL 测序缓冲液和 1.5 μL 酶混合物加入 25 μL DNA（约 1 μg）中，用移液管混合 10 次并快速旋转。</li>
			<li>放入具有以下条件的热循环仪中：盖子在75°C下预热，（1）30分钟20°C（2）30分钟65°C（3）在4°C下保持。</li>
		</ol></li>
		<li>第 2 步：通过连接添加接头<ol><li>用移液管加入 15 μL 连接预混液、1.25 μL 衔接液、0.5 μL 连接增强剂和 30 μL 终端制备 DNA 混合物。</li>
			<li>在20°C下在热循环仪中孵育15分钟，然后向连接混合物中加入1.5μL尿嘧啶特异性切除试剂（USER）酶，得到总48.26μL。 孵育37°C15分钟。</li>
			<li>将 43.5 μL 磁珠加入接头连接 DNA 中，用移液管混合 20 次，并在室温下孵育 5 分钟。 用磁架分离磁珠，然后孵育 5-10 分钟，弃去上清液。</li>
			<li>用 100 μL 80% 乙醇洗涤沉淀，干燥 30 秒，然后再次洗涤。将微珠和沉淀放入 8.5 μL 10 mM Tris HCl/0.1x Tris-EDTA（TE）/HPLCH 20 中，与移液管混合并在室温下孵育 2 分钟。</li>
			<li>在磁性支架中孵育并除去 7.5 μL 洗脱的 DNA 作为上清液。将洗脱液放入新管中并进行PCR步骤。</li>
			<li>首先，将 12.5 μL 预混液、2.5 μL 索引引物 i7 引物和 2.5 μL 通用 PCR 引物/i5 引物加入 7.5 μL 接头连接的 DNA 中，得到 25 μL PCR 反应混合物。使用以下PCR条件：将盖子预热至103°C，98°C30秒，98°C10秒，65°C75秒，65°C5分钟，并在10个循环后保持在4°C。</li>
			<li>清洁 25 μL 扩增的 DNA。加入 22.5 μL 磁珠并混合 20 次。在室温下孵育 5 分钟并除去 50 μL 上清液。用 100 μL 80% 乙醇洗涤珠子，并通过轻轻干燥珠子 30 秒除去所有乙醇。</li>
			<li>将深棕色珠子重悬于16μL水中，用移液管混合20次，然后放入磁性架中30-60秒。将 15 μL 转移到新试管中。</li>
			<li>使用荧光计测定DNA浓度。混合 90 μL 缓冲液 + 1 μL 染料 + 2 μL DNA 文库样品，并读取浓度。将 DNA 稀释至 2 nM，将 27-30 μL 上样至流通池，然后将其放入测序仪中。</li>
		</ol></li>
		<li>第 3 步：对接头进行测序，将上面生成的文库杂交到图案化流通池的基质中，并按照制造商的说明捕获 DNA。将其用作第二次合成的模板。<ol><li>然后，将第二条链扩增成克隆簇。线性化群集并阻止活动站点。添加测序引物，以根据制造商的方案在序列中合成，为测序提供一个位点。 注意：在化学上，修饰的核苷酸使用天然互补性与DNA模板链结合。每个核苷酸都有一个荧光标签和一个可逆终止子，可阻止下一个碱基的加入。因此，荧光信号的存在表明插入了哪个核苷酸，并且终止子被切割以结合下一个碱基。</li>
		</ol></li>
		<li>第 4 步：扩增单结合链，得到通过合成串联测序的序列簇。 注意：通过双向扫描和双通道测序化学反应，簇发出比单链更亮的信号。测序软件自动将碱基调用 （*.cbcl） 文件传输到指定的输出文件夹位置进行数据分析。</li>
	</ol></li>
</ol>5. 生物信息学
<ol><li>将序列数据生成为 FASTQ 文件。分析以包括以下部分。<ol><li>第一部分：<ol><li>保存从音序器获取的 FASTQ 文件，单击以打开电子表格文件，并创建映射/元数据文件。</li>
			<li>打开 Nephele 网站 （https://nephele.niaid.nih.gov/），上传 FASTQ 文件，读取 QIIME222 中的 QC、过滤和修剪。将原始序列作为序列读取存档 （SRA） 和 GenBank 存入 NCBI BioProject （https://submit.ncbi.nlm.nih.gov/subs/bioproject/）</li>
		</ol></li>
		<li>第二部分：转到 Nephele 网站 （https://nephele.niaid.nih.gov/），执行解复用的双端读取、滤波器替换和嵌合体错误，并使用 DADA223 进行合并。使用在 Silva 版本 132 99% OTU 数据库上训练的朴素贝叶斯分类器以 97% 的相似度执行细菌分类分配24。</li>
		<li>第三部分：打开MicrobiomeDB网站，点击上传文件，生成OTU α-多样性、β-多样性、相对丰度和稀有度曲线（https://microbiomedb.org/mbio/app）。</li>
		<li>第 IV 部分：打开电子表格并传输数据以制作热图、维恩图和线性判别分析效应大小 （https://microbiomedb.org/mbio/app）。</li>
	</ol></li>
</ol>6. 统计分析
<ol><li>打开 QIIME222，并使用 alpha-group-significance 脚本确定组间 alpha 多样性的显著差异。这也将执行 Kruskal-Wallis 测试。</li>
	<li>使用 PERMANOVA 确定组间 β 多样性的差异，包括成对检验25。</li>
	<li>使用MicrobiomeDB获得各组之间细菌群落结构的显着差异。p 值 ≤0.05 被认为具有统计学意义。</li>
</ol>

使用 Amplicon 宏基因组学分析葡萄中的细菌群落

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J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Grapevine+microbiomics:+bacterial+diversity+on+grape+leaves+and+berries+revealed+by+high-throughput+sequence+analysis+of+16S+rRNA+amplicons.">Grapevine microbiomics: bacterial diversity on grape leaves and berries revealed by high-throughput sequence analysis of 16S rRNA amplicons.</a> International Symposium on Biological Control of Postharvest Diseases: Challenges and Opportunities. 905, 31-42 (2010).</li><li>Rubiola, S., Macori, G., Civera, T., Fanning, S., Mitchell, M., Chiesa, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparison+between+full-length+16s+RNA+metabarcoding+and+whole+metagenome+sequencing+suggests+the+use+of+either+is+suitable+for+large-scale+microbiome+studies.">Comparison between full-length 16s RNA metabarcoding and whole metagenome sequencing suggests the use of either is suitable for large-scale microbiome studies.</a> Foodborne Pathogens and Disease. 19, 495-504 (2022).</li><li>Bokulich, N. A., 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=Associations+among+wine+grape+microbiome,+metabolome,+and+fermentation+behavior+suggest+microbial+contribution+to+regional+wine+characteristics.">Associations among wine grape microbiome, metabolome, and fermentation behavior suggest microbial contribution to regional wine characteristics.</a> Mbio. 7 (3), e00631-e00716 (2016).</li><li>Siren, K., 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=Multi-omics+and+potential+applications+in+wine+production.">Multi-omics and potential applications in wine production.</a> Current Opinion in Biotechnology. 56, 172-178 (2019).</li><li>Kidd, S. P., Bastian, S. E. P., Eisenhofer, R., Welsh, B. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Monitoring+the+viable+grapevine+microbiome+to+enhance+the+quality+of+wild+wines.">Monitoring the viable grapevine microbiome to enhance the quality of wild wines.</a> Microbiology Australia. 44 (1), 13-17 (2023).</li><li>Lorenz, P., Eck, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Metagenomics+and+industrial+applications.">Metagenomics and industrial applications.</a> Nature Reviews Microbiology. 3, 510-516 (2005).</li><li>Tosi, E., Azzolini, M., Guzzo, F., Zapparoli, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evidence+of+different+fermentation+behaviours+of+two+indigenous+strains+of+Saccharomyces+cerevisiae+and+Saccharomyces+uvarum+isolated+from+Amarone+wine.">Evidence of different fermentation behaviours of two indigenous strains of Saccharomyces cerevisiae and Saccharomyces uvarum isolated from Amarone wine.</a> Journal of Applied Microbiology. 107 (1), 210-218 (2009).</li><li>Stefanini, I., Cavalier, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Metagenomic+approaches+to+investigate+the+contribution+of+the+vineyard+environment+to+the+quality+of+wine+fermentation:+potentials+and+difficulties.">Metagenomic approaches to investigate the contribution of the vineyard environment to the quality of wine fermentation: potentials and difficulties.</a> Frontiers in Microbiology. 9, 991 (2018).</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=QIIME+allows+analysis+of+high-throughput+community+sequencing+data.">QIIME allows analysis of high-throughput community sequencing data.</a> Nature Methods. 5, 335-336 (2010).</li><li>Callahan, B. J., McMurdie, P. J., Rosen, M. J., Han, A. W., Johnson, A. J. A., Holmes, S. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=DADA2:+High-resolution+sample+inference+from+Illumina+amplicon+data.">DADA2: High-resolution sample inference from Illumina amplicon data.</a> Nature Methods. 13, 581-583 (2016).</li><li>Quast, 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=The+SILVA+ribosomal+RNA+gene+database+project:+improved+data+processing+and+web-based+tools.">The SILVA ribosomal RNA gene database project: improved data processing and web-based tools.</a> Nucleic Acids Research. 41 (Database issue), D590-D596 (2013).</li><li>Anderson, M. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+new+method+for+non-parametric+multivariate+analysis+of+variance.">A new method for non-parametric multivariate analysis of variance.</a> Austral Ecology. 26 (1), 32-46 (2001).</li><li>Bolyen, 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=interactive,+scalable+and+extensible+microbiome+data+science+using+QIIME+2.">interactive, scalable and extensible microbiome data science using QIIME 2.</a> Nature Biotechnology. 37, 852-857 (2019).</li><li>Fadiji, A. E., Babalola, O. O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Metagenomics+methods+for+the+study+of+plant-associated+microbial+communities:+A+review.">Metagenomics methods for the study of plant-associated microbial communities: A review.</a> Journal of Microbiological Methods. 170, 105860 (2020).</li><li>van Hijum, S. A., Vaughan, E. E., Vogel, R. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Application+of+state-of-art+sequencing+technologies+to+indigenous+food+fermentations.">Application of state-of-art sequencing technologies to indigenous food fermentations.</a> Current Opinion in Biotechnology. 24, 178-186 (2013).</li><li>V&#237;quez-R, L., Fleischer, R., Wilhelm, K., Tschapka, M., Sommer, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Jumping+the+green+wall:+The+use+of+PNA-DNA+clamps+to+enhance+microbiome+sampling+depth+in+wildlife+microbiome+research.">Jumping the green wall: The use of PNA-DNA clamps to enhance microbiome sampling depth in wildlife microbiome research.</a> Ecology and Evolution. 10 (20), 11779-11786 (2020).</li><li>Bukin, Y. S., Galachyants, P., Yu, I. V., Morozov, S. V., Bukin, A. S., Zakharenko, T. I., Zemskaya, <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+effect+of+16S+rRNA+region+choice+on+bacterial+community+metabarcoding+results.">The effect of 16S rRNA region choice on bacterial community metabarcoding results.</a> Scientific Data. 6, 190007 (2019).</li></ol>

作者没有利益冲突需要声明。

宏基因组学的方案从葡萄汁的取样开始，当酵母被添加到葡萄汁中时，发酵的葡萄酒和最终的葡萄酒样品。随后从这些样本中成功提取了重复的DNA。获得的量浓度从15 ng/μL到87 ng/μL不等。这表明DNA提取方案对葡萄酒的宏基因组研究是有效的。尽管 A260/A280 的 DNA 质量各不相同，但这可能归因于可能影响提取的不同参数，例如酒精浓度和发酵过程中和发酵后产生的其他发酵代谢物，以及其他可能抑制...

Traminette葡萄的特点是生产出卓越的葡萄酒质量，此外还有可观的产量和对几种真菌感染的部分抵抗力1,2,3。葡萄的自然发酵依赖于相关的微生物、葡萄酒生产环境和发酵容器4,5。通常，许多酿酒厂依靠野生酵母和细菌进行发酵、酒精生产、酯类、香气和风味开发6.
本研究的目的是检查葡萄的细菌组成并监测其在发酵过程中的动态。虽然，现代使用酿酒酵母等发酵酵母进行初级发酵，生产酒精，在不同的葡萄酒风格中很常见 7.此外，二次发酵，其中苹果酸被Oenococcus oeni脱羧为乳酸，改善了葡萄酒的感官和味道特征，并降低了葡萄酒的酸度8,9。随着使用不依赖培养的方法的最新进展，现在可以确定与酿酒葡萄相关的不同微生物，以及转移到葡萄汁并在不同时间参与发酵的物种，直到最终产品10。
在葡萄酒发酵过程中，来自不同葡萄的野生细菌转移到葡萄汁中的作用和动力学知之甚少。其中许多细菌的分类学甚至未知，或者它们的表型特性尚未表征。这使得它们在共培养发酵中的应用仍然没有得到充分利用。然而，基于微生物培养的分析已被用于确定与葡萄和葡萄酒相关的细菌种群10。众所周知，选择性培养铺板繁琐，容易污染，重现性低，产量可能令人怀疑;它还遗漏了生长要求未知的细菌物种。先前的研究表明，基于培养的 16S rRNA 基因方法为表征复杂的微生物群落提供了一种更可靠和更具成本效益的方法11。例如，对 16S rRNA 基因的高变区域进行测序已成功用于研究葡萄叶、浆果和葡萄酒中的细菌 12,13,14。研究表明，使用 16S rRNA 宏条形码或全宏基因组测序适用于微生物组研究15。关于细菌多样性与其在葡萄酒生产过程中的代谢属性可能存在联系的信息不断涌现，这可能有助于确定酿酒特性和风土16。
人们强调需要最大限度地利用使用下一代测序 （NGS） 的宏基因组工具的优势来研究葡萄和葡萄酒微生物生态学16,17。此外，使用基于高通量测序的培养非依赖性方法来分析食品和发酵生态系统的微生物多样性对许多实验室来说已经变得非常相关和有价值，并被推荐用于工业用途18,19。它为检测和分类学分析现有微生物种群以及环境微生物的贡献、它们的相对丰度以及 α 和 β 多样性提供了优势 20。16S区可变区的测序已成为重要的选择基因，并已用于不同的微生物生态学研究。
虽然许多研究都集中在葡萄酒发酵过程中的真菌，尤其是酵母21，但本研究报告了用于研究葡萄酒生产中 Traminette 葡萄发酵过程中细菌的 16S 扩增子测序和生物信息学工具。

<table><tbody><tr><td>Agarose gel&amp;nbsp;</td><td>Promega, Madison, WI USA</td><td>V3121</td><td>Electrophoresis&amp;nbsp;</td></tr><tr><td>FastPrep DNA spinKit for soil&amp;nbsp;</td><td>MP Biomedicals, Solon, OH USA</td><td>116560-200</td><td>DNA extraction&amp;nbsp;</td></tr><tr><td>FastQC software</td><td>Babraham Institute, United Kingdom</td><td></td><td>Bioinformatics&amp;nbsp;</td></tr><tr><td>Fermaid O</td><td>Scott Laboratory, Petaluma, CA USA&amp;nbsp;</td><td></td><td>Fermentation&amp;nbsp;</td></tr><tr><td>High-Fidelity PCR Master Mix&amp;nbsp;</td><td>New England Biolabs, USA</td><td>F630S</td><td>Polymerase chain reaction for sequencing&amp;nbsp;</td></tr><tr><td>NEBNext Ultra</td><td>New England Biolabs, USA</td><td>NEB #E7103</td><td>DNA Library Prep</td></tr><tr><td>NEBNext Ultra II DNA Library Prep Kit&amp;nbsp;</td><td>Illumina, San Diego, CA&amp;nbsp; USA</td><td></td><td>DNA sequencing&amp;nbsp;</td></tr><tr><td>NovaSeq Control Software (NVCS)</td><td>Illumina, San Diego, CA&amp;nbsp; USA</td><td></td><td>DNA sequencing&amp;nbsp;</td></tr><tr><td>Novaseq6000 platform&amp;nbsp;</td><td>Illumina, San Diego, CA&amp;nbsp; USA</td><td></td><td>DNA sequencing&amp;nbsp;</td></tr><tr><td>QuiBit</td><td>Thermoscientific, Waltham, MA, USA</td><td></td><td>DNA quantification&amp;nbsp;</td></tr><tr><td>Quickdrop spectrophotometer&amp;nbsp;</td><td>Molecular device, San Jose, CA, USA</td><td></td><td>DNA quantification&amp;nbsp;</td></tr><tr><td>Sodium Phosphate&amp;nbsp;</td><td>Sigma Aldrich</td><td>342483</td><td>DNA extraction buffer</td></tr><tr><td>Stimula Sauvignon Blanc</td><td>Scott Laboratory, Petaluma, CA USA&amp;nbsp;</td><td></td><td>Fermentation&amp;nbsp;</td></tr></tbody></table>

profiling the bacterial community of fermenting traminette grapes during wine production using metagenomic amplicon sequencing

测序技术的进步以及使用生物信息学工具分析微生物群落结构的相对容易获得，有助于更好地了解葡萄和葡萄酒中可培养和不可培养的微生物。在工业发酵过程中，已知和未知的微生物通常负责产品开发和异味。因此，分析从葡萄到葡萄酒的细菌可以很容易地理解原位微生物动力学。在这项研究中，Traminette葡萄的细菌必须经过发酵，最终的葡萄酒经过DNA提取，产生15 ng/μL至87 ng/μL。对V4区高变区的16S扩增子进行测序，相对丰富的细菌由变形菌门、放线菌门、厚壁菌门、拟杆菌门、梭杆菌门组成，其次是疣微生物群、盐杆菌门、脱硫杆菌门、粘菌门和酸杆菌门。对共享的独特操作分类单元 （OTU） 的维恩图分析显示，葡萄汁、发酵阶段和最终葡萄酒共有 15 个细菌门。使用16S扩增子测序检测了以前未报道的门，以及肠杆菌科和乳杆菌科等属。测试了葡萄酒中有机营养素利用的变化及其对细菌的影响;含有 Fermaid O 和 Traminette L 的 Traminette R 罐用 Stimula Sauvignon blanc + Fermaid O 刺激。 使用 Kruskal-Wallis 检验确定均匀度的 Alpha 多样性。β多样性表明两种处理的细菌在发酵阶段发生了变化，最终的葡萄酒细菌看起来相似。该研究证实，16S扩增子测序可用于监测葡萄酒生产过程中的细菌变化，以支持葡萄酒生产过程中葡萄细菌的质量和更好的利用。

首先测定葡萄葡萄汁、发酵酒和最终葡萄酒中提取的DNA的数量和质量;数量值范围为15-87 ng/μL（表 1）。
测序和生物信息学 Illumina高通量测序仪生成了一个FASTQ文件，该文件被导入到Nephele，并在QIIME 2平台26上查看。首先，使用FastQC软件检查序列质量;然后，在5'和3'端对其进行修剪以消除劣质核苷酸，去噪，合并和耗尽嵌...

Watch this Scientific Journal Video about Profiling the Bacterial Community of Fermenting Traminette Grapes during Wine Production using Metagenomic Amplicon Sequencing at JoVE.com

作者聚焦：通过下一代测序探索发酵微生物组

在这里，我们提出了一种描述扩增子宏基因组的方案，用于确定Traminette葡萄，发酵葡萄和最终葡萄酒的细菌群落。

使用宏基因组扩增子测序分析葡萄酒生产过程中发酵Traminette葡萄的细菌群落

Advances in sequencing technology and the relatively easy access to the use of bioinformatics tools to profile microbial community structures have ...

研究范围侧重于对发酵微生物组的理解。这是为了回答诸如自然发酵中的微生物的多样性，以及代谢物的哪些基因编码对于风味和安全性是必要的等问题。我们领域的最新进展之一是了解微生物组的动态如何与发酵食品和饮料中观察到的代谢特征相关联。下一代序列作为一项关键技术脱颖而出，目前正在推动我们研究领域的进步

profiling-bacterial-community-fermenting-traminette-grapes-during

Universidad de Cartagena UDEC

Research

JoVE Journal

Biology

Profiling the Bacterial Community of Fermenting Traminette Grapes during Wine Production using Metagenomic Amplicon Sequencing

626 次观看.  Appalachian State University. 研究范围侧重于对发酵微生物组的理解。这是为了回答诸如自然发酵中的微生物的多样性，以及代谢物的哪些基因编码对于风味和安全性是必要的等问题。我们领域的最新进展之一是了解微生物组的动态如何与发酵食品和饮料中观察到的代谢特征相关联。下一代序列作为一项关键技术脱颖而出，目前正在推动我们研究领域的进步

视频: 作者聚焦：通过下一代测序探索发酵微生物组

The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics

Terroir refers to the combination of environmental factors that affect the characteristics of crops such as grapevine (Vitis vinifera) according to particular habitats and management practices. This article shows how certain terroir signatures can be detected in the berry metabolome and transcriptome of the grapevine cultivar Corvina using multivariate statistical analysis. The method first requires an appropriate sampling plan. In this case study, a specific clone of the Corvina cultivar was selected to minimize genetic differences, and samples were collected from seven vineyards representing three different macro-zones during three different growing seasons. An untargeted LC-MS metabolomics approach is recommended due to its high sensitivity, accompanied by efficient data processing using MZmine software and a metabolite identification strategy based on fragmentation tree analysis. Comprehensive transcriptome analysis can be achieved using microarrays containing probes covering ~99% of all predicted grapevine genes, allowing the simultaneous analysis of all differentially expressed genes in the context of different terroirs. Finally, multivariate data analysis based on projection methods can be used to overcome the strong vintage-specific effect, allowing the metabolomics and transcriptomics data to be integrated and analyzed in detail to identify informative correlations.

This article describes the application of untargeted metabolomics, transcriptomics and multivariate statistical analysis to grape berry transcripts and metabolites in order to gain insight into the terroir concept, i.e., the impact of the environment on berry quality traits.

风土通过概念葡萄果实代谢组学和转录组解释的

Terroir refers to the combination of environmental factors that affect the characteristics of crops such as grapevine (Vitis vinifera) according to ...

科研

遗传学

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 ...

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 ...

Evaluating the Impact of Hydraulic Fracturing on Streams using Microbial Molecular Signatures

Hydraulic fracturing (HF), commonly called &#34;fracking&#34;, uses a mixture of high-pressure water, sand, and chemicals to fracture rocks, releasing oil and gas. This process revolutionized the U.S. energy industry, as it gives access to resources that were previously unobtainable and now produces two-thirds of the total natural gas in the United States. Although fracking has had a positive impact on the U.S. economy, several studies have highlighted its detrimental environmental effects. Of particular concern is the effect of fracking on headwater streams, which are especially important due to their disproportionately large impact on the health of the entire watershed. The bacteria within those streams can be used as indicators of stream health, as the bacteria present and their abundance in a disturbed stream would be expected to differ from those in an otherwise comparable but undisturbed stream. Therefore, this protocol aims to use the bacterial community to determine if streams have been impacted by fracking. To this end, sediment, and water samples, from streams near fracking (potentially impacted) and upstream or in a different watershed of fracking activity (unimpacted) must be collected. Those samples are then subjected to nucleic acid extraction, library preparation, and sequencing to investigate microbial community composition. Correlational analysis and machine learning models can subsequently be employed to identify which features are explanative of variation in the community, as well as identification of predictive biomarkers for fracking&#39;s impact. These methods can reveal a variety of differences in the microbial communities among headwater streams, based on the proximity to fracking, and serve as a foundation for future investigations on the environmental impact of fracking activities.

Here, we present a protocol to investigate the impacts of hydraulic fracturing on nearby streams by analyzing their water and sediment microbial communities.

使用微生物分子特征评估水力压裂对溪流的影响

Hydraulic fracturing (HF), commonly called &#34;fracking&#34;, uses a mixture of high-pressure water, sand, and chemicals to fracture rocks, releasing oil ...

环境科学

Workflow Based on the Combination of Isotopic Tracer Experiments to Investigate Microbial Metabolism of Multiple Nutrient Sources

Studies in the field of microbiology rely on the implementation of a wide range of methodologies. In particular, the development of appropriate methods substantially contributes to providing extensive knowledge of the metabolism of microorganisms growing in chemically defined media containing unique nitrogen and carbon sources. In contrast, the management through metabolism of multiple nutrient sources, despite their broad presence in natural or industrial environments, remains virtually unexplored. This situation is mainly due to the lack of suitable methodologies, which hinders investigations.
We report an experimental strategy to quantitatively and comprehensively explore how metabolism operates when a nutrient is provided as a mixture of different molecules, i.e., a complex resource. Here, we describe its application for assessing the partitioning of multiple nitrogen sources through the yeast metabolic network. The workflow combines information obtained during stable isotope tracer experiments using selected 13C- or 15N-labeled substrates. It first consists of parallel and reproducible fermentations in the same medium, which includes a mixture of N-containing molecules; however,a selected nitrogen source is labeled each time. A combination of analytical procedures (HPLC, GC-MS) is implemented to assess the labeling patterns of targeted compounds and to quantify the consumption and recovery of substrates in other metabolites. An integrated analysis of the complete dataset provides an overview of the fate of consumed substrates within cells. This approach requires an accurate protocol for the collection of samples&#8211;facilitated by a robot-assisted system for online monitoring of fermentations&#8211;and the achievement of numerous time-consuming analyses. Despite these constraints, it allowed understanding, for the first time, the partitioning of multiple nitrogen sources throughout the yeast metabolic network. We elucidated the redistribution of nitrogen from more abundant sources toward other N-compounds and determined the metabolic origins of volatile molecules and proteinogenic amino acids.

This protocol describes an experimental procedure to quantitatively and comprehensively investigate the metabolism of multiple nutrient sources. This workflow, based on a combination of isotopic tracer experiments and an analytical procedure, allows the fate of consumed nutrients and the metabolic origin of molecules synthetized by microorganisms to be determined.

基于同位素示踪实验结合的工作流研究多营养源的微生物代谢

Studies in the field of microbiology rely on the implementation of a wide range of methodologies. In particular, the development of appropriate methods ...

生物工程

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 ...

Comprehensive Workflow for the Genome-wide Identification and Expression Meta-analysis of the ATL E3 Ubiquitin Ligase Gene Family in Grapevine

Classification and nomenclature of genes in a family can significantly contribute to the description of the diversity of encoded proteins and to the prediction of family functions based on several features, such as the presence of sequence motifs or of particular sites for post-translational modification and the expression profile of family members in different conditions. This work describes a detailed protocol for gene family characterization. Here, the procedure is applied to the characterization of the Arabidopsis T&#243;xicos in Levadura (ATL) E3 ubiquitin ligase family in grapevine. The methods include the genome-wide identification of family members, the characterization of gene localization, structure, and duplication, the analysis of conserved protein motifs, the prediction of protein localization and phosphorylation sites as well as gene expression profiling across the family in different datasets. Such procedure, which could be extended to further analyses depending on experimental purposes, could be applied to any gene family in any plant species for which genomic data are available, and it provides valuable information to identify interesting candidates for functional studies, giving insights into the molecular mechanisms of plant adaptation to their environment.

This article describes the procedure for the identification and characterization of a gene family in grapevine applied to the family of Arabidopsis T&#243;xicos in Levadura (ATL) E3 ubiquitin ligases.

葡萄全基因组鉴定与表达的综合工作流 E3 泛素连接基因家族的 Meta 分析

Classification and nomenclature of genes in a family can significantly contribute to the description of the diversity of encoded proteins and to the ...

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. ...

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 ...