我们感谢在RIKEN的PSC和比约恩·Usadel博士和树齐藤教授有用讨论MPIMP的。 TT是从亚历山大·冯·洪堡基金会奖学金支持。

1。样品制备<ol><li>植物材料的收获，并立即冻结。 </li><li>冷冻材料厂搅拌机磨制成粉末（或砂浆）和猎鹰管或Eppendorf管中于-80°C。 </li></ol> 2。提取代谢物谱<ol><li>植物材料，在2毫升的Eppendorf管分装冷冻。 </li><li>添加5μL提取每毫克冷冻厂材料的鲜重的缓冲区。 </li><li>新增一个金属（或gilconia）的球，并融汇冻粉与调音台轧机在25 LS -1 2分钟。 </li><li>在12000转离心10分钟。 </li><li>转移上清NANOSEP离心式过滤器。 </li><li>在4000转速离心2分钟。 </li><li>将上清转移至新的Eppendorf管中，并储存在-20°C，直到使用。 </li></ol> 3。代谢物谱的LC-MS <ol><li>建立高效液相色谱法和检查柱箱温度和样品盘。 </li><li>成立MS条件和检查真空和加热毛细管的状态。 </li><li>执行的m / z MS检测校准。 </li><li> 50μL提取物转移到玻璃小瓶的LC-MS。 </li><li>注入5μL提取物的LC-MS。 </li></ol> 4。数据分析<ol><li>配置神剑传奇或Metalign 4，选择要处理的数据进行分析。 </li><li>准备检测到您的利益峰表，按照表I类化合物。 </li><li>识别标准化合物的共流出峰。 </li><li>注释使用MS 分析 ，文献调查，代谢物数据库搜索（ 图2），12,13检测峰。 </li></ol> 5。代谢途径的预测<ol><li>构建与检测的化合物可能的途径。预测使用高峰注解的途径，应根据检测到的化合物的化学结构S的 ​​预测连接酶的功能，代谢途径13。构建合成步骤应进行精确的峰值注释。但详细的化学结构的测定，例如糖基，是不可缺少的这一步，因为糖基和内收位置的预测是非常困难的，以确定由MS分析。酶法测定糖捐助的测定糖如hexoside和pentoside的类型将确定，在项目的最后一步。主要建设途径的预测，应为小分子较大分子的中间，除了在某些情况下，如脱水反应。原子分子量的名单，例如16米/ Z - H之间的差异和OH基（氧化），14米/ Z（碳原子）之间的-OH和-OMe的（甲基）差价和162 m / z为 （兆瓦H 2 O的己糖（糖基化）），是有用的预测。测定修改相关组织的具体分析的类型，有助于代谢途径的预测。一般代谢途径，如KEGG数据库（数据库<a href="http://www.genome.jp/kegg/" target="_blank">http://www.genome.jp/kegg/</a> ）PlantCyc（ <a href="http://plantcyc.org/" target="_blank">http://plantcyc.org/~~V</a> ），为您感兴趣的代谢途径的预测是非常有效的。 </li></ol> 6。基因名单的制备与拟南芥同源基因ID <ol><li>下载从基因组数据库，基因ID列表。 </li><li>添加同源基因的拟南芥基因ID，在你的目标工厂的情况下是不拟南芥。 </li><li>准备一个利益的途径中的基因名单。在TAIR网页（ <a href="http://www.arabidopsis.org/" target="_blank">http://www.arabidopsis.org/~~V</a> ），拟南芥的通路数据和基因家族的数据诠释。如果你准备拟南芥同源基因的清单，你可以随后组合NE他们。 </li></ol> 7。共表达基因分析<ol><li>测试使用搜索最佳通路的数据库，使用众所周知的基因对您的利益途径（ 表二 ）检查相关的准备基因ID列表。如果数据库或共同表达的基因表达数据库是不是你的兴趣在植物，植物共表达数据库，应使用与拟南芥的同源基因的清单。在大麦的情况下，可用于水稻，杨树，小麦，苜蓿，大豆，植物物种的共同表达分析（ 见表二 ）。 </li><li>基于利益的途径著名的基因连接，构建框架为目标的共同表达网络。 </li><li>新增相关的候选基因（R &lt;0.4〜0.90系数的途径利益知名的基因之间的连接价值的近似值，内），并检查他们在你的PR基因注释edicted家庭网络连接，寻找最佳的候选基因（ 图3）。系数阈值应根据网络结构和密度相关基因的协调。 </li><li>使您可以作为专门为你的目标的途径缩小的基因列表。 </li><li>检查你的候选基因的器官特异性和应激反应的基因表达。 </li></ol> 8。的所有信息整合到预测的新途径<ol><li>新增查询共表达分析已用于预测代谢途径的基因特征。 </li><li>检查这个途径在未知的部分，未知酶的步骤，例如运输蛋白和转录因子。 </li><li>预测这些失踪未知的步骤最合适的基因注释。 </li><li>结合GE 在硅片的代谢产物分析和候选基因的结果NE表达的基础上预测的途径。 </li><li>安排您的候选基因，根据基因功能的预测途径，例如，乙酰转移酶的代谢产物乙酰化，糖基苷，P450的氧化复合。氨基酸序列的系统发育树分析是有用的宽一些，例如P450和糖基转移酶的基因家族。 </li><li>检查的组织特异性应激反应或代谢产物的积累和候选基因的基因表达水平之间的一致性。 </li><li>检查提供基板和应激反应的基因连接到其他代谢。 </li></ol> 9。实验中使用的生物资源的基因鉴定<ol><li>检查的生物资源提供便利为候选基因鉴定实验。 </li><li>执行实验利用生物资源，如高突变体库和全长cDNA文库，基因功能鉴定。 ţ他的实验与编制过度植物及敲除突变体，酶的检测和启动子结合试验，基因功能鉴定，必须在你的预测最好的候选基因进行。蛋白质性质的表征和过度植物准备好要进行确认后代谢产物的文件，使用正突变体，因为它需要相当长的时间准备改造重组蛋白和基因的克隆，重组蛋白检测。 </li></ol> 10。代表结果在本协议中所述的综合分析的过程有很多可能性，取决于指定的实验目的和选择生物和分析组合。程序和实验设计的选择，应进行正确目标的途径，化合物和植物物种的基础上。在本协议中所述的一体化战略是FOC<html>用于植物基因功能和新基因功能的发现与一些生物和数据资源的有效使用注释。预期的结果是承诺提供与确凿预测的唯一情况。这一事实表明，如果有足够的证据不能组合型材，实验不应该被启动。出于这个原因，在任何情况下，可以支持额外的初步实验，RT-PCR技术，如针对性的基因表达谱，基因功能的预测。预测的准确性和正确性的相关性更高的质的区别和数量组合的变化而定。此外，良好的候选人和有效的结果，只能来自准确预测的途径。高峰应进行注释的几种方法的组合，例如文献调查，参考植物提取物，MS n分析，器官特异性和突变分析13。 1"SRC ="/ files/ftp_upload/3487/3487fig1.jpg"/&gt; 图1。实验相结合的方法，通过基因注释流程的概述。在某些情况下，项目开始发现这是在特殊条件下或组织发现一个新的高峰，和渴望了解在其新陈代谢作用。在其他情况下，该项目的目的是关键调控因子，如转录因子的基因鉴定或发现。实验设计应刨用一组数据显示了明显的差异，在你的目标途径的代谢水平，从不同器官的组织样本的广泛使用，接触应力条件差异生长的植物或植物，并受到材料代谢产物分析。突变和转基因植物以及QTL窝藏育种材料，也代表了适合这些研究的遗传材料。应认真进行准确预测新途径如metabolotype机关根据证券及应激反应的基因表达数据的途径利益的不同类型的峰值注释和相结合的办法。在最后一步，代谢物和成绩单分析应执行最终，当网络资源，在硅片分析和基因表达的特征， 通过异源表达在体外结合，导致确认的候选基因，并阐明其功能内的代谢途径中的地位。缩写:QTL定位，QTL定位。 <img alt="图2" src="/files/ftp_upload/3487/3487fig2.jpg" /> 图2。工作流高峰注释的组合方式。标准化合物的峰识别和注释，比较野生型和敲除突变体的过程，多维目标峰的质谱质谱纯COM从数据库12磅。缩写:DB，数据库;敲，敲了一维，一维，二维，二维核磁共振，核磁共振，红外光谱，红外多级质谱 ，质量质量spectrometries。 <img alt="图3" src="/files/ftp_upload/3487/3487fig3.jpg" /> 图3。例如合作的花青素途径调控网络分析的共表达分析进行了使用与总理（ <a href="http://prime.psc.riken.jp/?action=coexpression_index" target="_blank">http://prime.psc.riken.jp/?action=coexpression_index</a> ）设置ATTEDII版本的数据3 8,2的pajek计划（ <a href="http://vlado.fmf.uni-lj.si/pub/networks/pajek/" target="_blank">http://vlado.fmf.uni-lj.si/pub/networks/pajek/</a> ）。呈正相关（R &lt;0.5）被用来进行网络连接。红色节点:12花色苷酶基因（At5g13930，社区卫生服务，TT4，查尔酮合酶; At3g55120，C您好，TT5，查耳酮isomerise; At3g51240，F3H，TT6，黄烷酮3 -羟化酶; At5g07990，甘蓝型油菜F3&#39;H，TT7，类黄酮3&#39;-羟化酶; At5g17050，Fd3GT，UGT78D2，类黄酮3 - O型葡萄糖; At5g17220，AtGSTF12，TT19 ; At5g42800，DFR的二氢叶酸还原酶，TT3 At4g22880时，ANS / LDOX，TT18，花青素的合成; At4g14090 A5GT，花青素5 - O型葡萄糖; At5g54060，A3G2"XT的，公认的花青素3 - O-葡萄糖苷2" - O - xylosyltransferase; At3g29590，A5GMaT，花青素5 - O-葡萄糖苷6&#39;&#39;&#39; - O型 malonyltransferase; At1g03940，A3GCouT，花青素3 - O-葡萄糖苷6" - O - P-coumaroyltransferase）和两个花青素生产的转录因子（At1g56650， PAP1; At1g66390，PAP2）用于寻找候选基因的候选基因被发现一个"集合的交集"搜索与阈值的系数 r &lt;。/ EM &gt;&gt;质疑0.50套所有质疑的基因（十四花青素合成基因）的交点。共同表达网络，包括相关的候选基因（68个基因）和查询基因（14个基因），是由一个"套联网"重新构建与R&gt; 0.50总理数据库搜索。与"网"从总理的数据库和网络的文件格式的输出文件使用Pajek软件绘制。蓝色节点代表候选人花青素基因相关的基因。 <table border="1"><tbody><tr><td> 种类 </td><td> 主要次生代谢产物 </td></tr><tr><td> 拟南芥 </td><td>硫代葡萄糖甙，黄酮，花青素，sinapoyl的衍生</td></tr><tr><td> 杨毛果 </td><td>黄酮，花青素，水杨酸衍生物</td></tr><tr><td> 葡萄 </td><td>黄酮，花青素，单宁酸，苯乙烯</td></tr><tr><td> 马铃薯lycopersicum </td><td>黄酮，花青素，glycoalkaloid，chrologenate相关， </td></tr><tr><td> 烟草 </td><td>黄酮，花青素，nicotianamide，chrologenate相关acylsugar </td></tr><tr><td> 水稻 </td><td> glycoflavone，花色苷，甾醇衍生物</td></tr><tr><td> 玉米5月 </td><td> glycoflavone，花青素，benzoxazinone，甾醇衍生物</td></tr><tr><td> 苜蓿 </td><td>花青素，异黄酮，皂甙， </td></tr><tr><td> 莲花粳稻 </td><td>大豆异黄酮，花青素，黄酮，皂甙， </td></tr></tbody></table> 表一，主要模式植物的次生代谢产物。 <table border="1"><tbody><tr><td> 共表达数据库 </td><td> 地址 </td></tr><tr><td> 植物交叉规范IES </td><td></td></tr><tr><td>缔约方会议</td><td> <a href="http://webs2.kazusa.or.jp/kagiana/cop0911/" target="_blank">http://webs2.kazusa.or.jp/kagiana/cop0911/~~V</a> </td></tr><tr><td>沛</td><td> <a href="http://aranet.mpimp-golm.mpg.de/" target="_blank">http://aranet.mpimp-golm.mpg.de/</a> </td></tr><tr><td> 植物物种 </td><td></td></tr><tr><td> ATEED-II </td><td> <a href="http://atted.jp/" target="_blank">http://atted.jp/</a> </td></tr><tr><td>英美</td><td> <a href="http://142.150.214.117/welcome.htm" target="_blank">http://142.150.214.117/welcome.htm</a> </td></tr><tr><td>缔约方会议</td><td> <a href="http://webs2.kazusa.or.jp/kagiana/cop" target="_blank">http://webs2.kazusa.or.jp/kagiana/cop</a> </td></tr><tr><td> GeneCAT </td><td> <a href="http://genecat.mpg.de/" target="_blank">http://genecat.mpg.de/</a> </td></tr><tr><td> 拟南芥 </td><td></td></tr><tr><td>法</td><td> <a href="http://www.arabidopsis.leeds.ac.uk/act/coexpanalyser" target="_blank">http://www.arabidopsis.leeds.ac.uk/act/coexpanalyser</a> </td></tr><tr><td> AthCoR@CSB.DB </td><td> <a href="http://csbdb.mpimp-golm.mpg.de/csbdb/dbcor/ath.html" target="_blank">http://csbdb.mpimp-golm.mpg.de/csbdb/dbcor/ath.html</a> </td></tr><tr><td> CressExpress </td><td> <a href="http://cressexpress.org/" target="_blank">http://cressexpress.org/~~V</a> </td></tr><tr><td> PRIME </td><td> <a href="http://prime.psc.riken.jp/?action=coexpression_index" target="_blank">http://prime.psc.riken.jp/?action=coexpression_index</a> </td></tr><tr><td> 水稻 </td><td></td></tr><tr><td> RiceArrayNet </td><td> <a href="http://arraynet.mju.ac.kr/arraynet/" target="_blank">http://arraynet.mju.ac.kr/arraynet/~~V</a> </td></tr><tr><td>水稻阵列数据库</td><td> <a href="http://www.ricearray.org/coexpression/coexpression.shtml" target="_blank">http://www.ricearray.org/coexpression/coexpression.shtml</a> </td></tr></tbody></table> 表二。可用硅片共同表达分析基因表达的数据库。

<ol>
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R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Identification+of+genes+required+for+cellulose+synthesis+by+regression+analysis+of+public+microarray+data+sets.">Identification of genes required for cellulose synthesis by regression analysis of public microarray data sets.</a> Proceedings of the National Academy of Sciences of the United States of America. 102, 8633-8638 (2005).</li><li>Tohge, T., Yonekura-Sakakibara, K., Niida, R., Watanabe-Takahashi, A., Saito, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Phytochemical+genomics+in+Arabidopsis+thaliana:+A+case+study+for+functional+identification+of+flavonoid+biosynthesis+genes.">Phytochemical genomics in Arabidopsis thaliana: A case study for functional identification of flavonoid biosynthesis genes.</a> Pure and Applied Chemistry. 79, 811-823 (2007).</li><li>Tohge, T., Fernie, A. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Web-based+resources+for+mass-spectrometry-based+metabolomics:+A+user's+guide.">Web-based resources for mass-spectrometry-based metabolomics: A user's guide.</a> Phytochemistry. 70, 450-456 (2009).</li><li>Tohge, T., Fernie, A. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Combining+genetic+diversity,+informatics+and+metabolomics+to+facilitate+annotation+of+plant+gene+function.">Combining genetic diversity, informatics and metabolomics to facilitate annotation of plant gene function.</a> Nature Protocols. 5, 1210-1227 (2010).</li><li>Tohge, T., Kusano, M., Fukushima, A., Saito, K., Fernie, A. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Transcriptional+and+metabolic+programs+following+exposure+of+plants+to+UV-B+irradiation.">Transcriptional and metabolic programs following exposure of plants to UV-B irradiation.</a> Plant Signal Behav. 6, (2011).</li><li>Tohge, T., Ramos, M. S., Nunes-Nesi, A., Mutwil, M., Giavalisco, P., Steinhauser, D., Schellenberg, M., Willmitzer, L., Persson, S., Martinoia, E., Fernie, A. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Towards+the+storage+metabolome:+profiling+the+barley+vacuole.">Towards the storage metabolome: profiling the barley vacuole.</a> Plant Physiol. , (2011).</li><li>Yonekura-Sakakibara, K., Tohge, T., Matsuda, F., Nakabayashi, R., Takayama, H., Niida, R., Watanabe-Takahashi, A., Inoue, E., Saito, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comprehensive+flavonol+profiling+and+transcriptome+coexpression+analysis+leading+to+decoding+gene-metabolite+correlations+in+Arabidopsis.">Comprehensive flavonol profiling and transcriptome coexpression analysis leading to decoding gene-metabolite correlations in Arabidopsis.</a> Plant Cell. 20, 2160-2176 (2008).</li></ol>

没有利益冲突的声明。

既然已经用了好几年，转录组和代谢组学技术，数据集成为辅助的基因注释代谢过程中通常开始一个新的峰代表一个未知的代谢产物的鉴定。这一事实导致了下一个阶段，这是评估的定量或认为是负责其合成的新的候选基因变异的代谢产物峰。然而，在本协议中所述的战略有三大问题）峰值标注的困难，二）通路预测的复杂性，三）基因信息和基因表达数据质量的决议。要对付的第一个问题，高峰注释应开展合作与标准化合物或从MS n分析，参考提取物，突变分析，代谢物数据库检索和文献调查（ 图12）2，组合的方式，利用信息洗脱。为Second问题，预测途径只能得到正确的峰值注释。然而，组织特异性代谢产物分析也可以支持峰值标注，应与相关基因的基因表达密切相关，因为代谢产物积累。因此结合不同的组织和生长条件的文件，可以帮助这第二个问题。第三个问题，关于基因信息的分辨率取决于序列数据的进展。在没有完成基因组序列的模型工厂的情况下，使用等模式植物中的同源基因的共同表达分析是有益的。详细的氨基酸序列对齐比较和进化树分析，可以支持连接到其他物种的模式生物。 该协议是适用于所有的新陈代谢。这是最有效的，在初中和高中的代谢分析，这是很好的特点是受到强烈的转录çontrol 1,5,11,16。在一些例子，硫同化执行成功的合作表达分析，基因β-氧化，支链氨基酸降解，叶绿素击穿，和赖氨酸代谢3，细胞壁代谢10,7和光信号级联14。通过联合基因组学，代谢和信息注释基因的功能不仅是生物合成基因和转录因子直接调节，同时也为了解生理过程及反应（例如图3 14）。 要发展这种方法从模式植物，作物品种，各地植物物种的代谢比较是在一些一般性的代谢功能强大的方法。例如，如果检测到相同的化合物在不同的植物物种，一些同源基因在这些植物物种中发现，跨物种共同使用同源基因的表达分析可以提供strong支持你的预测。这种方法可以在拟南芥，杨树，紫花苜蓿，除了重要的作物，如大麦，大米，小麦和大豆，植物物种的共同表达分析（6，行星: <a href="http://aranet.mpimp-golm.mpg.de/" target="_blank">http://aranet.mpimp-golm.mpg DE /</a> 9，缔约方会议: <a href="http://webs2.kazusa.or.jp/kagiana/cop0911/" target="_blank">http://webs2.kazusa.or.jp/kagiana/cop0911/</a> ;看一个例子，15岁 ）。

<table border="1"><tbody><tr><td> 试剂名称 </td><td> 公司 </td><td> 目录编号 </td></tr><tr><td>蒸馏水的ULC / MS毕业生</td><td> BIOSOLVE </td><td> 23214102 </td></tr><tr><td>乙腈（ACN）的ULC / ms级</td><td> BIOSOLVE </td><td> 01204102 </td></tr><tr><td>甲醇（甲醇）的ULC / ms级</td><td> BIOSOLVE </td><td> 13684102 </td></tr><tr><td>蚁酸（甲酸）级的ULC / MS液相色谱</td><td> BIOSOLVE </td><td> 06914131 </td></tr><tr><td>标准化合物</td><td> EXTRASYNTHESE </td><td></td></tr><tr><td>线性离子阱（IT）的ESI-MS系统菲尼根 - 的LTQ </td><td>热电菲尼根</td><td></td></tr><tr><td>高效液相色谱系统测量师</td><td>热电菲尼根</td><td></td></tr><tr><td>分析列月神C18（2），2.0毫米直径，长度150毫米，100孔隙大小和3毫米的球形颗粒</td><td> phenomenex </td><td> 00F-4251-B0 </td></tr><tr><td> Xcalibur软件</td><td>热电菲尼根</td><td></td></tr></tbody></table>

annotation of plant gene function via combined genomics, metabolomics and informatics

由于模式植物物种完整的基因组序列和丰富的生物资源，如敲除突变体，野生种质和先进的繁殖种群数量不断扩大，有上升的负担，为基因功能注释。在这个协议中，使用联合共表达基因分析植物基因功能注释，代谢和信息提供（ 图1）。这种方法使用已知功能的靶基因，允许非注释基因的鉴定一定的代谢过程中可能会涉及与代谢目标化合物通过鉴定，理论基础。战略提出申请产生正向和反向遗传学的方法，尽管这些都不人口信息是毫不费力的。推论，这种方法也可以被用来作为一种方法来描述未知峰代表新的或特定的本身在有限的组织，植物或治疗压力，这是目前重要的试验，以了解植物的新陈代谢condary代谢产物。

Watch this Scientific Journal Video about Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics at JoVE.com

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics

基因组学相结合，共同表达的基因分析和鉴定目标化合物通过新陈代谢，给基因功能注释。

联合基因组学，代谢组学和信息通过植物基因功能的注解

Given the ever expanding number of model plant species for which complete genome sequences are available and the abundance of bio-resources such as ...

annotation-plant-gene-function-via-combined-genomics-metabolomics

Universidad de Cartagena UDEC

Research

JoVE Journal

Biology

19.6K 次观看.  Max-Planck-Institut. 基因组学相结合，共同表达的基因分析和鉴定目标化合物通过新陈代谢，给基因功能注释。

视频: Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics

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

mRNA Interactome Capture from Plant Protoplasts

RNA-binding proteins (RBPs) determine the fates of RNAs. They participate in all RNA biogenesis pathways and especially contribute to post-transcriptional gene regulation (PTGR) of messenger RNAs (mRNAs). In the past few years, a number of mRNA-bound proteomes from yeast and mammalian cell lines have been successfully isolated through the use of a novel method called &#34;mRNA interactome capture,&#34; which allows for the identification of mRNA-binding proteins (mRBPs) directly from a physiological environment. The method is composed of in vivo ultraviolet (UV) crosslinking, pull-down and purification of messenger ribonucleoprotein complexes (mRNPs) by oligo(dT) beads, and the subsequent identification of the crosslinked proteins by mass spectrometry (MS). Very recently, by applying the same method, several plant mRNA-bound proteomes have been reported simultaneously from different Arabidopsis tissue sources: etiolated seedlings, leaf tissue, leaf mesophyll protoplasts, and cultured root cells. Here, we present the optimized mRNA interactome capture method for Arabidopsis thaliana leaf mesophyll protoplasts, a cell type that serves as a versatile tool for experiments that include various cellular assays. The conditions for optimal protein yield include the amount of starting tissue and the duration of UV irradiation. In the mRNA-bound proteome obtained from a medium-scale experiment (107 cells), RBPs noted to have RNA-binding capacity were found to be overrepresented, and many novel RBPs were identified. The experiment can be scaled up (109 cells), and the optimized method can be applied to other plant cell types and species to broadly isolate, catalog, and compare mRNA-bound proteomes in plants.

Here, we present an interactome capture protocol applied to Arabidopsis thaliana leaf mesophyll protoplasts. This method critically relies on in vivo UV crosslinking and allows for the isolation and identification of plant mRNA-binding proteins from a physiological environment.

mRNA从植物原生质体捕获

RNA-binding proteins (RBPs) determine the fates of RNAs. They participate in all RNA biogenesis pathways and especially contribute to post-transcriptional ...

生物化学

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Recently, a new implementation of a previously described method for interpreting genome-wide association study (GWAS) data using metabolic pathway analysis has been developed and released. The Pathway Association Study Tool (PAST) was developed to address concerns with user-friendliness and slow-running analyses. This new user-friendly tool has been released on Bioconductor and Github. In testing, PAST ran analyses in less than one hour that previously required twenty-four or more hours. In this article, we present the protocol for using either the Shiny application or the R console to run PAST.

By running the Pathway Association Study Tool (PAST), either through the Shiny application or through the R console, researchers can gain a deeper understanding of the biological meaning of their genome-wide association study (GWAS) results by investigating the metabolic pathways involved.

代谢通路信息 GWAS 分析的路径关联研究工具

Recently, a new implementation of a previously described method for interpreting genome-wide association study (GWAS) data using metabolic pathway ...

A Multi-Omics Extraction Method for the In-Depth Analysis of Synchronized Cultures of the Green Alga Chlamydomonas reinhardtii

Microalgae have been the focus of research for their applications in the production of high value compounds, food and fuel. Moreover, they are valuable photosynthetic models facilitating the understanding of the basic cellular processes. System wide studies enable comprehensive and in-depth understanding of molecular functions of the organisms. However, multiple independent samples and protocols are required for proteomics, lipidomics and metabolomics studies introducing higher error and variability. A robust high throughput extraction method for the simultaneous extraction of chlorophyll, lipids, metabolites, proteins and starch from a single sample of the green alga Chlamydomonas reinhardtii is presented here. The illustrated experimental setup is for Chlamydomonas cultures synchronized using 12 h/12 h light/dark conditions. Samples were collected over a 24 h cell cycle to demonstrate that the metabolites, lipids and starch data obtained using various analytical platforms are well conformed. Furthermore, protein samples collected using the same extraction protocol were used to conduct detailed proteomics analysis to evaluate their quality and reproducibility. Based on the data, it can be inferred that the illustrated method provides a robust and reproducible approach to advance understanding of various biochemical pathways and their functions with greater confidence for both basic and applied research.

A Multi-Omics Extraction Method for the In-Depth Analysis of Synchronized Cultures of the Green Alga Chlamydomonas reinhardtii

System-wide analysis of multiple biomolecules is crucial to gain functional and mechanistic insights into biological processes. Hereby, an extensive protocol is described for high throughput extraction of lipids, metabolites, proteins and starch from a single sample harvested from synchronized Chlamydomonas culture.

一种对绿色藻原体同步文化进行深入分析的多表率提取方法

Microalgae have been the focus of research for their applications in the production of high value compounds, food and fuel. Moreover, they are valuable ...

发育生物学

Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products

The chemical space covered by natural products is immense and widely unrecognized. Therefore, convenient methodologies to perform wide-ranging evaluation of their functions in nature and potential human benefits (e.g., for drug discovery applications) are desired. This protocol describes the combination of genome mining (GM) and molecular networking (MN), two contemporary approaches that match gene cluster-encoded annotations in whole genome sequencing with chemical structure signatures from crude metabolic extracts. This is the first step towards the discovery of new natural entities. These concepts, when applied together, are defined here as MS-guided genome mining. In this method, the main components are previously designated (using MN), and structurally related new candidates are associated with genome sequence annotations (using GM). Combining GM and MN is a profitable strategy to target new molecule backbones or harvest metabolic profiles in order to identify analogues from already known compounds.

A mass spectrometry-guided genome mining protocol is established and described here. It is based on genome sequence information and LC-MS/MS analysis and aims to facilitate identification of molecules from complex microbial and plant extracts.

质谱导览基因组挖掘作为发现新天然产品的工具

The chemical space covered by natural products is immense and widely unrecognized. Therefore, convenient methodologies to perform wide-ranging evaluation ...

化学

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Both gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) are widely used metabolomics approaches to detect and quantify hundreds of thousands of metabolite features. However, the application of these techniques to a large number of samples is subject to more complex interactions, particularly for genome-wide association studies (GWAS). This protocol describes an optimized metabolic workflow, which combines an efficient and fast sample preparation with the analysis of a large number of samples for legume crop species. This slightly modified extraction method was initially developed for the analysis of plant and animal tissues and is based on extraction in methyl tert-butyl ether: methanol solvent to allow the capture of polar and lipid metabolites. In addition, we provide a step-by-step guide for reducing analytical variations, which are essential for the high-throughput evaluation of metabolic variance in GWAS.

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

In this protocol, we present an optimized workflow, which combines an efficient and fast sample preparation of many samples. In addition, we provide a step-by-step guide to reduce analytical variations for high-throughput evaluation of metabolic GWAS studies.

大规模多组学全基因组关联研究 （Mo-GWAS）:样品制备和归一化指南

Both gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) are widely used metabolomics approaches to detect ...

使用 13C6-葡萄糖标记鉴定药用植物中的初级器官

13C6&#45;Glucose Labeling Associated with LC&#45;MS&#58; Identification of Plant Primary Organs in Secondary Metabolite Synthesis

This paper presents a novel and efficient method for certifying primary organs involved in secondary metabolite synthesis. As the most important secondary metabolite in Parispolyphylla var. yunnanensis (Franch.) Hand. -Mzt. (PPY), Paris saponin (PS) has a variety of pharmacological activities and PPY is in increasing demand. This study established leaf, rhizome, and stem-vascular-bundle 13C6-Glucose feeding and non-feeding four treatments to precisely certify the primary organs involved in Paris saponins VII (PS VII) synthesis. By combining liquid chromatography-mass spectrometry (LC-MS), the 13C/12C ratios of leaf, rhizome, stem, and root in different treatments were quickly and accurately calculated, and four types of PS isotopic ion peak(M&#8722;) ratios were found: (M+1) &#8722;/M&#8722;, (M+2) &#8722;/M&#8722;, (M+3) &#8722;/M&#8722; and (M+4) &#8722;/M&#8722;. The results showed that the ratio of 13C/12C in the rhizomes of the stem-vascular-bundle and rhizome feeding treatments was significantly higher than that in the non-feeding treatment. Compared to the non-feeding treatment, the ratio of PS VII molecules (M+2) &#8722;/M&#8722; in the leaves increased significantly under leaf and stem-vascular-bundle feeding treatments. Simultaneously, compared to the non-feeding treatment, the ratio of PS VII molecules (M+2) &#8722;/M&#8722; in the leaves under rhizome treatment showed no significant difference. Furthermore, the ratio of PS VII molecules (M+2) &#8722;/M&#8722; in the stem, root, and rhizome showed no differences among the four treatments. Compared to the non-feeding treatment, the ratio of the Paris saponin II (PS II) molecule (M+2) &#8722;/M&#8722; in leaves under leaf feeding treatment showed no significant difference, and the (M+3) &#8722;/M&#8722; ratio of PS II molecules in leaves under leaf feeding treatment were lower. The data confirmed that the primary organ for the synthesizing of PS VII is the leaves. It lays the foundation for future identification of the primary organs and pathways involved in the synthesis of secondary metabolites in medicinal plants.

作者聚焦：使用 13C6-葡萄糖标记和 LC-MS 进行植物初级器官分析

The developed method of 13C6-Glucose labeling combined with liquid chromatography high-resolution mass spectrometry is versatile and lays the foundation for future studies on the primary organs and pathways involved in the synthesis of secondary metabolites in medicinal plants, as well as the comprehensive utilization of these secondary metabolites.

13与LC-MS相关的C6-葡萄糖标记：次生代谢物合成中植物初级器官的鉴定

This paper presents a novel and efficient method for certifying primary organs involved in secondary metabolite synthesis. As the most important secondary ...

Fluorescence-microscopy Screening and Next-generation Sequencing: Useful Tools for the Identification of Genes Involved in Organelle Integrity

This protocol describes a fluorescence microscope-based screening of Arabidopsis seedlings and describes how to map recessive mutations that alter the subcellular distribution of a specific tagged fluorescent marker in the secretory pathway. Arabidopsis is a powerful biological model for genetic studies because of its genome size, generation time, and conservation of molecular mechanisms among kingdoms. The array genotyping as an approach to map the mutation in alternative to the traditional method based on molecular markers is advantageous because it is relatively faster and may allow the mapping of several mutants in a really short time frame. This method allows the identification of proteins that can influence the integrity of any organelle in plants. Here, as an example, we propose a screen to map genes important for the integrity of the endoplasmic reticulum (ER). Our approach, however, can be easily extended to other plant cell organelles (for example see1,2), and thus represents an important step toward understanding the molecular basis governing other subcellular structures.

A fundamental quest in cell biology is to define the mechanisms that underlie the identity of the organelles that make eukaryotic cells. Here we propose a method to identify the genes responsible for the morphological and functional integrity of plant organelles using fluorescence microscopy and next-generation sequencing tools.

荧光显微镜筛选和新一代测序:参与细胞器完整性的基因鉴定的有用工具

This protocol describes a fluorescence microscope-based screening of Arabidopsis seedlings and describes how to map recessive mutations that alter the ...

联合基因组学，代谢组学和信息通过植物基因功能的注解

摘要

探索更多视频

此视频中的章节

风土通过概念葡萄果实代谢组学和转录组解释的

mRNA从植物原生质体捕获

代谢通路信息 GWAS 分析的路径关联研究工具

一种对绿色藻原体同步文化进行深入分析的多表率提取方法

质谱导览基因组挖掘作为发现新天然产品的工具

大规模多组学全基因组关联研究（Mo-GWAS）:样品制备和归一化指南

¹³与LC-MS相关的C₆-葡萄糖标记：次生代谢物合成中植物初级器官的鉴定

¹³与LC-MS相关的C₆-葡萄糖标记：次生代谢物合成中植物初级器官的鉴定

¹³与LC-MS相关的C₆-葡萄糖标记：次生代谢物合成中植物初级器官的鉴定

荧光显微镜筛选和新一代测序:参与细胞器完整性的基因鉴定的有用工具

联合基因组学，代谢组学和信息通过植物基因功能的注解

摘要

探索更多视频

此视频中的章节

风土通过概念葡萄果实代谢组学和转录组解释的

mRNA从植物原生质体捕获

代谢通路信息 GWAS 分析的路径关联研究工具

一种对绿色藻原体同步文化进行深入分析的多表率提取方法

质谱导览基因组挖掘作为发现新天然产品的工具

大规模多组学全基因组关联研究 （Mo-GWAS）:样品制备和归一化指南

13与LC-MS相关的C6-葡萄糖标记：次生代谢物合成中植物初级器官的鉴定

13与LC-MS相关的C6-葡萄糖标记：次生代谢物合成中植物初级器官的鉴定

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