这项工作是由德国联邦研究所资助风险评估 (BfR)。

<ol>
	<li>Seok, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genomic+responses+in+mouse+models+poorly+mimic+human+inflammatory+diseases.">Genomic responses in mouse models poorly mimic human inflammatory diseases.</a> Proc Natl Acad Sci U S A. 110 (9), 3507-3512 (2013).</li><li>Takao, K., Miyakawa, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genomic+responses+in+mouse+models+greatly+mimic+human+inflammatory+diseases.">Genomic responses in mouse models greatly mimic human inflammatory diseases.</a> Proc Natl Acad Sci U S A. 112 (4), 1167-1172 (2015).</li><li>Weidner, C., Steinfath, M., Opitz, E., Oelgeschl&#228;ger, M., Sch&#246;nfelder, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Defining+the+optimal+animal+model+for+translational+research+using+gene+set+enrichment+analysis.">Defining the optimal animal model for translational research using gene set enrichment analysis.</a> EMBO Mol Med. 8 (8), 831-838 (2016).</li><li>Subramanian, 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=Gene+set+enrichment+analysis:+a+knowledge-based+approach+for+interpreting+genome-wide+expression+profiles.">Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.</a> Proc Natl Acad Sci U S A. 102 (43), 15545-15550 (2005).</li><li>Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M., Tanabe, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=KEGG+as+a+reference+resource+for+gene+and+protein+annotation.">KEGG as a reference resource for gene and protein annotation.</a> Nucleic Acids Res. 44 (D1), D457-D462 (2016).</li><li>Kanehisa, M., Goto, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=KEGG:+kyoto+encyclopedia+of+genes+and+genomes.">KEGG: kyoto encyclopedia of genes and genomes.</a> Nucleic Acids Res. 28 (1), 27-30 (2000).</li><li>Fabregat, 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=The+Reactome+pathway+Knowledgebase.">The Reactome pathway Knowledgebase.</a> Nucleic Acids Res. 44 (D1), D481-D487 (2016).</li><li>Croft, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Reactome+pathway+knowledgebase.">The Reactome pathway knowledgebase.</a> Nucleic Acids Res. 42 (Database issue), D472-D477 (2014).</li><li>Nishimura, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=BioCarta.">BioCarta.</a> Biotech Software &amp; Internet Report. 2 (3), 117-120 (2001).</li><li>Edgar, R., Domrachev, M., Lash, A. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Gene+Expression+Omnibus:+NCBI+gene+expression+and+hybridization+array+data+repository.">Gene Expression Omnibus: NCBI gene expression and hybridization array data repository.</a> Nucleic Acids Res. 30 (1), 207-210 (2002).</li><li>Kolesnikov, N., 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=ArrayExpress+update--simplifying+data+submissions.">ArrayExpress update--simplifying data submissions.</a> Nucleic Acids Res. 43 (Database issue), D1113-D1116 (2015).</li><li>Cohen, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Sepsis:+a+roadmap+for+future+research.">Sepsis: a roadmap for future research.</a> Lancet Infect Dis. 15 (5), 581-614 (2015).</li><li>Spinelli, L., Carpentier, S., Montanana Sanchis, F., Dalod, M., Vu Manh, T. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=BubbleGUM:+automatic+extraction+of+phenotype+molecular+signatures+and+comprehensive+visualization+of+multiple+Gene+Set+Enrichment+Analyses.">BubbleGUM: automatic extraction of phenotype molecular signatures and comprehensive visualization of multiple Gene Set Enrichment Analyses.</a> BMC Genomics. 16 (1), 814 (2015).</li></ol>

作者宣称，他们有没有经济利益的竞争。

动物模型长申请疾病机制研究和发展的新的治疗策略。然而，动物模型的真怀疑开始蔓延后临床试验12失败。此外，适当的战略分析和解释临床前试验的大组学数据有争议讨论提出了相反的结论，应用不同的数据分析策略1 后来自相同的数据 ，2。因此，是高需求量进一步健全生物信息学技术复杂的组学数据分析系统定义的最优给定的人类疾病动物模型。最好的可用模型的应用不仅提高了转化型研究进一步有助于避免与人类的处境可能不相关的动物实验动物福利。
提出的协议描述了系统地比较，目的是找出最优的动物模型和给定的人类疾病治疗方案的不同物种的组学数据的标准的方法。通过使用 GSEA 而不是单基因分析，本议定书绕过与主观设置的阈值基因表达和基因筛选有关的所有问题。将重点放在所选路径进一步允许具体地址 （病理） 生理过程的障碍/条件问题 （例如，炎症）。当然，GSEA 结果的准确性取决于当前基因集注释的质量和是否调节机制保守的物种之间。然而，我们推测，在一般养护是更高一级通路比单一基因水平上。此外，集的浓缩方法是为不同的平台和实验模型或临床组群之间的转录组数据的比较，比单基因分析13更加健全。
而不是使用预定义的基因集等途径，该方法还允许定义自定义基因集。尤其是，实验表达数据可以用于标识相关基因的激活或抑制处于一种状态 （例如，重叠的人类基因在临床队列）。De novo定义的基因集可然后用于测试为丰富的数据来自不同的动物模型。此替代方法避免了使用带注释的通路的 '改道'。此外，议定书 》 不局限于转录组数据的比较，但转移到任何组学数据包括蛋白质组学和代谢组学。尽管如此，就必须牢记这种办法是限制向现有的组学数据从模型小鼠和人类，和，它并不表示如何发展新的动物模型。然而，它代表现有的数据，这可能促进仔细选择最优的动物模型，从而避免不必要和误导性的翻译研究的规范解释的有效途径。

广泛用于动物模型来研究人类疾病，因为它们对人类在遗传学、 解剖学和生理学方面的假定相似性。此外，动物模型经常作为临床治疗的守门人，可以对转化医学研究的成功产生巨大的影响。谨慎选择最优的动物模型可以减少误导的动物研究的数量。最近，为转化型研究的动物模型的相关性有争议的讨论，特别是因为分析从人类的炎症性疾病和相关的小鼠模型中获得相同的数据集导致相互矛盾的结论1，2。这次讨论组学数据分析期间展示了一个基本的问题: 标准化系统的数据分析方法需减少有偏见的基因选择，增加的物种间比较3的鲁棒性。
传统上，转录组学数据 （和其他组学数据） 分析在级别上进行单基因和包括基因选择基于严格禁产参数初始步骤 (例如，褶皱变化 &#62; 2.0，p 值 &#60; 0.05)。然而，最初截止参数的设置往往是主观的、 任意的和生理上有正当理由，和甚至可以导致相反结论1，2。此外，基因的初步选择一般限于几个高度-和氪基因分析，因此不具有足够的灵敏度，要包括的大多数基因表达的差异程度较轻。
随着基因组学时代在本世纪初的兴起和增长的知识的生物学通路和上下文，替代统计方法被开发，允许绕过单基因水平分析的局限性。基因集富集分析 (GSEA)4，这是被广泛接受的转录组学数据分析方法之一，利用先验定义组的基因 （例如，信号转导通路，近端位置对染色体等.）。GSEA 首先映射所有检测到未筛选的基因到预期的基因集 （例如，路径），不论其个人的转变，在表达式中。因此，这种方法还包括适度下调的基因，否则会丢失与单基因水平的分析。在基因组内的表达式中的添加剂更改随后可使用运行总和统计信息。
尽管其广泛应用在医学研究中，GSEA 和相关设置的浓缩方法不不言而喻考虑复杂的组学数据分析。在这里，我们描述协议比较组学数据来自于人体样本与那些从小鼠模型以确定翻译研究的理想模式。我们展示协议基于集合用于模仿人类的炎性疾病的小鼠模型的适用性。然而，这个分析管道不局限于人-鼠比较和修正对进一步研究的问题。

<table><tbody><tr><td>Excel</td><td>Microsoft Corporation</td><td></td><td></td></tr></tbody></table>

a protocol for using gene set enrichment analysis to identify the appropriate animal model for translational research

最近的研究表明人类疾病转录组数据集与数据集从小鼠模型使用传统基因到基因比较技术导致相互矛盾的结论关于相关性的动物模型的转化研究。不同的基因表达分析差异的主要原因是任意筛选差异表达基因。此外，单个基因不同物种和经常的平台之间的比较被受到限制技术的差异，导致人类与动物模型的数据之间的误解 con/不一致。因此，需要系统的数据分析的标准化的方法。为了克服主观基因筛选和无效基因到基因比较，我们最近表明基因集的富集分析 (GSEA)，潜力来避免这些问题。因此，我们制定的 GSEA 来区分合适与不合适的动物模型，为转化型研究使用标准化的协议。本议定书不适于预测如何设计新模型系统是-先验的因为它需要现有实验组学数据。但是，议定书 》 描述了如何选择最合适的动物模型，从而避免不必要的动物实验和误导翻译研究以标准化的方式解释现有的数据。

论证了的 GSEA 工作流和模范数据的屏幕截图。图 1显示了包含感兴趣的转录组数据的基因表达数据文件。为每一个描述性的表型文件是必修课，是图 2中所示。带注释的基因集 （例如，通路） 基因设置的数据库文件 (图 3) 中定义。图 4显示了分步协议 GSEA 软件工具的使用。图 5给出了堪称典范的结果报告。图 6中，总结了详细的 GSEA 富集结果。对于不同的基因表达研究的比较，特别是人类与老鼠的研究，列联表是需要 (图 7)。为结果的可视化，图 8显示了相关矩阵的途径比较人类和老鼠的研究。
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/55768/55768fig1v2.jpg"> 
图 1: GSEA 基因表达数据文件。该文件包含表达式值为所有检出基因 （或探针），也不可能差异表达的基因。因此，该文件通常包含许多成千上万个基因。(A) 基因表达数据文件包含每个示例中的数据。第一行包含的标签名称 (在这里: 探测器 ID) 其次是可选的描述和个别样品名称 (在这里: GSM515585、 GSM515586，等)。该文件的其余部分包含表达式值为每个基因，每个样本的数据集。(B) 替代基因表达数据格式。从外部计算组度量 (在这里: 平均比率) 可用于 GSEA preranked 工具如果个别样本数据不可用。<a href="http://ecsource.jove.com/files/ftp_upload/55768/55768fig1v2large.jpg" target="_blank">请点击这里查看此图的大版本。</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/55768/55768fig2.jpg"> 
图 2: GSEA 型文件。文件据此结合各自的样品组和组的标签。第一行包含的样本总数和进一步的组数。第一行第三个字段始终是 '1'。第二行包含每个组的名称。行以一个磅符号 （#） 后跟一个空格开头。第三行包含为每个样品组标签 (在这里: 0 或 1)。<a href="http://ecsource.jove.com/files/ftp_upload/55768/55768fig2large.jpg" target="_blank">请点击这里查看此图的大版本。</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/55768/55768fig3.jpg"> 
图 3: GSEA 基因数据库文件设置。这个文件定义分配给某些生物过程或种类的基因组的 (在这里: 炎症通路)。在格林尼治标准时间格式中，每一行代表一基因组，由一个名称、 说明和包括的基因 （官方雨果基因符号）。<a href="http://ecsource.jove.com/files/ftp_upload/55768/55768fig3large.jpg" target="_blank">请点击这里查看此图的大版本。</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/55768/55768fig4v2.jpg"> 
图 4: GSEA 软件设置。GSEA 软件工具是从 Broad 研究所网站中下载 java 桌面应用程序来决定的。(A) 开始菜单。左侧包含导航菜单，而右侧部分 （家里） 给出了 GSEA 工作流的简短摘要。单击加载数据按钮将会打开一个新的选项卡导入文件。(B)负载数据节之前数据导入。所需的文件可以导入通过文件浏览器。(C)负载数据节后数据导入。导入的数据文件列出在对象缓存中和到数据集 （强制性文件），表型 （强制性文件），组织基因设置数据库 （可选，如果互联网连接提供） 和芯片文件 （可选，如果互联网连接提供）。单击运行 GSEA按钮将会打开一个新的选项卡设置分析参数。(D)运行 GSEA节。选项卡用于设置分析参数分为基本字段和先进的字段必填字段。单击运行按钮在窗口的右下角将开始分析。分析的进展，将然后在窗口的左下角 GSEA 报告部分可见。后整理分析，状态 '成功' 出现在 GSEA 报告部分。(E) GSEA preranked 工具。基因表达数据文件包含外部计算的组而不是单个样本数据的度量数据可以分析通过主导航栏。<a href="http://ecsource.jove.com/files/ftp_upload/55768/55768fig4v2large.jpg" target="_blank">请点击这里查看此图的大版本。</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/55768/55768fig5.jpg"> 
图 5: GSEA 报告。GSEA 报告将在总结了所有的结果和所选的参数的浏览器窗口中打开。导航菜单的上部的两个部分包括定义的组 （例如，在金黄色葡萄球菌处理样品或健康对照样品浓缩） 的基因集的富集结果。在该示例中，65 基因集 （途径） 42金黄色葡萄球菌治疗小鼠状态时激活其中 14 显著富集与罗斯福低于 25%。同样，23 65 基因集 （途径） 被抑制金黄色葡萄球菌治疗小鼠，而其中 18 显著富集与罗斯福低于 25%。点击详细的富集结果打开 html 或 excel 用于导出所需的不同的基因表达研究比较的分析数据文件。<a href="http://ecsource.jove.com/files/ftp_upload/55768/55768fig5large.jpg" target="_blank">请点击这里查看此图的大版本。</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/55768/55768fig6v2.jpg"> 
图 6: 详细的富集结果。(A) 导出电子表格文件包含详细的分析结果为被激活在金黄色葡萄球菌的基因组 （途径） 治疗小鼠。电子表格文件包含每个分析的基因组，包括基因组、 其大小，其归一化的浓缩分数，其名义 （裸眼） p 值和其罗斯福值名称庞大的数据。(B) 简化电子表格文件，仅包含所需的比较不同的基因表达研究的信息。<a href="http://ecsource.jove.com/files/ftp_upload/55768/55768fig6v2large.jpg" target="_blank">请点击这里查看此图的大版本。</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/55768/55768fig7.jpg"> 
图 7:3 x 3 列联表的 GSEA 结果。(A) 共同的列联表格式 2 研究的比较。(B) 模范数字调节通路的人类脓毒症研究 (GSE9960) 与小鼠比较的金黄色葡萄球菌注入模型 (GSE20524)。<a href="http://ecsource.jove.com/files/ftp_upload/55768/55768fig7large.jpg" target="_blank">请点击这里查看此图的大版本。</a>
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/55768/55768fig8.jpg"> 
图 8: 相关矩阵的途径比较人类和小鼠研究。重叠的通路就可以从一个 （鼠标） 研究预测的影响在另一个 （人类） 研究 （蓝色、 减少、 低度相关; 红，增加，高相关性） 获得的信息增益所示。在该示例中的人类和小鼠数据集比较揭示了高度相关人类临床研究 （研究 10 和 11，虚线），指示这些小鼠模型最适合于模仿的实验小鼠模型的一个子群人类的处境。相比之下，7、 8 和 9 的研究表明没有相关人类疾病的研究。<a href="http://ecsource.jove.com/files/ftp_upload/55768/55768fig8large.jpg" target="_blank">请点击这里查看此图的大版本。</a>

Watch this Scientific Journal Video about A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research at JoVE.com

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

我们提供一个标准化的协议用于基因集的浓缩转录组数据分析，找出理想的小鼠模型的转化研究。 该协议可以用 DNA 微阵列和 RNA 测序数据，并且可以进一步扩展到其他组学数据如果数据可用。

一个协议来使用基因集富集分析以确定合适的动物模型转化型研究

Recent studies that compared transcriptomic datasets of human diseases with datasets from mouse models using traditional gene-to-gene comparison ...

a-protocol-for-using-gene-set-enrichment-analysis-to-identify

Nanyang Technological University

Research

JoVE Journal

Biology

17.8K 次观看.  German Federal Institute for Risk Assessment (BfR). 我们提供一个标准化的协议用于基因集的浓缩转录组数据分析，找出理想的小鼠模型的转化研究。该协议可以用 DNA 微阵列和 RNA 测序数据，并且可以进一步扩展到其他组学数据如果数据可用。

视频: A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Sample Preparation and Analysis of RNASeq-based Gene Expression Data from Zebrafish

The analysis of global gene expression changes is a valuable tool for identifying novel pathways underlying observed phenotypes. The zebrafish is an excellent model for rapid assessment of whole transcriptome from whole animal or individual cell populations due to the ease of isolation of RNA from large numbers of animals. Here a protocol for global gene expression analysis in zebrafish embryos using RNA sequencing (RNASeq) is presented. We describe preparation of RNA from whole embryos or from cell populations obtained using cell sorting in transgenic animals. We also describe an approach for analysis of RNASeq data to identify enriched pathways and Gene Ontology (GO) terms in global gene expression data sets. Finally, we provide a protocol for validation of gene expression changes using quantitative reverse transcriptase PCR (qRT-PCR). These protocols can be used for comparative analysis of control and experimental sets of zebrafish to identify novel gene expression changes, and provide molecular insight into phenotypes of interest.

This protocol presents an approach for whole transcriptome analysis from zebrafish embryos, larvae, or sorted cells. We include isolation of RNA, pathway analysis of RNASeq data, and qRT-PCR-based validation of gene expression changes.

斑马鱼 RNASeq-based 基因表达数据的样品制备与分析

The analysis of global gene expression changes is a valuable tool for identifying novel pathways underlying observed phenotypes. The zebrafish is an ...

科研

遗传学

Transcriptome Profiling of In-Vivo Produced Bovine Pre-implantation Embryos Using Two-color Microarray Platform

Early embryonic loss is a large contributor to infertility in cattle. Moreover, bovine becomes an interesting model to study human preimplantation embryo development due to their similar developmental process. Although genetic factors are known to affect early embryonic development, the discovery of such factors has been a serious challenge. Microarray technology allows quantitative measurement and gene expression profiling of transcript levels on a genome-wide basis. One of the main decisions that have to be made when planning a microarray experiment is whether to use a one- or two-color approach. Two-color design increases technical replication, minimizes variability, improves sensitivity and accuracy as well as allows having loop designs, defining the common reference samples. Although microarray is a powerful biological tool, there are potential pitfalls that can attenuate its power. Hence, in this technical paper we demonstrate an optimized protocol for RNA extraction, amplification, labeling, hybridization of the labeled amplified RNA to the array, array scanning and data analysis using the two-color analysis strategy.

Transcriptome Profiling of In-Vivo Produced Bovine Pre-implantation Embryos Using Two-color Microarray Platform

Microarray technology allows quantitative measurement and gene expression profiling of transcripts on a genome-wide basis. Therefore, this protocol provides an optimized technical procedure in a two-color custom made bovine array using Day 7 bovine embryos to demonstrate the feasibility of using low amount of total RNA.

转录组分析]<em&gt;活体</em&gt;生产牛胚胎植入前胚胎采用双色微阵列平台

Early embryonic loss is a large contributor to infertility in cattle. Moreover, bovine becomes an interesting model to study human preimplantation embryo ...

发育生物学

Exploring the Effects of Spaceflight on Mouse Physiology using the Open Access NASA GeneLab Platform

Performing biological experiments in space requires special accommodations and procedures to ensure that these investigations are performed effectively and efficiently. Moreover, given the infrequency of these experiments it is imperative that their impacts be maximized. The rapid advancement of omics technologies offers an opportunity to dramatically increase the volume of data produced from precious spaceflight specimens. To capitalize on this, NASA has developed the GeneLab platform to provide unrestricted access to spaceflight omics data and encourage its widespread analysis. Rodents (both rats and mice) are common model organisms used by scientists to investigate space-related biological impacts. The enclosure that house rodents during spaceflight are called Rodent Habitats (formerly Animal Enclosure Modules), and are substantially different from standard vivarium cages in their dimensions, air flow, and access to water and food. In addition, due to environmental and atmospheric conditions on the International Space Station (ISS), animals are exposed to a higher CO2 concentration. We recently reported that mice in the Rodent Habitats experience large changes in their transcriptome irrespective of whether animals were on the ground or in space. Furthermore, these changes were consistent with a hypoxic response, potentially driven by higher CO2 concentrations. Here we describe how a typical rodent experiment is performed in space, how omics data from these experiments can be accessed through the GeneLab platform, and how to identify key factors in this data. Using this process, any individual can make critical discoveries that could change the design of future space missions and activities.

The NASA GeneLab platform provides unfettered access to precious omics data from biological spaceflight experiments. We describe how a typical mouse experiment is conducted in space and how data from such experiments can be accessed and analyzed.

利用开放接入美国宇航局基因实验室平台探索航天对小鼠生理的影响

Performing biological experiments in space requires special accommodations and procedures to ensure that these investigations are performed effectively ...

A Rat Methyl-Seq Platform to Identify Epigenetic Changes Associated with Stress Exposure

As genomes of a wider variety of animals become available, there is an increasing need for tools that can capture dynamic epigenetic changes in these animal models. The rat is one particular model animal where an epigenetic tool can complement many pharmacological and behavioral studies to provide insightful mechanistic information. To this end, we adapted the SureSelect Target Capture System (referred to as Methyl-Seq) for the rat, which can assess DNA methylation levels across the rat genome. The rat design targeted promoters, CpG islands, island shores, and GC-rich regions from all RefSeq genes. 
To implement the platform on a rat experiment, male Sprague Dawley rats were exposed to chronic variable stress for 3 weeks, after which blood samples were collected for genomic DNA extraction. Methyl-Seq libraries were constructed from the rat DNA samples by shearing, adapter ligation, target enrichment, bisulfite conversion, and multiplexing. Libraries were sequenced on a next-generation sequencing platform and the sequenced reads were analyzed to identify DMRs between DNA of stressed and unstressed rats. Top candidate DMRs were independently validated by bisulfite pyrosequencing to confirm the robustness of the platform.
Results demonstrate that the rat Methyl-Seq platform is a useful epigenetic tool that can capture methylation changes induced by exposure to stress.

Here, we describe the protocol and implementation of Methyl-Seq, an epigenomic platform, using a rat model to identify epigenetic changes associated with chronic stress exposure. Results demonstrate that the rat Methyl-Seq platform is capable of detecting methylation differences that arise from stress exposure in rats.

大鼠甲基 Seq 平台识别与应力接触相关的表观变化

As genomes of a wider variety of animals become available, there is an increasing need for tools that can capture dynamic epigenetic changes in these ...

生物化学

基因组规模筛选，用于识别和比较肿瘤中的治疗靶点

Genetic Profiling and Genome-Scale Dropout Screening to Identify Therapeutic Targets in Mouse Models of Malignant Peripheral Nerve Sheath Tumor

Malignant Peripheral Nerve Sheath Tumors (MPNSTs) are derived from Schwann cells or their precursors. In patients with the tumor susceptibility syndrome neurofibromatosis type 1 (NF1), MPNSTs are the most common malignancy and the leading cause of death. These rare and aggressive soft-tissue sarcomas offer a stark future, with 5-year disease-free survival rates of 34-60%. Treatment options for individuals with MPNSTs are disappointingly limited, with disfiguring surgery being the foremost treatment option. Many once-promising therapies such as tipifarnib, an inhibitor of Ras signaling, have failed clinically. Likewise, phase II clinical trials with erlotinib, which targets the epidermal growth factor (EFGR), and sorafenib, which targets the vascular endothelial growth factor receptor (VEGF), platelet-derived growth factor receptor (PDGF), and Raf, in combination with standard chemotherapy, have also failed to produce a response in patients. 
In recent years, functional genomic screening methods combined with genetic profiling of cancer cell lines have proven useful for identifying essential cytoplasmic signaling pathways and the development of target-specific therapies. In the case of rare tumor types, a variation of this approach known as cross-species comparative oncogenomics is increasingly being used to identify novel therapeutic targets. In cross-species comparative oncogenomics, genetic profiling and functional genomics are performed in genetically engineered mouse (GEM) models and the results are then validated in the rare human specimens and cell lines that are available. 
This paper describes how to identify candidate driver gene mutations in human and mouse MPNST cells using whole exome sequencing (WES). We then describe how to perform genome-scale shRNA screens to identify and compare critical signaling pathways in mouse and human MPNST cells and identify druggable targets in these pathways. These methodologies provide an effective approach to identifying new therapeutic targets in a variety of human cancer types.

作者聚焦:通过基因组规模 shRNA 筛选寻找恶性周围神经鞘瘤的新治疗靶点 

We have developed a cross-species comparative oncogenomics approach utilizing genomic analyses and functional genomic screens to identify and compare therapeutic targets in tumors arising in genetically engineered mouse models and the corresponding human tumor type.

遗传分析和基因组规模的辍学筛选以确定恶性周围神经鞘瘤小鼠模型中的治疗靶点

Malignant Peripheral Nerve Sheath Tumors (MPNSTs) are derived from Schwann cells or their precursors. In patients with the tumor susceptibility syndrome ...

癌症研究

Single Cell Multiplex Reverse Transcription Polymerase Chain Reaction After Patch-clamp

The cerebral cortex is composed of numerous cell types exhibiting various morphological, physiological, and molecular features. This diversity hampers easy identification and characterization of these cell types, prerequisites to study their specific functions. This article describes the multiplex single cell reverse transcription polymerase chain reaction (RT-PCR) protocol, which allows, after patch-clamp recording in slices, to detect simultaneously the expression of tens of genes in a single cell. This simple method can be implemented with morphological characterization and is widely applicable to determine the phenotypic traits of various cell types and their particular cellular environment, such as in the vicinity of blood vessels. The principle of this protocol is to record a cell with the patch-clamp technique, to harvest and reverse transcribe its cytoplasmic content, and to detect qualitatively the expression of a predefined set of genes by multiplex PCR. It requires a careful design of PCR primers and intracellular patch-clamp solution compatible with RT-PCR. To ensure a selective and reliable transcript detection, this technique also requires appropriate controls from cytoplasm harvesting to amplification steps. Although precautions discussed here must be strictly followed, virtually any electrophysiological laboratory can use the multiplex single cell RT-PCR technique.

This protocol describes the critical steps and precautions required to perform single cell multiplex reverse transcription polymerase chain reaction after patch-clamp. This technique is a simple and effective method to analyze the expression profile of a predetermined set of genes from a single cell characterized by patch-clamp recordings.

膜片钳后单细胞多重反转录聚合酶链反应

The cerebral cortex is composed of numerous cell types exhibiting various morphological, physiological, and molecular features. This diversity hampers ...

神经科学

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Here, we present methods for the development of assays to query potentially clinically significant nonsynonymous changes using in vivo complementation in zebrafish. Zebrafish (Danio rerio) are a useful animal system due to their experimental tractability; embryos are transparent to enable facile viewing, undergo rapid development ex vivo, and can be genetically manipulated.1 These aspects have allowed for significant advances in the analysis of embryogenesis, molecular processes, and morphogenetic signaling. Taken together, the advantages of this vertebrate model make zebrafish highly amenable to modeling the developmental defects in pediatric disease, and in some cases, adult-onset disorders. Because the zebrafish genome is highly conserved with that of humans (~70% orthologous), it is possible to recapitulate human disease states in zebrafish. This is accomplished either through the injection of mutant human mRNA to induce dominant negative or gain of function alleles, or utilization of morpholino (MO) antisense oligonucleotides to suppress genes to mimic loss of function variants. Through complementation of MO-induced phenotypes with capped human mRNA, our approach enables the interpretation of the deleterious effect of mutations on human protein sequence based on the ability of mutant mRNA to rescue a measurable, physiologically relevant phenotype. Modeling of the human disease alleles occurs through microinjection of zebrafish embryos with MO and/or human mRNA at the 1-4 cell stage, and phenotyping up to seven days post fertilization (dpf). This general strategy can be extended to a wide range of disease phenotypes, as demonstrated in the following protocol. We present our established models for morphogenetic signaling, craniofacial, cardiac, vascular integrity, renal function, and skeletal muscle disorder phenotypes, as well as others.

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Here, we present a systematic approach for developing physiologically relevant, sensitive and specific in vivo assays for interpreting variation in human pathology. Transient genetic manipulation via microinjection of WT and mutant human mRNA and morpholino (MO) antisense oligonucleotides harness the tractability of the developing zebrafish embryo to rapidly assay pathogenic mutations, especially, but not exclusively, in the context of human developmental disorders.

在体内病态的人类基因组建模使用斑马鱼

Here,  we present methods for the development of assays to query potentially clinically  significant nonsynonymous changes using in  vivo complementation ...

生物学

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Pathogens can cause a wide variety of infectious diseases. The biological processes induced by the host in response to infection determine the severity of the disease.&#160;To study such processes, researchers can use high-throughput sequencing techniques (RNA-seq) that measure the dynamic changes of the host transcriptome at different stages of infection, clinical outcomes, or disease severity.This investigation can lead to a better understanding of the diseases, as well as uncovering potential drug targets and treatments. The protocol presented here describes a complete pipeline to analyze RNA-sequencing data from raw reads to functional analysis. The pipeline is divided into five steps: (1) quality control of the data; (2) mapping and annotation of genes; (3) statistical analysis to identify differentially expressed genes and co-expressed genes; (4) determination of the molecular degree of the perturbation of samples; and (5) functional analysis. Step 1 removes technical artifacts that may impact the quality of downstream analyses. In step 2, genes are mapped and annotated according to standard library protocols.&#160;The statistical analysis in step 3 identifies genes that are differentially expressed or co-expressed in infected samples, in comparison with non-infected ones. Sample variability and the presence of potential biological outliers are verified using the molecular degree of perturbation approach in step 4. Finally, the functional analysis in step 5 reveals the pathways associated with the disease phenotype. The presented pipeline aims to support researchers through the RNA-seq data analysis from host-pathogen interaction studies and drive future&#160;in vitro or in vivo experiments, that are essential to understand the molecular mechanism of infections.

The protocol presented here describes a complete pipeline to analyze RNA-sequencing transcriptome data from raw reads to functional analysis, including quality control and preprocessing steps to advanced statistical analytical approaches.

用于研究宿主-病原体相互作用的高通量转录组分析

Pathogens can cause a wide variety of infectious diseases. The biological processes induced by the host in response to infection determine the severity of ...

一个协议来使用基因集富集分析以确定合适的动物模型转化型研究

摘要

探索更多视频

斑马鱼 RNASeq-based 基因表达数据的样品制备与分析

转录组分析]

利用开放接入美国宇航局基因实验室平台探索航天对小鼠生理的影响

大鼠甲基 Seq 平台识别与应力接触相关的表观变化

遗传分析和基因组规模的辍学筛选以确定恶性周围神经鞘瘤小鼠模型中的治疗靶点

遗传分析和基因组规模的辍学筛选以确定恶性周围神经鞘瘤小鼠模型中的治疗靶点

遗传分析和基因组规模的辍学筛选以确定恶性周围神经鞘瘤小鼠模型中的治疗靶点

膜片钳后单细胞多重反转录聚合酶链反应

在体内病态的人类基因组建模使用斑马鱼

用于研究宿主-病原体相互作用的高通量转录组分析