这项工作得到了中国国家自然科学基金 (31371596)、生物技术研究中心、中国三三峡大学 (2016KBC04) 和中国江苏省自然科学基金 (BK20151424) 的支持。

1. 序列对准
<ol><li>
在单独的文献中收集真核和原核甜的氨基酸序列, 并以 FASTA 格式列出它们。下载序列从国家生物技术信息中心 (NCBI), 欧洲分子生物学实验室 (EMBL) 和日本 DNA 数据库 (DDBJ) 数据库通过相似性搜索与基本的本地对齐搜索工具 (爆破) 工具。
	<ol>
<li>在示例文件中, 收集228个假定的甜蛋白序列, 其中有两个 MtN3/saliva 域 (7 跨膜螺旋) 的真核生物和57甜蛋白序列拥有一个单一的 MtN3/saliva 域 (3 跨膜螺旋) 的原核生物13。</li>
		<li>为了简化这个过程, 从单细胞真核生物中选择35个候选的甜蛋白, 在228假定的糖果中进行系统进化树的构建。这些序列是附加的, 以便读者可以在实际数据集上进行练习。</li>
	</ol>
</li>
	<li>
通过将35甜序列输入 Clustal 欧米茄 (http://www.ebi.ac.uk/Tools/msa/clustalo/) 来对齐。
	<ol>
<li>将 FASTA 格式的蛋白质序列复制并粘贴到输入框中, 或以 FASTA 格式上载序列文件。通过单击 "步骤 1" 部分下拉菜单下的图标, 指定它们是氨基酸序列。</li>
		<li>如果需要, 请在 "步骤 2" 部分中指定输出格式和其他参数。对于本研究, 将输出格式设置为 "clustal w/o 号", 并将其他参数保留在默认设置上。在大多数情况下, 默认参数在没有任何规范的情况下工作正常。</li>
	</ol>
</li>
	<li>在 "步骤 3" 部分中提交并运行对齐方式。它可能需要从几秒钟到几分钟, 直到对齐完成。在 "结果摘要" 面板中, 右键单击 "CLUSTAL 格式的对齐方式" 下的链接, 并将对齐的序列保存为 "35.clustal" (图 1)。</li>
	<li>
在 BioEdit 中打开对齐结果文件。
	<ol>
<li>在 BioEdit 的主面板上, 单击 "序列", 然后在第一个下拉菜单中选择 "编辑情绪", 然后单击子菜单中的 "编辑残滓" (图 2)。</li>
		<li>选择与光标对齐方式左侧的突出序列 (所选序列将显示为黑色), 然后单击 "编辑" 菜单下的 "删除" 图标以删除选定的序列 (图 3)。</li>
		<li>选择并删除第一个 MtN3/saliva 域右侧的突出序列, 并将修剪的第一个 MtN3/saliva 域序列保存为 35-i. fas (图 4)。同样, 删除第二个 MtN3/saliva 域的左、右凸序列, 并将其保存为35秒。第一和第二 MtN3/saliva 域序列可以预测的节奏 (http://proteinformatics.charite.de/rhythm/inndex.php？site=helix) 或 TMHMM (http://www.cbs.dtu.dk/services/TMHMM/) 提前。</li>
	</ol>
</li>
	<li>
打开文件 35-i. fas 与兆, 并在提示时单击 "对齐"。在 "编辑" 菜单下, 单击 "全部选择", 然后单击 "选择序列";分类的名称和序列将被选中为黑色 (图 5)。
	<ol>
<li>从 "编辑" 菜单中选择 "复制", 将序列复制到剪贴板上, 然后将复制的序列粘贴到 doc 文件中。</li>
		<li>在 doc 文件中, 用 ">" 替换所有 "#", 然后删除任何不相关的字符, 将其转换为 FASTA 格式。在每个分类名称的末尾添加 "I", 将它们标记为第一个 MtN3/saliva 域序列。按照相同的方法处理第二个 MtN3/saliva 域序列, 并在每个分类名称之后添加 "II"。</li>
	</ol>
</li>
	<li>
将 FASTA 格式中的第一个和第二个 MtN3/saliva 域序列合并到 doc 文件中。
	<ol>
<li>再次将组合序列加载到 Clustal 欧米茄中, 并按照上面所述对齐序列。将结果保存为 "35 重新调整 clustal"。</li>
		<li>在 BioEdit 中打开 "35 重新排列的 clustal" 文件, 删除对齐序列的任意一端的不均匀 (突出) 氨基酸残留物, 然后将序列保存为 "35 重新排列. fas"。当警告某些非标准字符无法保存时, 单击 "是"。</li>
	</ol>
</li>
</ol>2. 系统进化树的计算
<ol><li>
打开 "35 重新调整. fas" 在兆。
	<ol>
<li>单击 "数据" 菜单并选择 "导出对齐方式", 并将 PAUP 格式 (关联) 中的对齐方式保存为 "35.nex", 以便以后在 MrBayes 中使用 (图 6)。</li>
		<li>同时, 点击超级大面板上的 "模型" 图标, 选择 "找到最佳的 DNA/蛋白质模型 (ML)", 然后单击弹出窗口中的 "确定"。单击 "计算" 开始模型搜索过程 (图 7)。将打开一个新的进度面板;此过程持续数分钟到数天, 这取决于加载序列的复杂性和计算机的性能。 注意: 显示结果的表将在模型搜索过程完成后打开 (图 8)。最小的 BIC 评分将首先列出, 其次是一系列不同的模型, 逐渐增加的 bic 分数。第一个模型 "LG + G + F" 与最小的 BIC 评分是建议模型的 ML 树基于 "35 重新调整. fas" 文件。</li>
	</ol>
</li>
	<li>
单击巨型主面板上的 "系统系统" 图标, 单击 "构造/测试最大似然树", 然后在弹出式面板上单击 "是"。将打开一个新窗口, 显示需要指定的不同参数 (图 9)。
	<ol>
<li>首先, 在系统系统的测试中设置引导值;在大多数情况下, 500 或1000是足够的。在替代模型下, 选择 "氨基酸" 作为替代类型。选择替代模型的目的是根据它们目前的状态3来估计序列之间的真正差异。</li>
		<li>选择 "LG 与 Freqs。(+ f) 模型 "(LG + F) 在" 模型/方法 "框中。在 "速率和模式" 框中, 选择 "伽玛分布" (G) 来描述跨站点的速率变化,即在缓慢变化的站点3中对变化给予更多的权重。在 "数据子集" 框中, 选择 "完全删除" 以删除包含连字符的所有列。</li>
		<li>将所有其他参数保留在其默认状态 (图 9)。在这些参数的规范之后, 单击 "计算" 图标开始计算。</li>
	</ol>
</li>
</ol>3. 系统进化树的呈现
注: 当使用巨型计算完成时, 将呈现系统进化 ML 树 (图 10)。
<ol><li>在树面板上 "文件" 图标的下拉菜单下, 选择 "保存当前会话" 以保存结果 (. mas 是默认文件类型)。在本研究中, 结果被保存为 "35.mas"。在树面板上, 将显示许多参数, 包括分支长度、树型、树状拓扑、分类名称、大小和颜色的字体, 并可设置为不同的选项。</li>
	<li>通过单击图像图标保存最终树文件, 并以不同格式保存图形或将图像复制为照片编辑的源。</li>
</ol>4. 用序列对齐法分析糖果与 SemiSWEETs 的关系
注意: 在常规序列分析中可能不需要此步骤。
<ol><li>按照上文所述, 将228真核糖和57个原核 SemiSWEETs 排列在 Clustal 欧米茄中。对齐结果可以显示在 Jalview 中, 它集成在 Clustal 欧米茄中, 并复制到照片编辑器中保存 (图 11)。 注意: 在示例对齐方式中, 某些 SemiSWEETs 从α Proteobacteria 与甜序列的第一个 MtN3/saliva 域对齐, 而 SemiSWEETs 从 Methanobacteria (古菌) 与甜序列的第二 MtN3/saliva 域对齐。</li>
</ol>5. MrBayes 的系统进化树结构
<ol><li>对于 MrBayes 的贝叶斯推断, 打开 MrBayes 可执行文件, 一个 DOS 接口将出现在一个新窗口中。第一步是读取关联数据ﬁle。在提示后输入 "执行 35.nex" (请记住将35.nex 文件保存在 MrBayes 可执行文件的同一目录中, 或者在上载文件之前指出它的路径)。"成功读取矩阵" 消息将显示在最后列出的分类群之后 (图 12)。35.nex 文件已经准备好保存在兆 (见上文 2.1)。</li>
	<li>
设置进化模型。
	<ol>
<li>提示后, 键入 "prset aamodelpr = 固定 (lg)";lset 率 = g "。"lg" 和 "g" 对应于 "lg" 和 "g" 模型, 这是设置在兆。成功设置模型后, 在提示后键入 "mcmc nchains = 4 ngen = 500万"。使用 "nchains=4" 词条意味着一个总数量的一个冷链和三热链的大都会耦合。"ngen = 500万" 意味着运行500万代大都市耦合为冷和热链子的汇合。在这项研究中, 0.01 以下的分裂频率的平均标准偏差被认为是热冷链的收敛性。</li>
		<li>请注意, 在过程开始时不能准确预测 ngen 数, 通常需要根据分割频率的平均标准偏差的变化进行调整。此外, 每次基于相同数据运行程序时, ngen 的收敛数可能会有所不同。</li>
	</ol>
</li>
	<li>
运行分析:由于输入数据的复杂性和计算机的性能, 这一步持续时间从几分钟到数天。完成预设计算后, 提示将询问 "继续分析 (是/否)"？如果在提示之后键入 "no", 计算将停止 (图 13), 否则它将继续计算在输入的后代数之后。计算完成后 (平均分频率 < 0.01 或0.05 的标准偏差), 在查询提示符后键入 "否" 以停止计算。 注: 0.01 是一个严格的标准, 0.05 是适度的, 通常是足够的。</li>
	<li>
汇总示例:在提示汇总模型参数样本后, 键入 "废油坑" (图 14)。然后在提示汇总树样后键入 "sumt relburnin = 是 burninfrac = 0.25"。关于系统进化树构造的详细信息将显示如图 15所示, 后面是两个树形图, 这些数字将出现在屏幕上的 ASC II 代码中, 一个显示分支可信度, 另一个显示分支长度。同时, 将自动保存名称为 "35" 的树文件。</li>
	<li>要更好地显示系统结构树, 请使用 FigTree 工具 (http://tree.bio.ed.ac.uk/software/figtree/) 打开 "35...............更便于读者阅读。</li>
</ol>

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作者没有什么可透露的。

以核苷酸或氨基酸序列8为基础的进化树, 在生物研究中越来越受欢迎。一般情况下, 有三个关键阶段的实践包括序列对准, 对齐序列的评估与适当的方法或算法, 并可视化的计算结果作为系统进化树。在本研究中, 进行了三轮序列对准: 首先, 甜蛋白序列, 包括第一和第二 MtN3/saliva 域, 被排列;其次, 每个单独的 MtN3/saliva 域序列的糖果作为一个独立的分类收集和排列在一起;最后, 甜序?...

DNA 或 RNA 序列携带遗传信息的基础表型, 可以分析通过生理和生化方法或观察通过形态学和化石证据。从某种意义上说, 遗传信息比评价外部表型更为可靠, 因为前者是后者的基础。在进化研究中, 化石证据是非常直接和令人信服的。然而, 许多有机体, 例如微生物, 在长的地质时代几乎没有机会形成化石。因此, 分子信息, 如核苷酸序列和氨基酸序列从相关现存的有机体是有价值的探索进化关系1。在本研究中, 为需要自行构建系统进化树的新来者提供了基本的系统发育知识和易于学习的协议的简单介绍。
DNA (核苷酸) 和蛋白质 (氨基酸) 序列可以用来推断同源基因, 细胞器, 甚至有机体之间的系统发育关系2。DNA 序列更可能受到进化过程中变化的影响。相比之下, 由于核苷酸序列中的同义突变不会导致氨基酸序列的突变, 氨基酸序列的稳定性要稳定得多。因此, DNA 序列是有用的比较密切相关的生物体同源基因, 而氨基酸序列是适当的同源基因从远亲相关的生物3。
系统发育分析始于氨基酸或核苷酸序列4从一个带注释的基因组测序数据库中检索到的5 FASTA 格式,即假定或表达的蛋白质序列, RNA 序列或 DNA 序列。值得注意的是, 收集高质量的序列进行分析是至关重要的, 只有同源序列可以用来分析系统的系统关系。许多不同的平台, 如 Clustal W, Clustal X, 肌肉, T 咖啡, MAFFT, 可用于序列对准。最广泛使用的是 Clustal 欧米茄6,7 (http://www.ebi.ac.uk/Tools/msa/clustalo/), 可以在线使用或可以免费下载。对齐工具有许多参数, 用户可以在开始对齐之前进行调整, 但默认参数在大多数情况下都很好地工作。完成该过程后, 对齐的序列应以正确的格式保存到下一步。然后, 应使用编辑软件 (如 BioEdit) 对其进行编辑或修剪, 因为巨型系统的进化树结构要求序列的长度相等 (包括氨基酸缩写和连字符)。在对齐序列中, 任何没有氨基酸或核苷酸的位置都用连字符 "-" 表示。一般来说, 在对齐的任何一端的突起的氨基酸或核苷酸都应该被移除。此外, 在对齐中包含不协调的序列的列可以被删除, 因为它们传递的信息很少, 有时会给出混淆或错误的信息3。包含一个或多个连字符的列可以在此时或在以后的树结构阶段删除。或者, 它们可以用于系统系统计算。当序列对齐和修剪完成后, 对齐序列应以 FASTA 格式或所需格式保存, 供以后使用。
许多软件平台使用不同的方法或算法提供树构造函数。通常, 这些方法可以分为距离矩阵法或离散数据法两种。距离矩阵方法计算简单、快速, 而离散数据方法复杂、耗时。对于与氨基酸或核苷酸序列标识高度共享的非常紧密相关的类群, 距离矩阵法 (邻域: 新泽西州;加权对群法与算术平均值: UPGMA) 是适当的;对于远亲群, 一种离散数据方法 (最大似然: ML;最大吝啬: MP;贝叶斯推断) 是最佳3,8。本研究将兆 (6.0.6) 和贝叶斯推理 (MrBayes 3.2) 的 ML 方法应用于构造系统进化树9。理想情况下, 当使用适当的模型和参数时, 从不同方法得出的结果可能是一致的, 因此它们更可靠、更有说服力。
对于使用巨型10构建的 ML 系统进化树, 必须将 FASTA 格式的对齐序列文件上载到程序中。然后, 第一步是为上载的数据选择最佳替代模型。所有可用替换模型都是根据上传的序列进行比较, 最后的分数将显示在结果表中。选择具有最小贝叶斯信息准则 (BIC) 评分 (表中第一列) 的模型, 根据推荐的模型设置 ML 参数, 并开始计算。计算时间从几分钟到数天不等, 具体取决于加载数据的复杂性 (序列的长度和分类数) 以及运行程序的计算机的性能。计算完成后, 将在新窗口中显示系统系统树。将文件另存为 "文件名. 垫"。设置参数以指定树的外观后, 再保存一次。利用该方法可以生成发布级系统树形图。
对于 MrBayes11的树结构, 第一步是将对齐序列 (通常以 FASTA 格式列出) 转换为关联格式 (. 作为文件类型的执行)。将 FASTA 文件转换为关联格式可以在兆超中进行处理。接下来, 将关联格式的对齐序列可以上载到 MrBayes 中。当文件成功上载时, 指定树计算的详细参数。这些参数包括氨基酸替代模型、变异率、马尔可夫链蒙特卡罗 (MCMC) 耦合的链数、ngen 数、分裂频率的平均标准偏差等细节。指定这些参数后, 开始计算。最后, 在 ASC II 代码中的两个树图, 一个显示分支可信度, 另一个显示分支长度, 将显示在屏幕上。
树结果将自动保存为 "FileName.nex.con"。此树文件可以由 FigTree 打开和编辑, 并且在 FigTree 中显示的图形可以进一步修改以使其更适合发布。
在本研究中, 分析了228甜蛋白, 包括单细胞真核生物35糖和 57 SemiSWEETs 的原核生物。糖和 SemiSWEETs 的特点是葡萄糖, 果糖, 或蔗糖转运体横跨膜12,13。系统发育分析表明, 含有甜食的两个 MtN3/saliva 域可能来源于细菌甜和 archaeon14的进化融合。

<table><tbody><tr><td>Adobe Illustration</td><td></td><td></td><td>a graphical tool developed by Adobe Systems Software Ireland Ltd. Copyright &amp;copy; 2017</td></tr><tr><td>BioEdit</td><td></td><td></td><td>a biological sequence alignment editor written for Windows 95/98/NT/2000/XP/7. Copyright &amp;copy; Tom Hall</td></tr><tr><td>Clustal Omega</td><td></td><td></td><td>a package for making multiple sequence alignments of amino acid or nucleotide sequences.&amp;nbsp; http://www.clustal.org/</td></tr><tr><td>CorelDRAW</td><td></td><td></td><td>a graphic design software. Copyright &amp;copy; 2017 Corel Corporation</td></tr><tr><td>FigTree</td><td></td><td></td><td>a graphical viewer of phylogenetic trees designed by the University of Edinburgh</td></tr><tr><td>MEGA</td><td></td><td></td><td>MolecularEvolutionary Genetics Analysis version6.0 http://www.megasoftware.net/home</td></tr><tr><td>MrBayes</td><td></td><td></td><td>an Bayesian phylogenetic inference tool</td></tr><tr><td>NVIDIA</td><td></td><td></td><td>a company designs graphics processing units (GPUs) for the gaming and professional markets. Corporation Copyright &amp;copy; 2017</td></tr><tr><td>PAUP</td><td></td><td></td><td>Phylogenetic Analysis Using Parsimony. David Swofford&#039;s program implements the maximum likelihood method under a number of nucleotide models.</td></tr><tr><td>Photoshop</td><td></td><td></td><td>a raster graphics editor developed and published by Adobe Systems Software Ireland Ltd. Copyright &amp;copy; 2017</td></tr><tr><td>RHYTHM</td><td></td><td></td><td>a knowledge based prediction of hekix contacts. Charit&amp;eacute; Berlin &amp;ndash; Protein Formatics Group - Copyright 2007-2009</td></tr><tr><td>TMHMM</td><td></td><td></td><td>a tool for prediction of transmembrane helices in proteins. http://www.cbs.dtu.dk/services/TMHMM/</td></tr><tr><td>Compter</td><td></td><td></td><td>4 GB memory, Core 2 or above CPU. Windows 7, Windows 10</td></tr></tbody></table>

using phylogenetic analysis to investigate eukaryotic gene origin

系统发育分析使用核苷酸或氨基酸序列或其他参数, 如域序列和三维结构, 建立一棵树, 以显示不同分类群之间的进化关系 (归类单位) 在分子水平。系统进化分析也可以用来研究单个分类中的领域关系, 特别是对于那些经历了形态学和生理学上发生了实质性变化的有机体, 但由于研究人员缺乏化石证据,生物体的长期演化史或石化的稀缺性。
在本文中, 描述了一个详细的协议, 使用系统进化方法, 包括氨基酸序列对 Clustal 欧米茄, 和随后的系统进化树建设使用最大似然 (ML) 的分子进化遗传学分析 (兆) 和贝叶斯推理通过 MrBayes。为了研究真核糖的起源,最终将出口转运体 (甜) 基因, 分析了228种甜食, 包括35种来自单细胞真核生物的甜蛋白和来自原核生物的57甜蛋白。有趣的是, SemiSWEETs 被发现在原核生物, 但糖果被发现在真核生物。用理论上不同的方法构建的两种系统进化树一致认为, 第一真核甜基因可能来源于细菌甜基因和古细菌甜基因的融合。值得注意的是, 一个人应该谨慎地得出一个结论, 只基于系统进化分析, 虽然它是有用的, 以解释不同的分类群之间的潜在关系, 这是很难甚至不可能辨别通过实验手段.

系统进化树表明, 所有的第一个 MtN3/saliva 域的35甜序列聚集作为一个分支和第二 MtN3/saliva 域的甜序列聚集作为另一个分支。此外, 糖果和 SemiSWEETs 的对准结果表明, 有些 SemiSWEETs 从α Proteobacteria 与甜序列的第一 MtN3/saliva 域对齐, 而 SemiSWEETs 从 Methanobacteria (古菌) 与第二 MtN3/saliva 对齐。甜序列的域。这些结果共同表明, 含有甜食的两个 MtN3/saliva 域可能来源于细菌甜和 archaeon<su...

Watch this Scientific Journal Video about Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin at JoVE.com

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

本文介绍了一种基于原核生物中的真核生物和 SemiSWEETs 的糖序列同源性建立系统进化树的方法。系统进化分析是解释同源蛋白或不同生物体基因之间进化相关性的有用工具。

应用系统进化分析研究真核基因的起源

Phylogenetic analysis uses nucleotide or amino acid sequences or other parameters, such as domain sequences and three-dimensional structure, to construct ...

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15.7K 次观看.  China Three Gorges University. 本文介绍了一种基于原核生物中的真核生物和 SemiSWEETs 的糖序列同源性建立系统进化树的方法。系统进化分析是解释同源蛋白或不同生物体基因之间进化相关性的有用工具。

视频: Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Routine and systematic use of bacterial whole-genome sequencing (WGS) is enhancing the accuracy and resolution of epidemiological investigations carried out by Public Health laboratories and regulatory agencies. Large volumes of publicly available WGS data can be used to study pathogenic populations at a large scale. Recently, a freely available computational platform called ProkEvo was published to enable reproducible, automated, and scalable hierarchical-based population genomic analyses using bacterial WGS data. This implementation of ProkEvo demonstrated the importance of combining standard genotypic mapping of populations with mining of accessory genomic content for ecological inference. In particular, the work highlighted here used ProkEvo-derived outputs for population-scaled hierarchical analyses using the R programming language. The main objective was to provide a practical guide for microbiologists, ecologists, and epidemiologists by showing how to: i) use a phylogeny-guided mapping of hierarchical genotypes; ii) assess frequency distributions of genotypes as a proxy for ecological fitness; iii) determine kinship relationships and genetic diversity using specific genotypic classifications; and iv) map lineage differentiating accessory loci. To enhance reproducibility and portability, R markdown files were used to demonstrate the entire analytical approach. The example dataset contained genomic data from 2,365 isolates of the zoonotic foodborne pathogen Salmonella Newport. Phylogeny-anchored mapping of hierarchical genotypes (Serovar -&#62; BAPS1 -&#62; ST -&#62; cgMLST) revealed the population genetic structure, highlighting sequence types (STs) as the keystone differentiating genotype. Across the three most dominant lineages, ST5 and ST118 shared a common ancestor more recently than with the highly clonal ST45 phylotype. ST-based differences were further highlighted by the distribution of accessory antimicrobial resistance (AMR) loci. Lastly, a phylogeny-anchored visualization was used to combine hierarchical genotypes and AMR content to reveal the kinship structure and lineage-specific genomic signatures. Combined, this analytical approach provides some guidelines for conducting heuristic bacterial population genomic analyses using pan-genomic information.

This analytical computational platform provides practical guidance for microbiologists, ecologists, and epidemiologists interested in bacterial population genomics. Specifically, the work presented here demonstrated how to perform: i) phylogeny-guided mapping of hierarchical genotypes; ii) frequency-based analysis of genotypes; iii) kinship and clonality analyses; iv) identification of lineage differentiating accessory loci.

细菌种群中分层基因型和辅助基因组位点的启发式挖掘

Routine and systematic use of bacterial whole-genome sequencing (WGS) is enhancing the accuracy and resolution of epidemiological investigations carried ...

科研

遗传学

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

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 ...

生物学

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Distilling and reporting large datasets, such as whole genome or transcriptome data, is often a daunting task. One way to break down results is to focus on one or more gene families that are significant to the organism and study. In this protocol, we outline bioinformatic steps to generate a phylogeny and to quantify the expression of genes of interest. Phylogenetic trees can give insight into how genes are evolving within and between species as well as reveal orthology. These results can be enhanced using RNA-seq data to compare the expression of these genes in different individuals or tissues. Studies of molecular evolution and expression can reveal modes of evolution and conservation of gene function between species. The characterization of a gene family can serve as a springboard for future studies and can highlight an important gene family in a new genome or transcriptome paper.

The purpose of this protocol is to investigate the evolution and expression of candidate genes using RNA sequencing data.

使用RNA-seq研究分子进化和基因表达的生物信息学管道

Distilling and reporting large datasets, such as whole genome or transcriptome data, is often a daunting task. One way to break down results is to focus ...

An Integrated Approach for Microprotein Identification and Sequence Analysis

Next-generation sequencing (NGS) has propelled the field of genomics forward and produced whole genome sequences for numerous animal species and model organisms. However, despite this wealth of sequence information, comprehensive gene annotation efforts have proven challenging, especially for small proteins. Notably, conventional protein annotation methods were designed to intentionally exclude putative proteins encoded by short open reading frames (sORFs) less than 300 nucleotides in length to filter out the exponentially higher number of spurious noncoding sORFs throughout the genome. As a result, hundreds of functional small proteins called microproteins (&#60;100 amino acids in length) have been incorrectly classified as noncoding RNAs or overlooked entirely.
Here we provide a detailed protocol to leverage free, publicly available bioinformatic tools to query genomic regions for microprotein-coding potential based on evolutionary conservation. Specifically, we provide step-by-step instructions on how to examine sequence conservation and coding potential using Phylogenetic Codon Substitution Frequencies (PhyloCSF) on the user-friendly University of California Santa Cruz (UCSC) Genome Browser. Additionally, we detail steps to efficiently generate multiple species alignments of identified microprotein sequences to visualize amino acid sequence conservation and recommend resources to analyze microprotein characteristics, including predicted domain structures. These powerful tools can be used to help identify putative microprotein-coding sequences in noncanonical genomic regions or to rule out the presence of a conserved coding sequence with translational potential in a noncoding transcript of interest.

The protocol described here provides detailed instructions on how to analyze genomic regions of interest for microprotein-coding potential using PhyloCSF on the user-friendly UCSC Genome Browser. Additionally, several tools and resources are recommended to further investigate sequence characteristics of identified microproteins to gain insight into their putative functions.

一种用于微蛋白鉴定和序列分析的综合方法

Next-generation sequencing (NGS) has propelled the field of genomics forward and produced whole genome sequences for numerous animal species and model ...

Evolutionary Relationships

Humans have been attempting to properly classify living things since Aristotle made the first attempt during the 4th century BC. Aristotle&#8217;s system was improved upon during the Renaissance and then, subsequently, by Carolus Linnaeus in the mid 1700&#8217;s. These more formal classification and organization systems grouped species by their physical similarity to one another. For example, all vertebrates have a backbone, but invertebrates do not. Traits like the backbone are called synapomorphies, which are traits that are shared by a group of organisms, presumably because they were derived from a common ancestor. As we will explore, this method has been shown to have limitations and has more recently been amended to include genetic analysis. Still, scientists construct trees called dendrograms to create a visual representation of how species are related to one another and share common ancestors. These dendrograms can aid in our understanding of the evolutionary processes that drive these relationships. Genetic comparisons have added an important tool guiding the analysis of evolutionary relationships.
<h4>Types of Dendrogram</h4>
A type of dendrogram, called a cladogram, depicts the hypothetical genealogical relationships between species with the tips (or leaves) of the tree representing a species and the branches showing how species are related to each other. A slightly more complicated type of tree, called a phylogram, differs from a cladogram in that the branches leading to the species are of different lengths. The length of a branch in this type of tree represents the degree of change between species: the longer the branch, the more time since the species have diverged from a common ancestor. In both types of tree, the common ancestor of a group of species is indicated by a node, which is the point where a series of branches meet. Species that are more closely related to each other (most recently shared a common ancestor) will be located closest to the node. The two species that share a node are called a sister group1.
<h4>Understanding Evolutionary Relationships using Genetic Data</h4>
Historically, cladograms were constructed by comparing the morphology (physical structure) of organisms. This method is still practiced but the techniques have been modernized to include comparison of DNA (deoxyribonucleic acid) sequences between species. Using DNA for building trees has several advantages over relying solely on morphology, including being able to calculate an estimate of how long ago different species shared a common ancestor1. However, using DNA is not always feasible, especially when trees include extinct organisms. DNA is best found in soft tissues, which are not preserved during the fossilization process, and therefore it is uncommon for a DNA sample of an extinct species to be available.
DNA is passed on from parents to their offspring in hereditary units called genes. The nucleotide (A, G, C, and T) sequence of genes found in different species are frequently quite similar, likely due to their having come from a common ancestor. This fact allows researchers to align sequences from different species with one another to build the trees described above. Species with more similarity between their nucleotide sequences will be placed next to each other in a tree, and species with less sequence similarity will be placed further apart from each other.
Bioinformatics are the tools used by biologists to analyze large datasets using a combination of computer science, mathematical modeling, and statistics. One such tool is called BLAST (Basic Local Alignment Search Tool), which can be used to quickly search the entire genome of any species that is available in the NCBI (National Center for Biotechnology Information) database2. The NCBI database combines several different databases that hold different types of DNA sequence information. The process of a BLAST search includes complex computer algorithms, but basically, BLAST aligns sequences of each nucleotide base from a submitted DNA sequence (known as the query sequence) with sequences in the data base that most closely match it. The DNA sequences that are found will be listed in order of similarity to the sequence in question, and will therefore be from species closely related to the species containing the query gene. This comparison may or may not depict the actual evolutionary relationship between species because genes evolve at different rates. Additionally, genomes sometimes contain more than one instance of a similar sequence.
Comparison of DNA sequences of genes is valuable beyond consideration of evolutionary relationships. Frequently, genes are identified in model organisms, such as the fruit fly, Drosophila melanogaster, or the mouse3. Integral to studying a gene, the function of its product is commonly identified and analyzed. If a researcher is interested in studying that function in a different organism (humans for example), BLAST or other bioinformatic tools can be used to find candidate genes based on their similarities to the genes of known function from model organisms.
Human genes can also be used as the starting point to find homologs in model organisms. In fact, human disease research depends heavily on this. Once a human gene of interest is identified, mice can be genetically manipulated to have the homologous gene disrupted, or &#8220;knocked out,&#8221; creating a model of the human disease that can be studied in order to understand and treat the disease. There are many of these mouse strains currently available. For example, there is a mouse model for human Cystic Fibrosis (CF) called the Cftr knockout mouse and another modeling atherosclerosis, called the Apoe knockout3. 
 
<h4>References:</h4>
<ol class="references">
	<li>Annenberg Learner. Online Textbook: Unit 3 Evolution and Phylogenetics. Rediscovering Biology. [Online] 2017. [Cited: August 23, 2018.] https://www.learner.org/courses/biology/textbook/compev/compev_3.html.</li>
	<li>National Center for Biotechnology Information, U.S. National Library of Medicine. BLAST: Basic Local Alignment Search Tool. [Online] https://blast.ncbi.nlm.nih.gov/Blast.cgi.</li>
	<li>National Human Genome Research Institute. Background on Mouse as a Model Organism. National Human Genome Research Institute. [Online] December 2002. [Cited: August 23, 2018.] <a href="https://www.genome.gov/10005834/background-on-mouse-as-a-model-organism/" target="__blank">https://www.genome.gov/10005834/background-on-mouse-as-a-model-organism/</a>.</li>
</ol>

Evolutionary Relationships: Using BLAST to Test Evolutionary Hypotheses - Concept

进化关系

Humans have been attempting to properly classify living things since Aristotle made the first attempt during the 4th century BC. Aristotle&#8217;s system ...

教育

JoVE Lab Manual

进化

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse organisms.
Genome comparison can reveal three levels of evolutionary relationships. The first level provides deep insight into sequences and protein domains that are conserved across diverse groups of organisms, such as humans and fishes. The second level increases the resolution further to identify the unique DNA elements present in the closely related species, such as humans and chimpanzees. The third level with even higher data resolution distinguishes the genetic differences within a species, such as different variants and subtypes of an organism. This high-level resolution may identify mutations particular to individual microbial strains or clusters of infected cases, helping to track the disease outbreaks.
<h4>DNA sequencing tools</h4>
Several methods can be used to obtain the DNA sequence data required to deduce evolutionary relationships. Among them, whole-genome sequencing or WGS is a widely used technique. It provides high-resolution data extremely helpful to analyze mutations and conserved sequences among several organisms. It can also identify the cause of genetic disorders by comparing the DNA sequence of affected individuals to those of other unaffected subjects.
<h4>Data analysis tools</h4>
The data obtained by WGS or similar sequencing methods is analyzed by appropriate software tools to deduce evolutionary relationships. Molecular Evolutionary Genetics Analysis (MEGA) is one of the most widely used software tools. The programs present in the MEGA, such as assembly sequence alignment, building evolutionary trees, estimating genetic distances, and computation of evolutionary time trees, allow the users to curate and interpret the raw data obtained from sequencing techniques.

通过基因组比较的进化关系

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is ...

分子生物学

关键术语与概念

Genomic DNA in Eukaryotes

Eukaryotes have large genomes compared to prokaryotes. To fit their genomes into a cell, eukaryotic DNA is packaged extraordinarily tightly inside the nucleus. To achieve this, DNA is tightly wound around proteins called histones, which are packaged into nucleosomes that are joined by linker DNA and coil into chromatin fibers. Additional fibrous proteins further compact the chromatin, which is recognizable as chromosomes during certain phases of cell division.

<h4>The Human Genome Measured in Meters</h4>

Most cells in the human body contain about 6 billion base pairs of DNA packaged into 23 pairs of chromosomes. It is hard to imagine exactly how much DNA these numbers represent, and therefore it is difficult to grasp how densely packed DNA must be to fit into a cell. We can gain some insight by expressing the genome in terms of length. If we were to arrange the DNA of a single diploid cell into a straight line, it would be about two meters long!

Note that humans do not have unusually large genomes. Many fish, amphibians, and flowering plants have much larger genomes than humans. For example, the haploid genome of the Japanese flowering plant Paris japonica contains about 50 times more DNA than the human haploid genome. These figures emphasize the astonishing work that histones and other chromatin remodeling proteins must do to package DNA.

Compaction of GenomIc DNA in Eukaryotes

真核生物基因组DNA

Eukaryotes have large genomes compared to prokaryotes. To fit their genomes into a cell, eukaryotic DNA is packaged extraordinarily tightly inside the ...

细胞结构与功能

Eukaryotic Evolution

The endosymbiont theory is the most widely accepted theory of eukaryotic evolution; however, its progression is still somewhat debated. According to the nucleus-first hypothesis, the ancestral prokaryote first evolved a membrane to enclose DNA and form the nucleus. Conversely, the mitochondria-first hypothesis suggests that the nucleus was formed after endosymbiosis of mitochondria.
Contrary to the endosymbiont theory, the eukaryote-first hypothesis proposes that the simpler prokaryotic and archaeal life forms evolved from the already existing complex eukaryotic cells.
Evidence for Endosymbiont Theory 
Evidence for the endosymbiont theory comes from the many similarities that the eukaryotic organelles, like mitochondria and chloroplast, share with prokaryotic cells. Like the prokaryotic cells, mitochondria and chloroplasts contain circular, double-stranded genomes devoid of histones. These organelles also divide by the process of fission, similar to bacterial cell division, rather than by mitosis. Of the two membranes that surround them, the inner membranes resemble the bacterial plasma membrane in lipid and protein composition. Finally, the structure of genes, genetic code, and translational machinery (such as ribosomes) in these organelles are more similar to the prokaryotic machinery than eukaryotic.
LECA - Last Eukaryotic Common Ancestor
All eukaryotes are believed to have evolved from the Last Eukaryotic Common Ancestor or LECA about 1100 to 2300 million years ago. It is thought that LECA had already acquired endosymbionts like mitochondria and chloroplast by this stage. The new organelles made these ancestral cells more efficient and powerful, which allowed them to evolve novel characteristics such as multicellularity and cellular specialization. Their large cell size and internal compartmentalization also allowed their genomes to increase in size during the course of evolution. However, only a small portion of this genome encoded proteins; the major portion was non-coding DNA mostly involved in regulatory processes. The ability to turn genes on and off when required was key to cell specialization and responding to environmental changes. Phylogenetic and comparative genomic studies show five to six eukaryotic supergroups that evolved from LECA and later diverged into the plethora of life forms we see today.

真核生物进化

The endosymbiont theory is the most widely accepted theory of eukaryotic evolution; however, its progression is still somewhat debated. According to the ...

细胞生物学

Unit 1

Cells, Genomes, and Evolution

Gene Evolution - Fast or Slow?

The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code for a protein might experience high selection pressure, because any changes in their sequence are likely to result in a protein which is less capable of performing its function optimally. However, occasionally a mutation in one of these regions will result in a beneficial outcome that contributes to the overall fitness of the organism, and such mutations often persist and may even become fixed in populations. When comparing the frequency of these mutation events to the relatively regular changes seen in non-coding sequences, this is exceedingly rare, and so in general coding regions are considered as evolving slowly.
It is also true that there is a measurable amount of variation in the levels of sequence conservation within coding sequences, and this is seen across all organisms. For instance, take the example of a receptor protein. Such proteins typically have different regions that may perform functions such as ligand binding, or intracellular signaling, or membrane integration. In this case, a mutation in the region that is involved in ligand binding may produce a protein that is less efficient at binding the ligand. Therefore, selection pressure would likely be high on the particular nucleotides coding for this part of the protein. However, in the section of the protein which spans the membrane, there may be less effect seen if an amino acid substitution occurs, and therefore lower levels of selection pressure. Under these conditions, we might see that two regions of the same protein-coding gene might have different rates of evolution.
Sequencing Genes or Genomic Regions to Build Phylogenies
This variation in the speed of genome evolution over different regions can be studied to answer questions about evolutionary relationships. Genes and gene regions can be selected and sequenced over groups of individuals to answer questions as narrow as &#8220;are these populations potentially different species?&#8221; or as broad as &#8220;how do these phyla place into the tree of life?&#8221;. For the former, selecting a gene that has a relatively lightly conserved region would help to identify population-level differences. Conversely, to answer questions over groups as diverse as phyla, a highly conserved gene region may provide enough homology to produce a phylogeny of such groups. Commonly used regions for molecular phylogenetic analyses such as these include ribosomal rRNA genes (such as 16s rRNA, 18s rRNA, or 28s rRNA), or genomic regions known as ITS (Internal Transcribed Spacers, I or II) which sit between the ribosomal rRNA subunit genes.

Gene Evolution and Sequencing Genomic Regions to Build Phylogenies

基因进化 - 快还是慢？

The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain ...