Systematic Analysis of Actively Transcribed Core Matrisome Genes Across Tissues and Cell Phenotypes.
Systematic Analysis of Actively Transcribed Core Matrisome Genes Across Tissues and Cell Phenotypes.
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DOI：
10.1016/j.matbio.2022.06.003
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发表时间：
2022-06
期刊：
影响因子：
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通讯作者：
Tristen V. Tellman;Merve Dede;V. Aggarwal;Duncan Salmon;A. Naba;M. Farach-Carson
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作者：
Tristen V. Tellman;Merve Dede;V. Aggarwal;Duncan Salmon;A. Naba;M. Farach-Carson
细胞外基质(ECM)是由组织特异性生物分子组成的高度动态、有序的无细胞网络，可分为结构或核心ECM蛋白和ECM相关蛋白。细胞外基质是器官发育和功能的蓝图，当通过突变、表达改变或降解而在结构上改变时，可能会导致衰弱综合征，通常对一个组织的影响比对另一个组织的影响更大。参考FANTOM5 SSTAR(样本语义目录，转录起始和调节因子)和定义的核心矩阵ECM(Glyco)蛋白目录，我们对511个不同的人类样本进行了全面的分析，以注释定义的矩阵体单个组件的上下文特定转录。从FANTOM5在线数据库下载相对对数表达归一化SSTAR帽分析基因表达峰值数据文件，并过滤以排除所有细胞系和病变组织。启动子水平的表达值进一步分类为八个核心组织系统和三个主要的ECM类别：蛋白多糖、糖蛋白和胶原蛋白。系统聚类法和相关性分析用于确定启动子驱动的基因表达活性之间的复杂关系。整合核心母体和精选的FANTOM5 SSTAR数据创建了一个独特的工具，以组织和细胞特有的方式提供对ECM编码基因的启动子水平表达的洞察。CAP分析基因表达峰值数据的无偏聚类揭示了定义的组织系统内独特的ECM特征。组织系统之间的相关性分析揭示了ECM启动子的正相关性和负相关性，其意义程度各不相同。这一工具可以用来提供对细胞外基质成分和组织之间关系的新见解，并可以为未来关于细胞外基质在人类疾病和发育中的研究提供信息。我们邀请基质生物界继续探索和讨论这一数据集，作为关于人类ECM的更大和持续对话的一部分。交互式网络工具可以在matrixpromoterome.githeb.io上找到，其他资源可以在dx.doi.org/10.6084/m9.figShar.19794481(附图)和https://figshare.com/s/e18ecbc3ae5aaf919b78(PYTHON笔记本)上找到。
The extracellular matrix (ECM) is a highly dynamic, well-organized acellular network of tissue-specific biomolecules, that can be divided into structural or core ECM proteins and ECM-associated proteins. The ECM serves as a blueprint for organ development and function and, when structurally altered through mutation, altered expression, or degradation, can lead to debilitating syndromes that often affect one tissue more than another. Cross-referencing the FANTOM5 SSTAR (Semantic catalog of Samples, Transcription initiation And Regulators) and the defined catalog of core matrisome ECM (glyco)proteins, we conducted a comprehensive analysis of 511 different human samples to annotate the context-specific transcription of the individual components of the defined matrisome. Relative log expression normalized SSTAR cap analysis gene expression peak data files were downloaded from the FANTOM5 online database and filtered to exclude all cell lines and diseased tissues. Promoter-level expression values were categorized further into eight core tissue systems and three major ECM categories: proteoglycans, glycoproteins, and collagens. Hierarchical clustering and correlation analyses were conducted to identify complex relationships in promoter-driven gene expression activity. Integration of the core matrisome and curated FANTOM5 SSTAR data creates a unique tool that provides insight into the promoter-level expression of ECM-encoding genes in a tissue- and cell-specific manner. Unbiased clustering of cap analysis gene expression peak data reveals unique ECM signatures within defined tissue systems. Correlation analysis among tissue systems exposes both positive and negative correlation of ECM promoters with varying levels of significance. This tool can be used to provide new insight into the relationships between ECM components and tissues and can inform future research on the ECM in human disease and development. We invite the matrix biology community to continue to explore and discuss this dataset as part of a larger and continuing conversation about the human ECM. An interactive web tool can be found at matrixpromoterome.github.io along with additional resources that can be found at dx.doi.org/10.6084/m9.figshare.19794481 (figures) and https://figshare.com/s/e18ecbc3ae5aaf919b78 (python notebook).