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A systems-based framework to computationally describe putative transcription factors and signaling pathways regulating glycan biosynthesis
Beilstein Journal of Organic Chemistry ( IF 2.2 ) Pub Date : 2021-07-22 , DOI: 10.3762/bjoc.17.119
Theodore Groth 1 , Rudiyanto Gunawan 1 , Sriram Neelamegham 1, 2, 3
Affiliation  

Glycosylation is a common posttranslational modification, and glycan biosynthesis is regulated by a set of glycogenes. The role of transcription factors (TFs) in regulating the glycogenes and related glycosylation pathways is largely unknown. In this work, we performed data mining of TF–glycogene relationships from the Cistrome Cancer database (DB), which integrates chromatin immunoprecipitation sequencing (ChIP-Seq) and RNA-Seq data to constitute regulatory relationships. In total, we observed 22,654 potentially significant TF–glycogene relationships, which include interactions involving 526 unique TFs and 341 glycogenes that span 29 the Cancer Genome Atlas (TCGA) cancer types. Here, TF–glycogene interactions appeared in clusters or so-called communities, suggesting that changes in single TF expression during both health and disease may affect multiple carbohydrate structures. Upon applying the Fisher’s exact test along with glycogene pathway classification, we identified TFs that may specifically regulate the biosynthesis of individual glycan types. Integration with Reactome DB knowledge provided an avenue to relate cell-signaling pathways to TFs and cellular glycosylation state. Whereas analysis results are presented for all 29 cancer types, specific focus is placed on human luminal and basal breast cancer disease progression. Overall, the article presents a computational approach to describe TF–glycogene relationships, the starting point for experimental system-wide validation.

中文翻译:

基于系统的框架,用于计算描述假定的转录因子和调节聚糖生物合成的信号通路

糖基化是一种常见的翻译后修饰,聚糖生物合成受一组糖原调控。转录因子 (TF) 在调节糖原和相关糖基化途径中的作用在很大程度上是未知的。在这项工作中,我们对 Cistrome 癌症数据库 (DB) 中的 TF-糖基因关系进行了数据挖掘,该数据库整合了染色质免疫沉淀测序 (ChIP-Seq) 和 RNA-Seq 数据以构成调控关系。总的来说,我们观察到 22,654 个潜在重要的 TF-糖基因关系,其中包括涉及 526 个独特的 TF 和跨越 29 种癌症基因组图谱 (TCGA) 癌症类型的 341 个糖基因的相互作用。在这里,TF-糖基因相互作用出现在集群或所谓的社区中,这表明在健康和疾病期间单个 TF 表达的变化可能会影响多种碳水化合物结构。在应用 Fisher 精确检验和糖原途径分类后,我们确定了可能特异性调节单个聚糖类型生物合成的转录因子。与 Reactome DB 知识的整合提供了将细胞信号通路与转录因子和细胞糖基化状态相关联的途径。虽然针对所有 29 种癌症类型提供了分析结果,但特别关注人类管腔和基底乳腺癌的疾病进展。总体而言,本文提出了一种描述 TF-糖基因关系的计算方法,这是实验性系统范围验证的起点。我们确定了可能专门调节单个聚糖类型生物合成的转录因子。与 Reactome DB 知识的整合提供了将细胞信号通路与转录因子和细胞糖基化状态相关联的途径。虽然针对所有 29 种癌症类型提供了分析结果,但特别关注人类管腔和基底乳腺癌的疾病进展。总体而言,本文提出了一种描述 TF-糖基因关系的计算方法,这是实验性系统范围验证的起点。我们确定了可能专门调节单个聚糖类型生物合成的转录因子。与 Reactome DB 知识的整合提供了将细胞信号通路与转录因子和细胞糖基化状态相关联的途径。虽然针对所有 29 种癌症类型提供了分析结果,但特别关注人类管腔和基底乳腺癌的疾病进展。总体而言,本文提出了一种描述 TF-糖基因关系的计算方法,这是实验性系统范围验证的起点。特别关注人类管腔和基底乳腺癌疾病进展。总体而言,本文提出了一种描述 TF-糖基因关系的计算方法,这是实验性系统范围验证的起点。特别关注人类管腔和基底乳腺癌疾病进展。总体而言,本文提出了一种描述 TF-糖基因关系的计算方法,这是实验性系统范围验证的起点。
更新日期:2021-07-22
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