Network analysis of host-pathogen protein interactions in microbe induced cardiovascular diseases.

Q2 Medicine
Nirupma Singh, Sneha Rai, Rakesh Bhatnagar, Sonika Bhatnagar
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引用次数: 1

Abstract

Large-scale visualization and analysis of HPIs involved in microbial CVDs can provide crucial insights into the mechanisms of pathogenicity. The comparison of CVD associated HPIs with the entire set of HPIs can identify the pathways specific to CVDs. Therefore, topological properties of HPI networks in CVDs and all pathogens was studied using Cytoscape3.5.1. Ontology and pathway analysis were done using KOBAS 3.0. HPIs of Papilloma, Herpes, Influenza A virus as well as Yersinia pestis and Bacillus anthracis among bacteria were predominant in the whole (wHPI) and the CVD specific (cHPI) network. The central viral and secretory bacterial proteins were predicted virulent. The central viral proteins had higher number of interactions with host proteins in comparison with bacteria. Major fraction of central and essential host proteins interacts with central viral proteins. Alpha-synuclein, Ubiquitin ribosomal proteins, TATA-box-binding protein, and Polyubiquitin-C &B proteins were the top interacting proteins specific to CVDs. Signaling by NGF, Fc epsilon receptor, EGFR and ubiquitin mediated proteolysis were among the top enriched CVD specific pathways. DEXDc and HELICc were enriched host mimicry domains that may help in hijacking of cellular machinery by pathogens. This study provides a system level understanding of cardiac damage in microbe induced CVDs.

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微生物诱发心血管疾病宿主-病原体蛋白相互作用的网络分析。
微生物cvd中hpi的大规模可视化和分析可以为了解致病性机制提供重要的见解。将CVD相关的hpi与整套hpi进行比较,可以确定CVD特有的途径。因此,我们使用Cytoscape3.5.1对cvd和所有病原体中HPI网络的拓扑特性进行了研究。使用KOBAS 3.0进行本体分析和通路分析。乳头状瘤病毒、疱疹病毒、甲型流感病毒以及鼠疫耶尔森氏菌和炭疽芽孢杆菌在整个网络(wHPI)和CVD特异性网络(cHPI)中占主导地位。预测中心病毒和分泌性细菌蛋白具有毒性。与细菌相比,中心病毒蛋白与宿主蛋白的相互作用次数更多。中心和必需宿主蛋白的主要部分与中心病毒蛋白相互作用。α -突触核蛋白、泛素核糖体蛋白、TATA-box-binding蛋白和多聚素- c和b蛋白是cvd特异性的主要相互作用蛋白。NGF、Fc epsilon受体、EGFR和泛素介导的蛋白水解是CVD特异性信号通路中富集最多的。DEXDc和HELICc富含宿主模仿结构域,可能有助于病原体劫持细胞机制。本研究对微生物诱导的心血管疾病的心脏损伤提供了系统水平的理解。
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来源期刊
In Silico Biology
In Silico Biology Computer Science-Computational Theory and Mathematics
CiteScore
2.20
自引率
0.00%
发文量
1
期刊介绍: The considerable "algorithmic complexity" of biological systems requires a huge amount of detailed information for their complete description. Although far from being complete, the overwhelming quantity of small pieces of information gathered for all kind of biological systems at the molecular and cellular level requires computational tools to be adequately stored and interpreted. Interpretation of data means to abstract them as much as allowed to provide a systematic, an integrative view of biology. Most of the presently available scientific journals focus either on accumulating more data from elaborate experimental approaches, or on presenting new algorithms for the interpretation of these data. Both approaches are meritorious.
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