scholarly journals Improving the Understanding of Pathogenesis of Human Papillomavirus 16 via Mapping Protein-Protein Interaction Network

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Yongcheng Dong ◽  
Qifan Kuang ◽  
Xu Dai ◽  
Rong Li ◽  
Yiming Wu ◽  
...  
Keyword(s):  
Human Papillomavirus ◽  
Protein Interaction ◽  
Cell Cycle Progression ◽  
Interaction Network ◽  
Support Vector ◽  
Human Papillomavirus 16 ◽  
Protein Protein Interaction ◽  
Topological Features ◽  
Broad Array ◽  
Human Proteins

The human papillomavirus 16 (HPV16) has high risk to lead various cancers and afflictions, especially, the cervical cancer. Therefore, investigating the pathogenesis of HPV16 is very important for public health. Protein-protein interaction (PPI) network between HPV16 and human was used as a measure to improve our understanding of its pathogenesis. By adopting sequence and topological features, a support vector machine (SVM) model was built to predict new interactions between HPV16 and human proteins. All interactions were comprehensively investigated and analyzed. The analysis indicated that HPV16 enlarged its scope of influence by interacting with human proteins as much as possible. These interactions alter a broad array of cell cycle progression. Furthermore, not only was HPV16 highly prone to interact with hub proteins and bottleneck proteins, but also it could effectively affect a breadth of signaling pathways. In addition, we found that the HPV16 evolved into high carcinogenicity on the condition that its own reproduction had been ensured. Meanwhile, this work will contribute to providing potential new targets for antiviral therapeutics and help experimental research in the future.

scholarly journals Comparing protein-protein interaction networks of SARS-CoV-2 and (H1N1) influenza using topological features

2021 ◽  
Author(s):  
Hakimeh Khojasteh ◽  
Alireza Khanteymoori ◽  
Mohammad Hossein Olyaee
Keyword(s):  
Dimensionality Reduction ◽  
Power Law ◽  
Protein Interaction ◽  
Interaction Network ◽  
H1n1 Influenza ◽  
Centrality Measures ◽  
Essential Proteins ◽  
Protein Protein Interaction ◽  
Topological Features ◽  
High Level

Background: SARS-CoV-2 pandemic first emerged in late 2019 in China. It has since infected more than 183 million individuals and caused about 4 million deaths globally. A protein-protein interaction network (PPIN) and its analysis can provide insight into the behavior of cells and lead to advance the procedure of drug discovery. The identification of essential proteins is crucial to understand for cellular survival. There are many centrality measures to detect influential nodes in complex networks. Since SARS-CoV-2 and (H1N1) influenza PPINs pose 553 common proteins. Analyzing influential proteins and comparing these networks together can be an effective step helping biologists in drug design. Results: We used 21 centrality measures on SARS-CoV-2 and (H1N1) influenza PPINs to identify essential proteins. PCA-based dimensionality reduction was applied on normalized centrality values. Some measures demonstrated a high level of contribution in comparison to others in both PPINs, like Barycenter, Decay, Diffusion degree, Closeness (Freeman), Closeness (Latora), Lin, Radiality, and Residual. Using validation measures, the appropriate clustering method was chosen for centrality measures. We also investigated some graph theory-based properties like the power law, exponential distribution, and robustness. Conclusions: Through analysis and comparison, both networks exhibited remarkable experimental results. The network diameters were equal and in terms of heterogeneity, SARS-CoV-2 PPIN tends to be more heterogeneous. Both networks under study display a typical power-law degree distribution. Dimensionality reduction and unsupervised learning methods were so effective to reveal appropriate centrality measures.

scholarly journals Insight into Bacterial Virulence Mechanisms against Host Immune Response via the Yersinia pestis-Human Protein-Protein Interaction Network

2011 ◽  
Vol 79 (11) ◽  
pp. 4413-4424 ◽  
Author(s):  
Huiying Yang ◽  
Yuehua Ke ◽  
Jian Wang ◽  
Yafang Tan ◽  
Sebenzile K. Myeni ◽  
...  
Keyword(s):  
Signaling Pathways ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Mapk Signaling ◽  
Human Protein ◽  
Content Type ◽  
Protein Protein Interaction ◽  
Human Proteins ◽  
Protein Protein Interaction Network

ABSTRACTAYersinia pestis-human protein interaction network is reported here to improve our understanding of its pathogenesis. Up to 204 interactions between 66Y. pestisbait proteins and 109 human proteins were identified by yeast two-hybrid assay and then combined with 23 previously published interactions to construct a protein-protein interaction network. Topological analysis of the interaction network revealed that human proteins targeted byY. pestiswere significantly enriched in the proteins that are central in the human protein-protein interaction network. Analysis of this network showed that signaling pathways important for host immune responses were preferentially targeted byY. pestis, including the pathways involved in focal adhesion, regulation of cytoskeleton, leukocyte transendoepithelial migration, and Toll-like receptor (TLR) and mitogen-activated protein kinase (MAPK) signaling. Cellular pathways targeted byY. pestisare highly relevant to its pathogenesis. Interactions with host proteins involved in focal adhesion and cytoskeketon regulation pathways could account for resistance ofY. pestisto phagocytosis. Interference with TLR and MAPK signaling pathways byY. pestisreflects common characteristics of pathogen-host interaction that bacterial pathogens have evolved to evade host innate immune response by interacting with proteins in those signaling pathways. Interestingly, a large portion of human proteins interacting withY. pestis(16/109) also interacted with viral proteins (Epstein-Barr virus [EBV] and hepatitis C virus [HCV]), suggesting that viral and bacterial pathogens attack common cellular functions to facilitate infections. In addition, we identified vasodilator-stimulated phosphoprotein (VASP) as a novel interaction partner of YpkA and showed that YpkA could inhibitin vitroactin assembly mediated by VASP.

scholarly journals Network controllability enrichment analysis reveals that SARS-CoV-2 infection tends to target indispensable nodes of a directed human protein-protein interaction network

2021 ◽  
Author(s):  
Ho-Joon Lee
Keyword(s):  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Protein Protein Interaction ◽  
Genome Wide ◽  
A Genome ◽  
Human Proteins ◽  
Protein Protein Interaction Network ◽  
Network Controllability

The COVID-19 disease has been a global threat caused by the new coronavirus species, SARS-CoV-2, since early 2020 with an urgent need for therapeutic interventions. In order to provide insight into human proteins targeted by SARS-CoV-2, here we study a directed human protein-protein interaction network (dhPPIN) based on our previous work on network controllability of virus targets. We previously showed that human proteins targeted by viruses tend to be those whose removal in a dhPPIN requires more control of the network dynamics, which were classified as indispensable nodes. In this study we introduce a more comprehensive rank-based enrichment analysis of our previous dhPPIN for SARS-CoV-2 infection and show that SARS-CoV-2 also tends to target indispensable nodes in the dhPPIN using multiple proteomics datasets, supporting validity and generality of controllability analysis of viral infection in humans. Also, we find differential controllability among SARS-CoV-2, SARS-CoV-1, and MERS-CoV from a comparative proteomics study. Moreover, we show functional significance of indispensable nodes by analyzing heterogeneous datasets from a genome-wide CRISPR screening study, a time-course phosphoproteomics study, and a genome-wide association study. Specifically, we identify SARS-CoV-2 ORF3A as most frequently interacting with indispensable proteins in the dhPPIN, which are enriched in TGF-beta signaling and tend to be sources nodes and interact with each other. Finally, we built an integrated network model of ORF3A-interacting indispensable proteins with multiple functional supports to provide hypotheses for experimental validation as well as therapeutic opportunities. Therefore, a sub-network of indispensable proteins targeted by SARS-CoV-2 could serve as a prioritized network of drug targets and a basis for further functional and mechanistic studies from a network controllability perspective.

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