Dataset Information

Connectedness of PPI network neighborhoods identifies regulatory hub proteins.

ABSTRACT:

Motivation

With the growing availability of high-throughput protein-protein interaction (PPI) data, it has become possible to consider how a protein's local or global network characteristics predict its function.

Results

We introduce a graph-theoretic approach that identifies key regulatory proteins in an organism by analyzing proteins' local PPI network structure. We apply the method to the yeast genome and describe several properties of the resulting set of regulatory hubs. Finally, we demonstrate how the identified hubs and putative target gene sets can be used to identify causative, functional regulators of differential gene expression linked to human disease.

Availability

Code is available at http://bcb.cs.tufts.edu/hubcomps.

Contact

fox.andrew.d@gmail.com; slonim@cs.tufts.edu

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Fox AD

PROVIDER: S-EPMC3072558 | biostudies-literature | 2011 Apr

REPOSITORIES: biostudies-literature

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Publications

Connectedness of PPI network neighborhoods identifies regulatory hub proteins.

Fox Andrew D AD Hescott Benjamin J BJ Blumer Anselm C AC Slonim Donna K DK

Bioinformatics (Oxford, England) 20110302 8

<h4>Motivation</h4>With the growing availability of high-throughput protein-protein interaction (PPI) data, it has become possible to consider how a protein's local or global network characteristics predict its function.<h4>Results</h4>We introduce a graph-theoretic approach that identifies key regulatory proteins in an organism by analyzing proteins' local PPI network structure. We apply the method to the yeast genome and describe several properties of the resulting set of regulatory hubs. Fina ...[more]

PMID: 21367871

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Project description:In many protein-protein interaction (PPI) networks, densely connected hub proteins are more likely to be essential proteins. This is referred to as the "centrality-lethality rule", which indicates that the topological placement of a protein in PPI network is connected with its biological essentiality. Though such connections are observed in many PPI networks, the underlying topological properties for these connections are not yet clearly understood. Some suggested putative connections are the involvement of essential proteins in the maintenance of overall network connections, or that they play a role in essential protein clusters. In this work, we have attempted to examine the placement of essential proteins and the network topology from a different perspective by determining the correlation of protein essentiality and reverse nearest neighbor topology (RNN).The RNN topology is a weighted directed graph derived from PPI network, and it is a natural representation of the topological dependences between proteins within the PPI network. Similar to the original PPI network, we have observed that essential proteins tend to be hub proteins in RNN topology. Additionally, essential genes are enriched in clusters containing many hub proteins in RNN topology (RNN protein clusters). Based on these two properties of essential genes in RNN topology, we have proposed a new measure; the RNN cluster centrality. Results from a variety of PPI networks demonstrate that RNN cluster centrality outperforms other centrality measures with regard to the proportion of selected proteins that are essential proteins. We also investigated the biological importance of RNN clusters.This study reveals that RNN cluster centrality provides the best correlation of protein essentiality and placement of proteins in PPI network. Additionally, merged RNN clusters were found to be topologically important in that essential proteins are significantly enriched in RNN clusters, and biologically important because they play an important role in many Gene Ontology (GO) processes.

Project description:Bladder cancer cells can acquire persistent infection of oncolytic Newcastle disease virus (NDV) but the molecular mechanism(s) remain unelucidated. This poses a major barrier to the effective clinical translation of oncolytic NDV virotherapy of cancers. To improve our understanding of the molecular mechanism(s) associated with the development of NDV persistent infection in bladder cancer, we used mRNA expression profiles of persistently infected bladder cancer cells to construct PPI networks. Based on paths and modules in the PPI network, the bridges were found mainly in the upregulated mRNA-pathways of p53 signalling, ECM-receptor interaction, and TGF-beta signalling and downregulated mRNA-pathways of antigen processing and presentation, protein processing in endoplasmic reticulum, completement and coagulation cascades in persistent TCCSUPPi cells. In persistent EJ28Pi cells, connections were identified mainly through upregulated mRNA-pathways of renal carcinoma, viral carcinogenesis, Ras signalling and cell cycle and the downregulated mRNA-pathways of Wnt signalling, HTLV-I infection and pathways in cancers. These connections were mainly dependent on RPL8-HSPA1A/HSPA4 in TCCSUPPi cells and EP300, PTPN11, RAC1-TP53, SP1, CCND1 and XPO1 in EJ28Pi cells. Oncomine validation showed that the top hub genes identified in the networks that include RPL8, THBS1, F2 from TCCSUPPi and TP53 and RAC1 from EJ28Pi are involved in the development and progression of bladder cancer. Protein-drug interaction networks identified several putative drug targets that could be used to disrupt the linkages between the modules and prevent bladder cancer cells from acquiring NDV persistent infection. This novel PPI network analysis of differentially expressed mRNAs of NDV persistently infected bladder cancer cell lines provide an insight into the molecular mechanisms of NDV persistency of infection in bladder cancers and the future screening of drugs that can be used together with NDV to enhance its oncolytic efficacy.

Dataset Information

Connectedness of PPI network neighborhoods identifies regulatory hub proteins.

Motivation

Results

Availability

Contact

Supplementary information

Publications

Connectedness of PPI network neighborhoods identifies regulatory hub proteins.

Similar Datasets

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