Connectivity In Protein-protein Interaction Network Research Articles

Identifying driver pathways based on a parameter-free model and a partheno-genetic algorithm

BackgroundTremendous amounts of omics data accumulated have made it possible to identify cancer driver pathways through computational methods, which is believed to be able to offer critical information in such downstream research as ascertaining cancer pathogenesis, developing anti-cancer drugs, and so on. It is a challenging problem to identify cancer driver pathways by integrating multiple omics data.ResultsIn this study, a parameter-free identification model SMCMN, incorporating both pathway features and gene associations in Protein–Protein Interaction (PPI) network, is proposed. A novel measurement of mutual exclusivity is devised to exclude some gene sets with “inclusion” relationship. By introducing gene clustering based operators, a partheno-genetic algorithm CPGA is put forward for solving the SMCMN model. Experiments were implemented on three real cancer datasets to compare the identification performance of models and methods. The comparisons of models demonstrate that the SMCMN model does eliminate the “inclusion” relationship, and produces gene sets with better enrichment performance compared with the classical model MWSM in most cases.ConclusionsThe gene sets recognized by the proposed CPGA-SMCMN method possess more genes engaging in known cancer related pathways, as well as stronger connectivity in PPI network. All of which have been demonstrated through extensive contrast experiments among the CPGA-SMCMN method and six state-of-the-art ones.

In-silico approach of identifying novel therapeutic targets against Yersinia pestis using pan and subtractive genomic analysis

The magnitude of human affliction brought about by bacterial infections has been on the rise since the mid-5th century. Yersinia pestis is one such notable, gram-negative bacterium that inflicted havoc around the globe three times throughout different millenniums by causing deadly plagues. Despite the unremitting efforts by scientists, different strains of Yersinia pestis are still affecting the populations in various parts of the world by growing resistant to existing antimicrobial agents owing to their overuse. The current scenario, therefore, calls for new therapeutics to further combat the disease. In this study, 3105 core, 387 pathogen-specific unique, 536 choke-point, 796 virulence factors, and 115 antimicrobial resistant proteins were found using a pan-genomic and subtractive genome analysis of nine Yersinia pestis strains that could be instrumental in the development of drugs against Yersinia pestis. Subsequently, 1461 and 1114 essential proteins were identified as non-homologous to human and gut microflora. 535 and 30 proteins were predicted as cytoplasmic and broad-spectrum targets respectively. Finally, four potential targets were selected for their high connectivity in protein-protein interaction network. These selected target proteins are associated with one of the major lipopolysaccharide biosynthesis pathways. Therefore, dismantling their activity might indicate a probable strategy for developing therapeutics to combat bacterial infection caused by Yersinia pestis. However, further experimental validation in the laboratory is needed to consolidate the research findings.

Identification of Three Key Genes Associated with Hepatocellular Carcinoma Progression Based on Co-expression Analysis.

Hepatocellular carcinoma (HCC) is the fifth most common cancer and one of the leading causes of cancer-related death in the world. Due to the recurrence of HCC, its survival rate is still low. Therefore, it is vital to seek prognostic biomarkers for HCC. In this study, differential analysis was conducted on gene expression data in The Cancer Genome Atlas -LIHC, and 4482 differentially expressed genes in tumor tissue were selected. Then, weighted gene co-expression network analysis was used to analyze the co-expression of the gained differential genes. By module-trait correlation analysis, the turquoise gene module that was significantly related to tumor grade, pathologic_T stage, and clinical stage was identified. Thereafter, enrichment analysis of genes in this module uncovered that the genes were mainly enriched in the signaling pathways involved in spliceosome and cell cycle. After that, through correlation analysis, 18 hub genes highly correlated with tumor grade, clinical stage, pathologic_T stage, and the turquoise module were selected. Meanwhile, protein-protein interaction (PPI) network was constructed by using genes in the module. Finally, three key genes, heterogeneous nuclear ribonucleoprotein L, serrate RNA effector molecule, and cyclin B2, were identified by intersecting the top 30 genes with the highest connectivity in PPI network and the previously obtained 18 hub genes in the turquoise module. Further survival analysis revealed that high expression of the three key genes predicted poor prognosis of HCC. These results indicated the direction for further research on clinical diagnosis and prognostic biomarkers of HCC.

An Effective Graph Clustering Method to Identify Cancer Driver Modules.

Identifying the molecular modules that drive cancer progression can greatly deepen the understanding of cancer mechanisms and provide useful information for targeted therapies. Most methods currently addressing this issue primarily use mutual exclusivity without making full use of the extra layer of module property. In this paper, we propose MCLCluster to identity cancer driver modules, which use somatic mutation data, Cancer Cell Fraction (CCF) data, gene functional interaction network and protein-protein interaction (PPI) network to derive the module property on mutual exclusivity, connectivity in PPI network and functionally similarity of genes. We have taken three effective measures to ensure the effectiveness of our algorithm. First, we use CCF data to choose stronger signals and more confident mutations. Second, the weighted gene functional interaction network is used to quantify the gene functional similarity in PPI. The third, graph clustering method based on Markov is exploited to extract the candidate module. MCLCluster is tested in the two TCGA datasets (GBM and BRCA), and identifies several well-known oncogenes driver modules and some modules with functionally associated driver genes. Besides, we compare it with Multi-Dendrix, FSME Cluster and RME in simulated dataset with background noise and passenger rate, MCLCluster outperforming all of these methods.

In silico identification of EP400 and TIA1 as critical transcription factors involved in human hepatocellular carcinoma relapse.

Hepatocellular carcinoma (HCC) is the second leading cause of cancer-associated mortality worldwide. Transcription factors (TFs) are crucial proteins that regulate gene expression during cancer progression; however, the roles of TFs in HCC relapse remain unclear. To identify the TFs that drive HCC relapse, the present study constructed co-expression network and identified the Tan module the most relevant to HCC relapse. Numerous hub TFs (highly connected) were subsequently obtained from the Tan module according to the intra-module connectivity and the protein-protein interaction network connectivity. Next, E1A-binding protein p400 (EP400) and TIA1 cytotoxic granule associated RNA binding protein (TIA1) were identified as hub TFs differentially connected between the relapsed and non-relapsed subnetworks. In addition, zinc finger protein 143 (ZNF143) and Yin Yang 1 (YY1) were also identified by using the plugin iRegulon in Cytoscape as master upstream regulatory elements, which could potentially regulate expression of the genes and TFs of the Tan module, respectively. The Kaplan-Meier (KM) curves obtained from KMplot and Gene Expression Profiling Interactive Analysis tools confirmed that the high expression of EP400 and TIA1 were significantly associated with shorter relapse-free survival and disease-free survival of patients with HCC. Furthermore, the KM curves from the UALCAN database demonstrated that high EP400 expression significantly reduced the overall survival of patients with HCC. EP400 and TIA1 may therefore serve as potential prognostic and therapeutic biomarkers.

Identification of potential mechanism and hub genes for neuropathic pain by expression-based genome-wide association study.

Neuropathic pain (NP) is a common pathological pain state with limited effective treatments. This study was designed to identify potential mechanisms and candidate genes using gene expression-based genome-wide association study (eGWAS). All NP-related microarray experiments were obtained from Gene Expression Omnibus and ArrayExpress. Significantly dysregulated genes were identified between experimental and untreated groups, and the number of microarray experiments in which each gene was dysregulated was calculated. Significantly dysregulated genes were ranked according to P values of the chi-square test. Using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes database, we performed functional and pathway enrichment analysis. Protein-protein interaction (PPI) network and module analysis was performed using Cytoscape software. A total of 115 candidate genes were identified from 19 independent microarray experiments by eGWAS based on the Bonferroni threshold ( P < 2.97 × 10 -6 ). Immune and inflammatory responses, and complement and coagulation cascades, were respectively the most enriched biological process and pathways for candidate genes. The hub genes with highest connectivity in PPI network and two modules Ccl2 and Jun, and Ctss application of the eGWAS methodology can identify mechanisms and candidate genes associated with NP. Our results support the validity and prevalence of inflammatory and immune mechanisms across different NP models, and Ccl2, Jun, and Ctss may be the hub genes for NP.

Functional constraints on adaptive evolution of protein ubiquitination sites

It is still unclear whether there exist functional constraints on the evolution of protein ubiquitination sites, because most previous studies regarded all protein ubiquitination sites as a whole or only focused on limited structural properties. We tried to clarify the relation between functional constraints and ubiquitination sites evolution. We investigated the evolutionary conservation of human ubiquitination sites in a broad evolutionary scale from G. gorilla to S. pombe, and we found that in organisms originated after the divergence of vertebrate, ubiquitination sites are more conserved than their flanking regions, while the opposite tendency is observed before this divergence time. By grouping the ubiquitination proteins into different functional categories, we confirm that many functional constraints like certain molecular functions, protein tissue expression specificity and protein connectivity in protein-protein interaction network enhance the evolutionary conservation of ubiquitination sites. Furthermore, by analyzing the gains of ubiquitination sites at different divergence time and their functional characters, we validate that the emergences of ubiquitination sites at different evolutionary time were also affected by the uncovered functional constraints. The above results suggest that functional constraints on the adaptive evolution of ubiquitination sites increase the opportunity for ubiquitination to synthetically regulate various cellular and developmental processes during evolution.

On the quest for selective constraints shaping the expressivity of the genes casting retropseudogenes in human

BackgroundPseudogenes, the nonfunctional homologues of functional genes are now coming to light as important resources regarding the study of human protein evolution. Processed pseudogenes arising by reverse transcription and reinsertion can provide molecular record on the dynamics and evolution of genomes. Researches on the progenitors of human processed pseudogenes delved out their highly expressed and evolutionarily conserved characters. They are reported to be short and GC-poor indicating their high efficiency for retrotransposition. In this article we focused on their high expressivity and explored the factors contributing for that and their relevance in the milieu of protein sequence evolution.ResultsWe here, analyzed the high expressivity of these genes configuring processed or retropseudogenes by their immense connectivity in protein-protein interaction network, an inclination towards alternative splicing mechanism, a lower rate of mRNA disintegration and a slower evolutionary rate. While the unusual trend of the upraised disorder in contrast with the high expressivity of the proteins encoded by processed pseudogene ancestors is accredited by a predominance of hub-protein encoding genes, a high propensity of repeat sequence containing genes, elevated protein stability and the functional constraint to perform the transcription regulatory jobs. Linear regression analysis demonstrates mRNA decay rate and protein intrinsic disorder as the influential factors controlling the expressivity of these retropseudogene ancestors while the latter one is found to have the most significant regulatory power.ConclusionsOur findings imply that, the affluence of disordered regions elevating the network attachment to be involved in important cellular assignments and the stability in transcriptional level are acting as the prevailing forces behind the high expressivity of the human genes configuring processed pseudogenes.

Genome-wide analysis of chromatin features identifies histone modification sensitive and insensitive yeast transcription factors

We propose a method to predict yeast transcription factor targets by integrating histone modification profiles with transcription factor binding motif information. It shows improved predictive power compared to a binding motif-only method. We find that transcription factors cluster into histone-sensitive and -insensitive classes. The target genes of histone-sensitive transcription factors have stronger histone modification signals than those of histone-insensitive ones. The two classes also differ in tendency to interact with histone modifiers, degree of connectivity in protein-protein interaction networks, position in the transcriptional regulation hierarchy, and in a number of additional features, indicating possible differences in their transcriptional regulation mechanisms.

Discovering Functional Interdependence Relationship in PPI Networks for Protein Complex Identification

Protein molecules interact with each other in protein complexes to perform many vital functions, and different computational techniques have been developed to identify protein complexes in protein-protein interaction (PPI) networks. These techniques are developed to search for subgraphs of high connectivity in PPI networks under the assumption that the proteins in a protein complex are highly interconnected. While these techniques have been shown to be quite effective, it is also possible that the matching rate between the protein complexes they discover and those that are previously determined experimentally be relatively low and the "false-alarm" rate can be relatively high. This is especially the case when the assumption of proteins in protein complexes being more highly interconnected be relatively invalid. To increase the matching rate and reduce the false-alarm rate, we have developed a technique that can work effectively without having to make this assumption. The name of the technique called protein complex identification by discovering functional interdependence (PCIFI) searches for protein complexes in PPI networks by taking into consideration both the functional interdependence relationship between protein molecules and the network topology of the network. The PCIFI works in several steps. The first step is to construct a multiple-function protein network graph by labeling each vertex with one or more of the molecular functions it performs. The second step is to filter out protein interactions between protein pairs that are not functionally interdependent of each other in the statistical sense. The third step is to make use of an information-theoretic measure to determine the strength of the functional interdependence between all remaining interacting protein pairs. Finally, the last step is to try to form protein complexes based on the measure of the strength of functional interdependence and the connectivity between proteins. For performance evaluation, PCIFI was used to identify protein complexes in real PPI network data and the protein complexes it found were matched against those that were previously known in MIPS. The results show that PCIFI can be an effective technique for the identification of protein complexes. The protein complexes it found can match more known protein complexes with a smaller false-alarm rate and can provide useful insights into the understanding of the functional interdependence relationships between proteins in protein complexes.

Linking Gene Expression and Functional Network Data in Human Heart Failure

BackgroundGene expression profiling and the analysis of protein-protein interaction (PPI) networks may support the identification of disease bio-markers and potential drug targets. Thus, a step forward in the development of systems approaches to medicine is the integrative analysis of these data sources in specific pathological conditions. We report such an integrative bioinformatics analysis in human heart failure (HF). A global PPI network in HF was assembled, which by itself represents a useful compendium of the current status of human HF-relevant interactions. This provided the basis for the analysis of interaction connectivity patterns in relation to a HF gene expression data set.ResultsRelationships between the significance of the differentiation of gene expression and connectivity degrees in the PPI network were established. In addition, relationships between gene co-expression and PPI network connectivity were analysed. Highly-connected proteins are not necessarily encoded by genes significantly differentially expressed. Genes that are not significantly differentially expressed may encode proteins that exhibit diverse network connectivity patterns. Furthermore, genes that were not defined as significantly differentially expressed may encode proteins with many interacting partners. Genes encoding network hubs may exhibit weak co-expression with the genes encoding their interacting protein partners. We also found that hubs and superhubs display a significant diversity of co-expression patterns in comparison to peripheral nodes. Gene Ontology (GO) analysis established that highly-connected proteins are likely to be engaged in higher level GO biological process terms, while low-connectivity proteins tend to be engaged in more specific disease-related processes.ConclusionThis investigation supports the hypothesis that the integrative analysis of differential gene expression and PPI network analysis may facilitate a better understanding of functional roles and the identification of potential drug targets in human heart failure.