Background: Esophageal adenocarcinoma (EAC) is one of the mostlethal cancers in the world with a very poor prognosis. Identification of molecular diagnostic methods is an important goal. Since protein-protein interaction (PPI) network analysis is a suitable method for molecular assessment, in the present research a PPI network related to EAC was targeted. Material and Method: Cytoscape software and its applications including STRING DB, Cluster ONE and ClueGO were applied to analyze the PPI network. Result: Among 182 EAC-related proteins which were identified, 129 were included in a main connected component. Proteins based on centrality analysis of characteristics such as degree, betweenness, closeness and stress were screened and key nodes were introduced. Two clusters were determined of which only one was significant statistically. Gene ontology revealed 50 terms in three groups associated with EAC. Conclusion:The findings indicate nine crucial proteins could form a candidate biomarker panel for EAC.
Sanz-Pamplona R, Berenguer A, Sole X, Cordero D, Crous-Bou M, Serra-Musach J, et al. Tools for protein-protein interaction network analysis in cancer research. Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico. 2012;14(1):3-14. Abstract ...
Sanz-Pamplona R, Berenguer A, Sole X, Cordero D, Crous-Bou M, Serra-Musach J, et al. Tools for protein-protein interaction network analysis in cancer research. Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico. 2012;14(1):3-14. Abstract ...
Self-interacting proteins (SIPs) play an essential role in cellular functions and the evolution of protein interaction networks (PINs). Due to the limitations of experimental self-interaction proteins detection technology, it is a very important task to develop a robust and accurate computational approach fo
The systematic analysis of protein-protein interactions can enable a better understanding of cellular organization, processes and functions. Functional modules can be identified from the protein interaction networks derived from experimental data sets. However, these analyses are challenging because of the presence of unreliable interactions and the complex connectivity of the network. The integration of protein-protein interactions with the data from other sources can be leveraged for improving the effectiveness of functional module detection algorithms. We have developed novel metrics, called semantic similarity and semantic interactivity, which use Gene Ontology (GO) annotations to measure the reliability of protein-protein interactions. The protein interaction networks can be converted into a weighted graph representation by assigning the reliability values to each interaction as a weight. We presented a flow-based modularization algorithm to efficiently identify overlapping modules in the weighted
Objective: To identify candidate biomarkers correlated with clinical prognosis of patients with bladder cancer (BC). Methods: Weighted gene co-expression network analysis was applied to build a co-expression network to identify hub genes correlated with tumor node metastasis (TNM) staging of BC patients. Functional enrichment analysis was conducted to functionally annotate the hub genes. Protein-protein interaction network analysis of hub genes was performed to identify the interactions among the hub genes. Survival analyses were conducted to characterize the role of hub genes on the survival of BC patients. Gene set enrichment analyses were conducted to find the potential mechanisms involved in the tumor proliferation promoted by hub genes. Results: Based on the results of topological overlap measure based clustering and the inclusion criteria, top 50 hub genes were identified. Hub genes were enriched in cell proliferation associated gene ontology terms (mitotic sister chromatid segregation, mitotic
Polyphosphate (polyP) has bactericidal activity against a gram-negative periodontopathogen Porphyromonas gingivalis, a black-pigmented gram-negative anaerobic rod. However, current knowledge about the mode of action of polyP against P. gingivalis is incomplete. To elucidate the mechanisms of antibacterial action of polyP against P. gingivalis, we performed the full-genome gene expression microarrays, and gene ontology (GO) and protein-protein interaction network analysis of differentially expressed genes (DEGs). We successfully identified 349 up-regulated genes and 357 down-regulated genes (|1.5-fold, P | 0.05) in P. gingivalis W83 treated with polyP75 (sodium polyphosphate, Nan+2PnO3n+1; n = 75). Real-time PCR confirmed the up- and down-regulation of some selected genes. GO analysis of the DEGs identified distinct biological themes. Using 202 DEGs belonging to the biological themes, we generated the protein-protein interaction network based on a database of known and predicted protein interactions. The
Researchers from the Institut Curie and from the Paris-based biotechnology company Hybrigenics announced today that they have built a protein-protein interaction map of the fruit fly, Drosophila melanogaster. This simple model organism allows them to study a reference set of proteins that includes most of those known to be involved in human cancer. Since proteins function in networks, the systematic identification of the physical interactions that occur between proteins will help understanding their biological function, and improve our capacity to intervene and, ultimately, to discover novel, more specific therapeutic targets. Their results are published in the March 1st issue of Genome Research ...
VisualComplexity.com is a unified resource space for anyone interested in the visualization of complex networks. The projects main goal is to leverage a critical understanding of different visualization methods, across a series of disciplines, as diverse as Biology, Social Networks or the World Wide Web.
Here, we described the multiplex in vivo measurement of 1,379 protein-protein interactions in 14 environmental conditions, to our knowledge the most extensive direct study of how protein interaction networks respond dynamically to extrinsic environmental perturbations. The most striking finding was the prevalence of dynamic binary complexes. More than half of the PPIs we considered (757 of 1,379) responded to at least one perturbation. The environmental perturbations that yielded the largest number of changes relative to our reference condition were respiratory growth in ethanol, heat shock, oxidative stress, and DNA damage. That these responses were the most profound might have been expected, as these conditions are likely to have been frequently experienced in the evolutionary history of yeast (Gasch & Werner‐Washburne, 2002; Gasch, 2007), allowing for selection and maintenance of a complex adaptive regulatory strategy. We observed that proteins with certain functions were more likely to ...
Rual JF، Venkatesan K، Hao T، Hirozane-Kishikawa T، Dricot A، Li N، Berriz GF، Gibbons FD، Dreze M، Ayivi-Guedehoussou N، Klitgord N، Simon C، Boxem M، Milstein S، Rosenberg J، Goldberg DS، Zhang LV، Wong SL، Franklin G، Li S، Albala JS، Lim J، Fraughton C، Llamosas E، Cevik S، Bex C، Lamesch P، Sikorski RS، Vandenhaute J، Zoghbi HY، Smolyar A، Bosak S، Sequerra R، Doucette-Stamm L، Cusick ME، Hill DE، Roth FP، Vidal M (2005). "Towards a proteome-scale map of the human protein-protein interaction network.". Nature. 437 (7062): 1173-8. PMID 16189514. doi:10.1038/nature04209. ...
An online database that integrates the extracellular protein interaction network. ARNIE allows users to browse the network by clicking on individual proteins, or by specifying the spatiotemporal parameters using the drop-down menus. Clicking on connector lines will allow users to compare stage-matched expression patterns for genes encoding interacting proteins. Additionally, users can rapidly search for their genes in the network using the BLAST server provided.
Predicting Protein Functions from Protein Interaction Networks: 10.4018/978-1-60566-398-2.ch012: Functional characterization of genes and their protein products is essential to biological and clinical research. Yet, there is still no reliable way of
Recent advances in biotechnology have resulted in a large amounts of protein-protein interaction (PPI) data. Modeling and clustering PPI networks with simple graphs makes it possible for us to understand the basic components and organization of cell machinery from the network level[1]. One of the most important challenges in the post-genomic era is to analyze the complex networks of PPIs and detect protein complexes or functional modules from them. Over the past decade, many computational methods have been proposed for clustering PPI networks, such as G-N [2], MCODE[3], RNSC[4], LCMA[5], DPClus [6], MoNet [7], IPCA [8], COACH [9], and SPICi [10].. While significant progress has been made in computational methods, there are two major challenges in clustering PPI networks. One of the challenges is that the conventional clustering methods generally considered the PPI network as a static graph and overlooked the dynamics inherent within these networks. This is mainly because that the widely used ...
Protein interaction networks have become a tool to study biological processes, either for predicting molecular functions or for designing proper new drugs to regulate the main biological interactions. Furthermore, such networks are known to be organized in sub-networks of proteins contributing to the same cellular function. However, the protein function prediction is not accurate and each protein has traditionally been assigned to only one function by the network formalism. By considering the network of the physical interactions between proteins of the yeast together with a manual and single functional classification scheme, we introduce a method able to reveal important information on protein function, at both micro- and macro-scale. In particular, the inspection of the properties of oscillatory dynamics on top of the protein interaction network leads to the identification of misclassification problems in protein function assignments, as well as to unveil correct identification of protein functions. We
Pathway analysis has become the first choice for gaining insight into the underlying biology of differentially expressed genes and proteins. Come learn web-based gene annotation, gene ID conversion, pathway enrichment, and protein-protein interaction networks and automate the process ...
In recent years, various types of cellular networks have penetrated biology and are nowadays used omnipresently for studying eukaryote and prokaryote organisms. Still, the relation and the biological overlap among phenomenological and inferential gene networks, e.g., between the protein interaction network and the gene regulatory network inferred from large-scale transcriptomic data, is largely unexplored. We provide in this study an in-depth analysis of the structural, functional and chromosomal relationship between a protein-protein network, a transcriptional regulatory network and an inferred gene regulatory network, for S. cerevisiae and E. coli. Further, we study global and local aspects of these networks and their biological information overlap by comparing, e.g., the functional co-occurrence of Gene Ontology terms by exploiting the available interaction structure among the genes. Although the individual networks represent different levels of cellular interactions with global structural and
Intrinsically disordered proteins (IDPs) are proteins that usually do not adopt well-defined native structures when isolated in solution under physiological conditions. Numerous IDPs have close relationships with human diseases such as tumor, Parkinson disease, Alzheimer disease, diabetes, and so on. These disease-associated IDPs commonly play principal roles in the disease-associated protein-protein interaction networks. Most of them in the disease datasets have more interactants and hence the size of the disease-associated IDPs interaction network is simultaneously increased. For example, the tumor suppressor protein p53 is an intrinsically disordered protein and also a hub protein in the p53 interaction network; α-synuclein, an intrinsically disordered protein involved in Parkinson diseases, is also a hub of the protein network. The disease-associated IDPs may provide potential targets for drugs modulating protein-protein interaction networks. Therefore, novel strategies for drug discovery based on
Xin Guo Introducing... the DOMain-oriented Alignment of Interaction Networks (DOMAIN). Previous paradigms include the node-then-edge-alignment paradigm and direct-edge-alignment paradigm. In the latter, interactions are more likely to be conserved. Many studies have suggested that direct PPIs can be mediated by interactions of their domains. Their method follows the direct-edge-alignment paradigm. In the method: try to…
Background Recent advances in proteomics technologies such as two-hybrid, phage display and mass spectrometry have enabled us to create a detailed map of biomolecular interaction networks. Initial...
SVM and RF contributed 15 and 10 unique predictions, respectively, that were confirmed experimentally. Thus, the two methods were somewhat complementary and, if used together, may provide better coverage of true predictions. On the other hand, using the overlap of predictions from both SVM and RF provides a more conservative and, hence, more reliable list of protein interactions that could be used as a starting point for further investigations.. The Cbf11 interactors predicted were significantly enriched for the experimentally determined targets, both in the case of SVM (Fishers exact test, P = 10−14), RF (Fishers P = 10−28), and in the overlap of the two methods (Fishers P = 10−28). So far, there are no known genetic interactions for Cbf11 and no functional interactions due to the protein not being conserved in budding yeast. For this reason, our method for predicting its physical association partners can only be compared with selecting proteins at random from the whole genome. The ...
Increasingly, human diseases and other traits are being probed by genome-wide screens. For example, several recent papers [10-14] describe genome-wide screening efforts to identify somatic mutations in several cancer types. Placing such lists of genes or proteins into a pathway context can yield information on the relationships among these genes and has the potential to generate hypotheses about the mechanism(s) linking these genes to phenotypes.. Reliable pathway databases are essential for such an analysis, but because of the effort needed to curate pathways is so human-intensive, even the largest pathway database has a SwissProt coverage of under 20% (Table 2). In this report, we describe how we have integrated several large-scale experimental data sets to build and train a machine-learning system that identifies potential functional interactions among pairs of human proteins. We have combined the FIs predicted by this classifier with the curated pathways from Reactome and other pathway ...
Although coevolution provides an appealing explanation for the similarity in the evolutionary rates of interacting proteins, alternative hypotheses must be considered. The proteins in an interacting pair presumably act in the same functional pathway and therefore are likely to have similar effects on organism fitness. Because the dispensability of a protein influences its rate of evolution (4), the similarity in the evolutionary rates of interacting proteins could be a consequence of similarity in their fitness effects. Our test of this hypothesis involved two steps.. First, we tested whether proteins that interact do indeed have similar effects on organism fitness. A randomization test showed that the mean difference in fitness effects between interacting proteins, ΔF̄* = 0.41, was significantly smaller than the mean difference between arbitrarily paired proteins ΔF̄(P , 10−5) (Fig. 3B). Thus, interacting proteins do have similar effects on organism fitness.. Second, we determined whether ...
To understand living cells one must study them as systems rather than as a collection of individual molecules. The abstract representation of intracellular systems as networks is fruitful, because it provides the ability to study these systems as a whole by ignoring details of individual components, but retaining the complexity of the interactions. This chapter will review the discoveries made through application of approaches from the science of complex networks to Protein Interaction Networks, i.e. undirected networks in which the nodes represent proteins, and pairs are connected by edges if the proteins physically interact. Over the last decade the experimental techniques for measuring protein interactions has been highly improved and large numbers of new protein interactions have been elucidated. Therefore, along with the reviewed concepts and discoveries, we provide a re-evaluation of several previous conclusions by analyzing a set of high quality networks from the organism S. ...
Extracting Biological Significant Subnetworks from Protein-Protein Interactions Induced by Differentially Expressed Genes of HIV-1 Vpr Variants: 10.4018/IJSDA.2015100103: Identification of protein interaction network is very important to find the cell signaling pathway for a particular disease. The authors have found the
The combined use of gene expression profiles and protein-protein interaction networks has shown remarkable successes in the prediction of breast cancer met
Towards Inter- and Intra- Cellular Protein Interaction Analysis: Applying the Betweenness Centrality Graph Measure for Node Importance
Ewing RM, Chu P, Elisma F, Li H, Taylor P, Climie S, McBroom-Cerajewski L, Robinson MD, OConnor L, Li M, Taylor R, Dharsee M, Ho Y, Heilbut A, Moore L, Zhang S, Ornatsky O, Bukhman YV, Ethier M, Sheng Y, Vasilescu J, Abu-Farha M, Lambert JP, Duewel HS, Stewart II, Kuehl B, Hogue K, Colwill K, Gladwish K, Muskat B, Kinach R, Adams SL, Moran MF, Morin GB, Topaloglou T, Figeys D. Large-scale mapping of human protein-protein interactions by mass spectrometry ...
An advanced course focusing on the analysis of the protein function and protein-protein interactions within the context of the entire protein complement of a cell. Some aspects of protein structure as it pertains to the principles of protein-protein interactions will be covered along with genetic and biochemical methods for the analysis of protein complexes, protein interaction networks and system wide protein identification and dynamics. This course is intended for students in Biochemistry. Students in other programs may be admitted subject to availability and with the consent of the Department ...
InterPro provides functional analysis of proteins by classifying them into families and predicting domains and important sites. We combine protein signatures from a number of member databases into a single searchable resource, capitalising on their individual strengths to produce a powerful integrated database and diagnostic tool.
Interaction map of inhibitor 6: hydrogen‐bonding network to structural water molecules in addition to direct hydrogen bonding to Thr 224, Thr 226 and Met
Scientists have created the largest-scale map to date of direct interactions between proteins encoded by the human genome and newly predicted dozens of genes to be involved in cancer. The new "human interactome" map describes about 14,000 direct interactions between proteins. The interactome is the network formed by proteins and other cellular components that stick together. The new map is over four times larger than any previous map of its kind, containing more high-quality interactions than have come from all previous studies put together.. ...
Complete understanding of cellular function requires knowledge of the composition and dynamics of protein interaction networks, the importance of which spans all ...
Network analysis of PG2-enriched signature reveals the connection to immune function. (a) The subnetwork was composed of 87 genes, including graph-based clique
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HYPs predicted by RCR in the GSE5372 Mechanical injury data set that were present in the Immune Regulation of Tissue Repair subnetwork are listed in the right column with the predicted direction. Yellow indicates increased HYPs, blue indicates decreased HYPs. Direction of HYP indicating increased fibrosis is indicated in the left column ...
In a differential gene experiment, a cell perturbation can be measured on a microarray before and after the perturbation. The information from these microarrays can then be used to inference genetic pathways and protein-protein interaction networks. In this paper we reverse this idea somewhat and measure a cell perturbation through microarrays and then rely on a protein interaction map to assess which proteins are most likely influenced by the specific perturbation. This in turn helps to elucidate the functional effect the perturbation has on the cell system. The first part of the paper focuses on the propagation model we developed to obtain this information. The second part of the paper reports on a specific experiment that was driven by the interpretation we obtained through such a gene influence network. We applied a PC12 cell line that allows doxocyclin-dependent expression of constitutive active mitogen-activated protein kinase-activated protein kinase (MAPKAPK5 or MK5) to compare the
Within the cell, biosynthetic pathways are embedded in protein-protein interaction networks. In Arabidopsis, the biosynthetic pathways of aliphatic and indole glucosinolate defense compounds are well-characterized. However, little is known about the spatial orchestration of these enzymes and their interplay with the cellular environment. To address these aspects, we applied two complementary, untargeted approaches - split-ubiquitin yeast 2-hybrid and co-immunoprecipitation screens - to identify proteins interacting with CYP83A1 and CYP83B1, two homologous enzymes specific for aliphatic and indole glucosinolate biosynthesis, respectively. Our analyses reveal distinct functional networks with substantial interconnection among the identified interactors for both pathway-specific markers, and add to our knowledge about how biochemical pathways are connected to cellular processes. Specifically, a group of protein interactors involved in cell death and the hypersensitive response provides a potential link
Rual JF, Venkatesan K, Hao T, Hirozane-Kishikawa T, Dricot A, Li N, Berriz GF, Gibbons FD, Dreze M, Ayivi-Guedehoussou N, Klitgord N, Simon C, Boxem M, Milstein S, Rosenberg J, Goldberg DS, Zhang LV, Wong SL, Franklin G, Li S, Albala JS, Lim J, Fraughton C, Llamosas E, Cevik S, Bex C, Lamesch P, Sikorski RS, Vandenhaute J, Zoghbi HY, Smolyar A, Bosak S, Sequerra R, Doucette-Stamm L, Cusick ME, Hill DE, Roth FP, Vidal M (Oct 2005). "Towards a proteome-scale map of the human protein-protein interaction network". Nature. 437 (7062): 1173-8. doi:10.1038/nature04209. PMID 16189514 ...
Rual JF, Venkatesan K, Hao T, Hirozane-Kishikawa T, Dricot A, Li N, Berriz GF, Gibbons FD, Dreze M, Ayivi-Guedehoussou N, Klitgord N, Simon C, Boxem M, Milstein S, Rosenberg J, Goldberg DS, Zhang LV, Wong SL, Franklin G, Li S, Albala JS, Lim J, Fraughton C, Llamosas E, Cevik S, Bex C, Lamesch P, Sikorski RS, Vandenhaute J, Zoghbi HY, Smolyar A, Bosak S, Sequerra R, Doucette-Stamm L, Cusick ME, Hill DE, Roth FP, Vidal M (October 2005). "Towards a proteome-scale map of the human protein-protein interaction network". Nature. 437 (7062): 1173-8. doi:10.1038/nature04209. PMID 16189514 ...
Hepatocellular carcinoma (HCC) is one of the most common types of cancer worldwide. Despite several efforts to elucidate molecular mechanisms involved in this cancer, they are still not fully understood. To acquire further insights into the molecular mechanisms of HCC, and to identify biomarkers for early diagnosis of HCC, we downloaded the gene expression profile on HCC with non-cancerous liver controls from the Gene Expression Omnibus (GEO) and analyzed these data using a combined bioinformatics approach. The dysregulated pathways and protein-protein interaction (PPI) network, including hub nodes that distinguished HCCs from non-cancerous liver controls, were identified. In total, 29 phenotype-related differentially expressed genes were included in the PPI network. Hierarchical clustering showed that the gene expression profile of these 29 genes was able to differentiate HCC samples from non-cancerous liver samples. Among these genes, CDC2 (Cell division control protein 2 homolo g), MMP 2 (matrix
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Background Schizophrenia (SZ) is a heritable, complex mental disorder. We have seen limited success in finding causal genes for schizophrenia from numerous conventional studies. Protein interaction network and pathway-based analysis may provide us an alternative and effective approach to investigating the molecular mechanisms of schizophrenia. Methodology/Principal Findings We selected a list of schizophrenia candidate genes (SZGenes) using a multi-dimensional evidence-based approach. The global network properties of proteins encoded by these SZGenes were explored in the context of the human protein interactome while local network properties were investigated by comparing SZ-specific and cancer-specific networks that were extracted from the human interactome. Relative to cancer genes, we observed that SZGenes tend to have an intermediate degree and an intermediate efficiency on a perturbation spreading throughout the human interactome. This suggested that schizophrenia might have different pathological
Reviews protein networks; human protein ranking; the mitogen-activated protein kinase (MAPK) and insulin signaling pathways; human diseases caused by somatic mutations to the pathway; use of the MAPK pathway in plant molecular breeding; and protein domain evolution.
The IMD pathway in Drosophila regulates the systemic immune response against Gram‐negative bacteria, and the molecular cascade from the PGRP‐LC receptor down to the activation of the NF‐κB factor Relish has been extensively studied. The Akirin molecule is required for IMD target gene activation by the Relish transcription factor (Goto et al, 2008), and this finding suggests that IMD effector gene transcription might depend on additional factors that remained to be identified. In order to further elucidate NF‐κB‐dependent gene activation, we re‐explore the IMD pathway using Akirin as a starting point. We undertook an unbiased two‐hybrid screen that identified BAP60 as an Akirin transcriptional partner during the innate immune response, confirming the data of the protein‐interaction map of the fly proteome (Giot et al, 2003). Additionally, we show that BAP55, an Actin‐related component of the SWI/SNF Brahma complex (Papoulas et al, 1998; Armstrong et al, 2002), engages Akirin ...
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Wiki-Pi: a web resource for human protein-protein interactions. It shows genes and PPIs with information about pathways, protein-protein interactions (PPIs), Gene Ontology (GO) annotations including cellular localization, molecular function and biological process, drugs, diseases, genome-wide association studies (GWAS), GO enrichments, PDB ID, Uniprot ID, HPRD ID, and word cloud from pubmed abstracts.
Wiki-Pi: a web resource for human protein-protein interactions. It shows genes and PPIs with information about pathways, protein-protein interactions (PPIs), Gene Ontology (GO) annotations including cellular localization, molecular function and biological process, drugs, diseases, genome-wide association studies (GWAS), GO enrichments, PDB ID, Uniprot ID, HPRD ID, and word cloud from pubmed abstracts.
TY - JOUR. T1 - Organized Modularity in the Interactome: Evidence from the Analysis of Dynamic Organization in the Cell Cycle. AU - Wang, Haiying. AU - Zheng, Huiru. PY - 2014/11. Y1 - 2014/11. N2 - The organization of global protein interaction networks (PINs) has been extensively studied and heatedly debated. We revisited this issue in the context of the analysis of dynamic organization of a PIN in the yeast cell cycle. Statistically significant bimodality was observed when analyzing the distribution of the differences in expression peak between periodically expressed partners. A close look at their behavior revealed that date and party hubs derived from this analysis have some distinct features. There are no significant differences between them in terms of protein essentiality, expression correlation and semantic similarity derived from Gene Ontology (GO) biological process hierarchy. However, date hubs exhibit significantly greater values than party hubs in terms of semantic similarity ...