Molecular Complex Detection Algorithm Research Articles

Protein network module-based identification of key pharmacological pathways of Curcuma phaeocaulis Val. acting on hepatitis

Ethnopharmacological relevanceCurcuma phaeocaulis Val. (CP), as the vital medicines for blood-breaking and disorder-eliminating, has been widely used for hepatitis with good curative effects. Owing to the complexity of traditional Chinese medicine, the pharmacological mechanism of CP remains unclear. To solve this problem, a protein network module-based approach was proposed in this study. Materials and methodsFirstly, the content of active components of CP was detected based on HPLC-DAD. Then the liver protection of CP on Con A-induced hepatitis was validated via the analysis of serum levels of ALT, AST and LDH and histological findings. Next, the targets of CP components obtained from TCMD database were predicted by STITCH and ChEMBL retrieval. In addition, the protein interaction network (PIN) of CP was constructed by Cytoscape based on protein-protein interaction of targets obtained from STRING database. Following the topological analysis of CP PIN, it showed to exhibit the properties of scale-free, small world, and modularity matched with the property of complex biological networks. Finally, the functional modules were identified by gene ontology enrichment analysis based on Molecular Complex Detection algorithm. ResultsThe functional modules indicated that the mechanism of CP acting on the hepatitis is significantly associated with NF-κB and TGF-β signaling pathway. More interestingly, curcumin, demethoxycurcumin and bisdemethoxycurcumin were the main active components of CP acting on the hepatitis, which were demonstrated to be associated with the inflammatory process that occurs during the progression of hepatitis. ConclusionThe protein network module-based approach is an efficient way to investigate the pharmacological mechanisms of CP.

Topology of protein–protein interaction network and edge reduction co-efficiency in VEGF signaling of breast cancer

Here, we summarize the identification of possible hub protein in the core VEGF-induced interactome in breast cancer by the application of centrality measures. This approach has been extended to investigate the role of subnetworks in the core interactome. For the identification of subnetworks, we applied a protein network-based approach to find the novel insight in the function of pathways involved in breast cancer. A PPI network was constructed and the complexity of network was simplified to modules using molecular complex detection algorithm. Topological analysis of PPI network was performed to assess the functional significance of selected genes using KEGG and PubAngioGen database. Globally accepted centrality measure, Betweenness centrality, Degree distribution and Clustering co-efficient metrics were used to find the hub protein by scale-free network analysis. The bottleneck nodes in the subnetworks were found to be involved in regulating endothelial cell proliferation, central carbon metabolism, signal complex assembly, Phosphatidylinositol 3-kinase (PI3K), Vascular endothelial growth factor (VEGF), Erb-B receptor tyrosine kinase (ErbB) and prolactin signaling pathway. Wherein, main interconnecting hub nodes find their predominant distribution. Moreover, these main hub nodes were subjected to power graph analysis to further reduce the number of edges to 80% without losing the basic biological information, as it helps us to understand much better about highly interconnected nodes.

Identification of Pharmacologically Tractable Protein Complexes in Cancer Using the R-Based Network Clustering and Visualization Program MCODER.

Current multiomics assay platforms facilitate systematic identification of functional entities that are mappable in a biological network, and computational methods that are better able to detect densely connected clusters of signals within a biological network are considered increasingly important. One of the most famous algorithms for detecting network subclusters is Molecular Complex Detection (MCODE). MCODE, however, is limited in simultaneous analyses of multiple, large-scale data sets, since it runs on the Cytoscape platform, which requires extensive computational resources and has limited coding flexibility. In the present study, we implemented the MCODE algorithm in R programming language and developed a related package, which we called MCODER. We found the MCODER package to be particularly useful in analyzing multiple omics data sets simultaneously within the R framework. Thus, we applied MCODER to detect pharmacologically tractable protein-protein interactions selectively elevated in molecular subtypes of ovarian and colorectal tumors. In doing so, we found that a single molecular subtype representing epithelial-mesenchymal transition in both cancer types exhibited enhanced production of the collagen-integrin protein complex. These results suggest that tumors of this molecular subtype could be susceptible to pharmacological inhibition of integrin signaling.

Abstract B1-32: Genome-wide integrative analysis correlating locus-specific DNA methylation and hydroxymethylation to gene expression in normal prostate cells

Abstract Aberrant epigenetic modification in the form of regional hypermethylation and global hypomethylation has been implicated in the dysregulation of gene expression in various cancers, including prostate cancer (PCa). Recently discovered 5-hydroxymethylated marks (5hmC), considered to be key intermediates in the process of DNA demethylation, have also been shown to exhibit significant global loss in solid tumors as compared to normal tissue. Similar to cytosine base methylation (5mC), 5hmC may also play a key role in the regulation of gene expression and, consequently, gene function in PCa. We hypothesize that dynamic interplay between 5mC and 5hmC enrichment in promoter, CpG island, and intragenic regions shows specific association patterns with gene expression. A systematic investigation of these methylation marks may provide novel insights into epigenomic architecture by elucidating novel pathways and candidate genes in prostate tumorigenesis. Using Next Generation Sequencing (NGS), we characterized genome-wide 5mC and 5hmC marks and RNA expression data in a normal prostate tissue-derived representative cell line (RWPE-1). We have completed NGS following methyl-binding protein capture (MBD-seq) and hydroxymethyl-selective chemical labeling (hMe-Seal), and have performed integrative analysis correlating enriched genes stratified by region (intergenic regions proximal to DNase I hypersensitivity sites, as well as promoter, CpG island, and intragenic regions) to expression data obtained from RNA-seq and also validated using publicly available microarray expression data (GEO Accession Number: GSM375783) We divided expression data into equal tiers representing genes with no expression, low expression, and high expression, and have performed pathway-based analysis on genes significantly enriched for either 5mC or 5hmC marks for each tier stratified by gene region. We found gene expression to exhibit a strong positive correlation with core promoter and CpG island hydroxymethylation, with a corresponding negative correlation to methylation of these regions. In contrast, gene expression was found to be positively correlated with both methylation and hydroxymethylation in both intragenic regions and intergenic regions proximal to ENCODE DNase I hypersensitivity sites (GEO Accession Number: GSM1008595), which are associated with open chromatin and may play a role in downstream gene regulation. Interestingly, pathway-based analysis of these stratified tiers, grouped via the Molecular Complex Detection algorithm (MCODE) to identify pathway clusters with high biological significance, showed 5hmC and 5mC enrichment of the same pathways in each expression tier, suggesting a possible co-operative role for intragenic co-expression of these marks in regulating biological pathways. Clustered pathways co-enriched in intragenic 5mC and 5hmC showed strong enrichment in Gene Ontology terms related to core cellular functions within each expression tier, such as ion channel and transport regulation, primary metabolic processes, and protein and RNA binding. Currently, we are investigating these pathways to identify key candidate genes regulated by 5hmC and 5mC marks in normal prostate. This is the first study to correlate locus-specific global 5hmC enrichment to expression in normal prostate cells. Our preliminary analysis has shown correlation between 5hmC and 5mC enrichment in genic, promoter, CpG island, and upstream DNase I hypersensitivity site-proximal regions and expression in RWPE-1 cells. Insights from this model will subsequently be explored via whole-genome differential analysis between normal prostate and PCa to determine the effect of epigenetic alterations in cancer on gene expression. Subsequently, our characterization of key cellular pathways exhibiting dynamic enrichment patterns for 5hmC or 5mC marks will potentially allow us to identify differentially epigenetically modified target genes implicated in prostate cancer tumorigenesis. Citation Format: Shivani N. Kamdar, Linh T. Ho, Ruth Isserlin, Ken J. Kron, Theodorus van der Kwast, Alexandre R. Zlotta, Neil E. Fleshner, Gary Bader, Bharati Bapat. Genome-wide integrative analysis correlating locus-specific DNA methylation and hydroxymethylation to gene expression in normal prostate cells. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B1-32.

Functional and protein‑protein interaction network analysis of colorectal cancer induced by ulcerative colitis.

Colorectal cancer (CRC) is a well-recognized complication of ulcerative colitis (UC), and patients with UC have a higher incidence of CRC, compared with the general population. However, the properties of CRC induced by UC have not been clarified using an interaction network to analyze and compare gene sets. In the present study, six microarray datasets of CRC and UC were extracted from the Array Express database, and gene signatures were identified using the genome-wide relative significance (GWRS) method. Functional analysis was performed based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Prediction of the genes and microRNA were performed using a hypergeometric method. A protein-protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes/proteins, and clusters were obtained through the Molecular Complex Detection algorithm. Topological centrality and a novel analyzing method, based on the rank value of GWGS, were used to characterize the biological importance of the clusters. A total of 217 differentially expressed (DE) genes of CRC were identified, 341 DE genes were identified in UC, and 62 common genes existed in the two. Several KEGG pathways were the same in CRC and UC. Collagenase, progesterone, heparin, urokinase, nadh and adenosine drugs demonstrated potential for use in treatment of CRC and UC. In the PPI network of CRC, 210 nodes and 752 edges were observed, wheras 314 nodes and 882 edges were identified in UC. Cluster 3 in UC had the highest GWGS, while the topological centrality of Cluster 3 in UC had the lowest degree and betweenness. PPI network analysis provided an effective way to estimate and understand the likelihood of the potential connections between proteins/genes. The results obtained following the use of GWGS to analyze differences between clusters did not agree with the topological degree and betweenness centrality, which indicated that gene fold change based GWGS was controversial with degree here in CRC and UC.

Topological centrality-based identification of hub genes and pathways associated with acute viral respiratory infection in infants.

The objective of this study was to identify hub genes and pathways associated with acute respiratory infection (ARI) in infants based on gene expression profiles. Differentially expressed genes (DEGs) between ARI and normal (controls) infants were identified based on linear modeling of the microarray data using Limma package. A protein-protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes/proteins and clusters were obtained by employing the molecular complex detection algorithm. Topological centrality was applied to characterize the biological importance of genes in the network. Functional enrichment analysis of the genes was performed based on the expression analysis systematic explore test. In total, 116 DEGs between ARI and controls were identified. Of the 61 nodes and 189 edges in the PPI network generated with the DEGs, three clusters were mined. Six hub genes namely RPL6, RPL3, EEF1B2, RPL15, EEF1A1, and RPS2, which were identified based on the topological centrality measures, were evaluated further. Functional enrichment analysis revealed that DEGs were significantly enriched in terms of Gene Ontology translational elongation, structural constituent of ribosome, and cytosol. The most significant term of pathway analysis was "ribosome". In summary, this study suggests RPL6, RPL3, and RPL15 as hub genes and the ribosome pathway to be significantly associated with viral ARI in infants, which might also be used as potential markers for the viral etiology.

Effect of curcumin on aged Drosophila Melanogaster: A pathway prediction analysis

To re-analyze the data published in order to explore plausible biological pathways that can be used to explain the anti-aging effect of curcumin. Microarray data generated from other study aiming to investigate effect of curcumin on extending lifespan of Drosophila melanogaster were further used for pathway prediction analysis. The differentially expressed genes were identified by using GeneSpring GX with a criterion of 3.0-fold change. Two Cytoscape plugins including BisoGenet and molecular complex detection (MCODE) were used to establish the protein-protein interaction (PPI) network based upon differential genes in order to detect highly connected regions. The function annotation clustering tool of Database for Annotation, Visualization and Integrated Discovery (DAVID) was used for pathway analysis. A total of 87 genes expressed differentially in D. melanogaster melanogaster treated with curcumin were identified, among which 50 were up-regulated significantly and 37 were remarkably down-regulated in D. melanogaster melanogaster treated with curcumin. Based upon these differential genes, PPI network was constructed with 1,082 nodes and 2,412 edges. Five highly connected regions in PPI networks were detected by MCODE algorithm, suggesting anti-aging effect of curcumin may be underlined through five different pathways including Notch signaling pathway, basal transcription factors, cell cycle regulation, ribosome, Wnt signaling pathway, and p53 pathway. Genes and their associated pathways in D. melanogaster melanogaster treated with anti-aging agent curcumin were identified using PPI network and MCODE algorithm, suggesting that curcumin may be developed as an alternative therapeutic medicine for treating aging-associated diseases.

Integration of topological measures for eliminating non-specific interactions in protein interaction networks

High-throughput protein interaction assays aim to provide a comprehensive list of interactions that govern the biological processes in a cell. These large-scale sets of interactions, represented as protein–protein interaction networks, are often analyzed by computational methods for detailed biological interpretation. However, as a result of the tradeoff between speed and accuracy, the interactions reported by high-throughput techniques occasionally include non-specific (i.e., false-positive) interactions. Unfortunately, many computational methods are sensitive to noise in protein interaction networks; and therefore they are not able to make biologically accurate inferences. In this article, we propose a novel technique based on integration of topological measures for removing non-specific interactions in a large-scale protein–protein interaction network. After transforming a given protein interaction network using line graph transformation, we compute clustering coefficient and betweenness centrality measures for all the edges in the network. Motivated by the modular organization of specific protein interactions in a cell, we remove edges with low clustering coefficient and high betweenness centrality values. We also utilize confidence estimates that are provided by probabilistic interaction prediction techniques. We validate our proposed method by comparing the results of a molecular complex detection algorithm (MCODE) to a ground truth set of known Saccharomyces cerevisiae complexes in the MIPS complex catalogue database. Our results show that, by removing false-positive interactions in the S. cerevisiae network, we can significantly increase the biological accuracy of the complexes reported by MCODE.