scholarly journals BIOINFORMATICS APPROACH ON ACTION AND MECHANISM OF BROMELAIN IN HEPATOCELLULAR CARCINOMA

2021 ◽  
pp. 122-129
Author(s):  
SUSHMA S MURTHY ◽  
BALA NARSAIAH T
Keyword(s):  
Hepatocellular Carcinoma ◽  
Signaling Pathways ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Interaction Network ◽  
P53 Gene ◽  
Computational Method ◽  
Mesenchymal Transition ◽  
In Silico Studies ◽  
Data Points

Objective: The objective of the study was to understand biomolecular interactions of Bromelain and its networking with p53 and β-catenin by a computational method of analysis in Hepatocellular carcinoma (HCC) condition. Methodology: The protein interaction partners for p53 and β-catenin involved in the progression of HCC were collected from National Center for Biotechnology Information. We collected data points and standardized the data points for our data analysis from the public database. We used Cytoscape 3.8.2 version plug-in for constructing a Protein-Protein interaction network. We constructed a pathway network using Biorender.com. Results: The protein interactions concerning p53 and β-catenin are identified and a network is constructed. A total of 18 and 34 nodes were identified which are involved in down-regulation and up-regulation of β-catenin and a total of 30 and 27 nodes for homosapiens are identified which are involved in the downregulation and upregulation of the p53 gene. We identified different pathways which trigger and impact the p53 and Wnt/β- catenin signaling pathways as potential target sites for Bromelain to arrest the progression of cancer Conclusion: In conclusion, our in silico studies anti-cancer activity of Bromelain in HCC relating its effect on apoptosis, cell differentiation, mesenchymal transition, p53 signaling, and Wnt/β-catenin signaling pathways.

scholarly journals Predicting Long Noncoding RNA and Protein Interactions Using Heterogeneous Network Model

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Ao Li ◽  
Mengqu Ge ◽  
Yao Zhang ◽  
Chen Peng ◽  
Minghui Wang
Keyword(s):  
Network Model ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Heterogeneous Network ◽  
Noncoding Rna ◽  
Interaction Network ◽  
Computational Method ◽  
Protein Protein Interaction ◽  
Protein Interaction Prediction ◽  
Leave One Out

Recent study shows that long noncoding RNAs (lncRNAs) are participating in diverse biological processes and complex diseases. However, at present the functions of lncRNAs are still rarely known. In this study, we propose a network-based computational method, which is called lncRNA-protein interaction prediction based on Heterogeneous Network Model (LPIHN), to predict the potential lncRNA-protein interactions. First, we construct a heterogeneous network by integrating the lncRNA-lncRNA similarity network, lncRNA-protein interaction network, and protein-protein interaction (PPI) network. Then, a random walk with restart is implemented on the heterogeneous network to infer novel lncRNA-protein interactions. The leave-one-out cross validation test shows that our approach can achieve an AUC value of 96.0%. Some lncRNA-protein interactions predicted by our method have been confirmed in recent research or database, indicating the efficiency of LPIHN to predict novel lncRNA-protein interactions.

scholarly journals Mapping of TP53 protein network using Cytoscape software

2019 ◽  
Author(s):  
Asita Elengoe ◽  
Salehhuddin Hamdan
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Interaction Network ◽  
P53 Gene ◽  
Tp53 Gene ◽  
Protein Network ◽  
Protein Protein Interaction ◽  
Interaction Database ◽  
Cycle Arrest ◽  
Tp53 Protein

TP53 acts as a tumor suppressor in cancer. It induces cell cycle arrest or apoptosis in response to cellularstress and damage. p53 gene alteration could cause uncontrolled cell proliferation. In the present study, weused TP53 gene as the seed in the construction of a protein-protein interaction network to identify genesthat might involve in tumorgenesis process with TP53. TP53 protein interaction database was obtainedfrom STRING version 9.1 program. High-throughput experimental data, literature data and hypotheticalstudies have been used to determine the roles of candidate genes in TP53 pathway. A total 500 genes fromSTRING database loaded into Cytoscape version 2.8.3. The 1762 protein interactions were assembled andvisualized in y organic form. We found eight specific non-overlapping clusters of various sizes, whichemerged from the huge network of protein-interactors using MCODE version 1.32 clustering algorithm.Biological Networks Gene Ontology (BiNGO) was used to determine two ontologies (molecular function andbiological process) involved in the protein network. Most of the genes mainly participated in gene andprotein expression, cell signaling and metabolism. A better understanding of the relationship between thegenes could aid in developing prognostic markers and better therapeutic strategies in cancer treatment.

Extracting Biological Significant Subnetworks from Protein-Protein Interactions Induced by Differentially Expressed Genes of HIV-1 Vpr Variants

2015 ◽  
Vol 4 (4) ◽  
pp. 35-51 ◽  
Author(s):  
Bandana Barman ◽  
Anirban Mukhopadhyay
Keyword(s):  
Differentially Expressed Genes ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Differentially Expressed ◽  
Wild Type ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Hiv 1

Identification of protein interaction network is very important to find the cell signaling pathway for a particular disease. The authors have found the differentially expressed genes between two sample groups of HIV-1. Samples are wild type HIV-1 Vpr and HIV-1 mutant Vpr. They did statistical t-test and found false discovery rate (FDR) to identify the genes increased in expression (up-regulated) or decreased in expression (down-regulated). In the test, the authors have computed q-values of test to identify minimum FDR which occurs. As a result they found 172 differentially expressed genes between their sample wild type HIV-1 Vpr and HIV-1 mutant Vpr, R80A. They found 68 up-regulated genes and 104 down-regulated genes. From the 172 differentially expressed genes the authors found protein-protein interaction network with string-db and then clustered (subnetworks) the PPI networks with cytoscape3.0. Lastly, the authors studied significance of subnetworks with performing gene ontology and also studied the KEGG pathway of those subnetworks.

scholarly journals Visualization of Host-Polerovirus Interaction Topologies Using Protein Interaction Reporter Technology

2015 ◽  
Vol 90 (4) ◽  
pp. 1973-1987 ◽  
Author(s):  
Stacy L. DeBlasio ◽  
Juan D. Chavez ◽  
Mariko M. Alexander ◽  
John Ramsey ◽  
Jimmy K. Eng ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Interaction Network ◽  
Host Protein ◽  
Host Proteins ◽  
Content Type ◽  
Host Pathogen ◽  
Pathogen Protein ◽  
Binding Interfaces

ABSTRACTDemonstrating direct interactions between host and virus proteins during infection is a major goal and challenge for the field of virology. Most protein interactions are not binary or easily amenable to structural determination. Using infectious preparations of a polerovirus (Potato leafroll virus[PLRV]) and protein interaction reporter (PIR), a revolutionary technology that couples a mass spectrometric-cleavable chemical cross-linker with high-resolution mass spectrometry, we provide the first report of a host-pathogen protein interaction network that includes data-derived, topological features for every cross-linked site that was identified. We show that PLRV virions have hot spots of protein interaction and multifunctional surface topologies, revealing how these plant viruses maximize their use of binding interfaces. Modeling data, guided by cross-linking constraints, suggest asymmetric packing of the major capsid protein in the virion, which supports previous epitope mapping studies. Protein interaction topologies are conserved with other species in theLuteoviridaeand with unrelated viruses in theHerpesviridaeandAdenoviridae. Functional analysis of three PLRV-interacting host proteinsin plantausing a reverse-genetics approach revealed a complex, molecular tug-of-war between host and virus. Structural mimicry and diversifying selection—hallmarks of host-pathogen interactions—were identified within host and viral binding interfaces predicted by our models. These results illuminate the functional diversity of the PLRV-host protein interaction network and demonstrate the usefulness of PIR technology for precision mapping of functional host-pathogen protein interaction topologies.IMPORTANCEThe exterior shape of a plant virus and its interacting host and insect vector proteins determine whether a virus will be transmitted by an insect or infect a specific host. Gaining this information is difficult and requires years of experimentation. We used protein interaction reporter (PIR) technology to illustrate how viruses exploit host proteins during plant infection. PIR technology enabled our team to precisely describe the sites of functional virus-virus, virus-host, and host-host protein interactions using a mass spectrometry analysis that takes just a few hours. Applications of PIR technology in host-pathogen interactions will enable researchers studying recalcitrant pathogens, such as animal pathogens where host proteins are incorporated directly into the infectious agents, to investigate how proteins interact during infection and transmission as well as develop new tools for interdiction and therapy.

scholarly journals RNA regulons and the RNA-protein interaction network

2012 ◽  
Vol 3 (5) ◽  
pp. 403-414 ◽  
Author(s):  
Jochen Imig ◽  
Alexander Kanitz ◽  
André P. Gerber
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Interaction Network ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Interaction Network ◽  
Coordinated Control ◽  
Non Coding Rnas ◽  
Rna Interaction

AbstractThe development of genome-wide analysis tools has prompted global investigation of the gene expression program, revealing highly coordinated control mechanisms that ensure proper spatiotemporal activity of a cell’s macromolecular components. With respect to the regulation of RNA transcripts, the concept of RNA regulons, which – by analogy with DNA regulons in bacteria – refers to the coordinated control of functionally related RNA molecules, has emerged as a unifying theory that describes the logic of regulatory RNA-protein interactions in eukaryotes. Hundreds of RNA-binding proteins and small non-coding RNAs, such as microRNAs, bind to distinct elements in target RNAs, thereby exerting specific and concerted control over posttranscriptional events. In this review, we discuss recent reports committed to systematically explore the RNA-protein interaction network and outline some of the principles and recurring features of RNA regulons: the coordination of functionally related mRNAs through RNA-binding proteins or non-coding RNAs, the modular structure of its components, and the dynamic rewiring of RNA-protein interactions upon exposure to internal or external stimuli. We also summarize evidence for robust combinatorial control of mRNAs, which could determine the ultimate fate of each mRNA molecule in a cell. Finally, the compilation and integration of global protein-RNA interaction data has yielded first insights into network structures and provided the hypothesis that RNA regulons may, in part, constitute noise ‘buffers’ to handle stochasticity in cellular transcription.

scholarly journals Identifying Novel Candidate Genes Related to Apoptosis from a Protein-Protein Interaction Network

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Baoman Wang ◽  
Fei Yuan ◽  
Xiangyin Kong ◽  
Lan-Dian Hu ◽  
Yu-Dong Cai
Keyword(s):  
Cell Death ◽  
Candidate Genes ◽  
Protein Interaction ◽  
Permutation Test ◽  
Interaction Network ◽  
Weighted Graph ◽  
Computational Method ◽  
Protein Protein Interaction ◽  
Underlying Mechanisms ◽  
Multicellular Organisms

Apoptosis is the process of programmed cell death (PCD) that occurs in multicellular organisms. This process of normal cell death is required to maintain the balance of homeostasis. In addition, some diseases, such as obesity, cancer, and neurodegenerative diseases, can be cured through apoptosis, which produces few side effects. An effective comprehension of the mechanisms underlying apoptosis will be helpful to prevent and treat some diseases. The identification of genes related to apoptosis is essential to uncover its underlying mechanisms. In this study, a computational method was proposed to identify novel candidate genes related to apoptosis. First, protein-protein interaction information was used to construct a weighted graph. Second, a shortest path algorithm was applied to the graph to search for new candidate genes. Finally, the obtained genes were filtered by a permutation test. As a result, 26 genes were obtained, and we discuss their likelihood of being novel apoptosis-related genes by collecting evidence from published literature.

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