• IMMUNE NETWORK
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• v.2 no.3
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• pp.137-141
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• 2002

Among the members of the inhibitor of apoptosis (IAP) protein family, only Livin and survivin have been reported to have variant forms. We have found a variant form of c-IAP2 through the interaction with the X protein of HBV using the yeast two-hybrid system. In contrast to the wild-type c-IAP2, the variant form has two stretches of sequence in the RING domain that are repeated in the C-terminus that would disrupt the RING domain. We demonstrate that the variant form has an inhibitory effect on TNF-mediated $NF-{\kappa}B$ activation unlike the wild-type c-IAP2, which increases TNFmediated $NF-{\kappa}B$ activation. These results suggest that this variant form has different activities from the wild-type and the RING domain may be involved in the regulation of TNF-induced $NF-{\kappa}B$ activation.

• Oncology Letters
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• v.18 no.3
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• pp.3388-3398
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• 2019

Leucine rich repeat LGI family member 3 (LGI3) is a member of the LGI protein family. Our previous studies reported that LGI3 was expressed in adipose tissues, brain and skin, where it served roles as a multifunctional cytokine and pro-inflammatory adipokine. It was hypothesized that LGI3 may be involved in cytokine networks in cancer. The present study aimed to analyze differentially expressed genes in non-small cell lung cancer (NSCLC) tissues and NSCLC cohort data, to evaluate the prognostic role of LGI3. Expression microarray and NSCLC cohort data were statistically analyzed by bioinformatic methods, and protein-protein interactions, functional enrichment and pathway, gene coexpression network (GCN) and prognostic association analyses were performed. The results demonstrated that the expression levels of LGI3 and its receptor a disintegrin and metalloproteinase domain-containing protein 22 were significantly decreased in NSCLC tissues. A total of two upregulated genes and 11 downregulated genes in NSCLC tissues were identified as LGI3-regulated genes. Protein-protein interaction network analysis demonstrated that all LGI3-regulated genes that were altered in NSCLC were involved in a protein-protein interaction network cluster. Functional enrichment, Kyoto Encyclopedia of Genes and Genomes pathway and GCN analyses demonstrated the association of these genes with the immune and inflammatory responses, angiogenesis, the tumor necrosis factor pathway, and chemokine and peroxisome proliferator-activated receptor signaling pathways. Analysis of NSCLC cohorts revealed that low expression levels of LGI3 was significantly associated with poor prognosis of NSCLC. Analysis of the somatic mutations of the LGI3 gene in NSCLC revealed that the amino acid residues altered in NSCLC included two single nucleotide polymorphism sites and three phylogenetically coevolved amino acid residues. Taken together, these results suggest that LGI3 may be a potential prognostic marker of NSCLC.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.41-47
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• 2021

Detection of protein-protein interactions (PPIs) remains essential for the development of therapies against diseases. Experimental studies to detect PPI are longer and more expensive. Today, with the availability of PPI data, several computer models for predicting PPIs have been proposed. One of the big challenges in this task is feature extraction. The relevance of the information extracted by some extraction techniques remains limited. In this work, we first propose an extraction method based on correlation relationships between the physicochemical properties of amino acids. The proposed method uses a correlation matrix obtained from the hydrophobicity and hydrophilicity properties that it then integrates in the calculation of the bigram. Then, we use the SVM algorithm to detect the presence of an interaction between 2 given proteins. Experimental results show that the proposed method obtains better performances compared to the approaches in the literature. It obtains performances of 94.75% in accuracy, 95.12% in precision and 96% in sensitivity on human HPRD protein data.

• Proceedings of the Korea Contents Association Conference
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• 2008.05a
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• pp.842-845
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• 2008

The protein interaction network contains a small number of highly connected protein, denoted hub and many destitutely connected proteins. Recently, several studies described that a hub protein is more likely to be essential than a non-hub protein. This phenomenon called as the centrality-lethality rule. This rule is widely credited to exhibit the importance of hub proteins in the complex network and the significance of network architecture as well. To confirm whether the rule is accurate, we investigated all protein interaction DBs of yeast in the public sites such as Uetz, Ito, MIPS, DIP, SGD, and BioGRID. Interestingly, the protein network shows that the rule is correct in lower scale DBs (e.g., Uetz, Ito, and DIP) but is not correct in higher scale DBs (e.g., SGD and BioGRID). We are now analyzing the features of networks obtained from the SGD and BioGRD and comparing those of network from the DIP.

• Genomics & Informatics
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• v.21 no.2
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• pp.23.1-23.9
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• 2023

The mammalian sirtuin family, consisting of SIRT1-SIRT7, plays a vital role in various biological processes, including cancer, diabetes, neurodegeneration, cardiovascular disease, cellular metabolism, and cellular homeostasis maintenance. Due to their involvement in these biological processes, modulating sirtuin activity seems promising to impact immuneand aging-related diseases, as well as cancer pathways. However, more understanding is required regarding the safety and efficacy of sirtuin-targeted therapies due to the complex regulatory mechanisms that govern their activity, particularly in the context of multiple targets. In this study, the interaction landscape of the sirtuin family was analyzed using a systems biology approach. A sirtuin protein-protein interaction network was built using the Cytoscape platform and analyzed using the NetworkAnalyzer and stringApp plugins. The result revealed the sirtuin family's association with numerous proteins that play diverse roles, suggesting a complex interplay between sirtuins and other proteins. Based on network topological and functional analysis, SIRT1 was identified as the most prominent among sirtuin family members, demonstrating that 25 of its protein partners are involved in cancer, 22 in innate immune response, and 29 in aging, with some being linked to a combination of two or more pathways. This study lays the foundation for the development of novel therapies that can target sirtuins with precision and efficacy. By illustrating the various interactions among the proteins in the sirtuin family, we have revealed the multifaceted roles of SIRT1 and provided a framework for their possible roles to be precisely understood, manipulated, and translated into therapeutics in the future.

• Journal of the Korea Society of Computer and Information
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• v.20 no.2
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• pp.137-147
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• 2015

This study proposes an algorithm for predicting breast cancer prognosis based on genetic network. We identify prognosis-specific network using gene expression data and PPI(protein-protein interaction) data. To acquire the network, we calculate Pearson's correlation coefficient(PCC) between genes in all PPI pairs using gene expression data. We develop a prediction model for breast cancer patients with estrogen-receptor-negative using the network as a classifier. We compare classification performance of our algorithm with existing algorithms on independent data and shows our algorithm is improved. In addition, we make an functionality analysis on the genes in the prognosis-specific network using GO(Gene Ontology) enrichment validation.

• CELLMED
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• v.4 no.1
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• pp.6.1-6.12
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• 2014

Anthrax is the deadly disease for human being caused by Bacillus anthracis. Instantaneous research work on the mode of infection of the organism revealed that different proteases are involved in different steps of pathogenesis. Present study reports the in silico characterization and the detection of pathogenic proteases involved in anthrax infection through protein-protein interaction. A total of 13 acid, 9 neutral, and 1 alkaline protease of Bacillus anthracis were selected for analysing the physicochemical parameter, the protein superfamily and family search, multiple sequence alignment, phylogenetic tree construction, protein-protein interactions and motif finding. Among the 13 acid proteases, 10 were found as extracellular enzymes that interact with immune inhibitor A (InhA) and help the organism to cross the blood brain barrier during the process of infection. Multiple sequence alignment of above acid proteases revealed the position 368, 489, and 498-contained 100% conserved amino acids which could be used to deactivate the protease. Among the groups analyzed, only acid protease were found to interact with InhA, which indicated that metalloproteases of acid protease group have the capability to develop pathogenesis during B. anthracis infection. Deactivation of conserved amino acid position of germination protease can stop the sporulation and germination of B anthracis cell. The detailed interaction study of neutral and alkaline proteases could also be helpful to design the interaction network for the better understanding of anthrax disease.

• Proceedings of the Korean Society of Plant Pathology Conference
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• 2003.10a
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• pp.29-29
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• 2003

Incompatible plant-pathogen interactions result in the rapid cell death response known as hypersensitive response (HR) and activation of host defense-related genes. To understand the molecular and cellular mechanism controlling defense response better, several approaches including isolation and characterization of novel genes, promoter analysis of those genes, protein-protein interaction analysis and reverse genetic approach etc. By using the yeast two-hybrid system a clone named Tsipl, Tsil -interacting protein 1, was isolated whose translation product apparently interacted with Tsil, an EREBP/AP2 type DNA binding protein. RNA gel blot analysis showed that the expression of Tsipl was increased by treatment with NaCl, ethylene, salicylic acid, or gibberellic acid. Transient expression analysis using a Tsipl::smGFP fusion gene in Arabidopsis protoplasts indicated that the Tsipl protein was targeted to the outer surface of chloroplasts. The targeted Tsipl::smGFP proteins were diffused to the cytoplasm of protoplasts in the presence of salicylic acid (SA) The PEG-mediated co-transfection analysis showed that Tsipl could interact with Tsil in the nucleus. These results suggest that Tsipl-Tsil interaction might serve to regulate defense-related gene expression. Basically the useful promoters are valuable tools for effective control of gene expression related to various developmental and environmental condition.(중략)

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.6
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• pp.2561-2567
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• 2015

Pancreatic ductal adenocarcinoma (PDAC) is a leading cause of cancer death worldwide. Our study aimed to reveal molecular mechanisms. Microarray data of GSE15471 (including 39 matching pairs of pancreatic tumor tissues and patient-matched normal tissues) was downloaded from Gene Expression Omnibus (GEO) database. We identified differentially expressed genes (DEGs) in PDAC tissues compared with normal tissues by limma package in R language. Then GO and KEGG pathway enrichment analyses were conducted with online DAVID. In addition, principal component analysis was performed and a protein-protein interaction network was constructed to study relationships between the DEGs through database STRING. A total of 532 DEGs were identified in the 38 PDAC tissues compared with 33 normal tissues. The results of principal component analysis of the top 20 DEGs could differentiate the PDAC tissues from normal tissues directly. In the PPI network, 8 of the 20 DEGs were all key genes of the collagen family. Additionally, FN1 (fibronectin 1) was also a hub node in the network. The genes of the collagen family as well as FN1 were significantly enriched in complement and coagulation cascades, ECM-receptor interaction and focal adhesion pathways. Our results suggest that genes of collagen family and FN1 may play an important role in PDAC progression. Meanwhile, these DEGs and enriched pathways, such as complement and coagulation cascades, ECM-receptor interaction and focal adhesion may be important molecular mechanisms involved in the development and progression of PDAC.

• Journal of the Korea Society of Computer and Information
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• v.21 no.1
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• pp.115-123
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• 2016

In this study, we collect various side effect pairs which are appeared frequently at many drugs, and select side effect pairs that have higher severity. For every selected side effect pair, we extract common genetic networks which are shared by side effects' genes and drugs' target genes based on PPI(Protein-Protein Interaction) network. For this work, firstly, we gather drug related data, side effect data and PPI data. Secondly, for extracting common genetic network, we find shortest paths between drug target genes and side effect genes based on PPI network, and integrate these shortest paths. Thirdly, we develop a classification model which uses this common genetic network as a classifier. We calculate similarity score between the common genetic network and genetic network of a drug for classifying the drug. Lastly, we validate our classification model by means of AUC(Area Under the Curve) value.