• Genomics & Informatics
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• 제6권4호
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• pp.202-209
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• 2008

Biological pathways are known as collections of knowledge of certain biological processes. Although knowledge about a pathway is quite significant to further analysis, it covers only tiny portion of genes that exists. In this paper, we suggest a model to extend each individual pathway using a microarray expression data based on the known knowledge about the pathway. We take the Rosetta compendium dataset to extend pathways of Saccharomyces cerevisiae obtained from KEGG (Kyoto Encyclopedia of genes and genomes) database. Before applying our model, we verify the underlying assumption that microarray data reflect the interactive knowledge from pathway, and we evaluate our scoring system by introducing performance function. In the last step, we validate proposed candidates with the help of another type of biological information. We introduced a pathway extending model using its intrinsic structure and microarray expression data. The model provides the suitable candidate genes for each single biological pathway to extend it.

• Asian Pacific Journal of Cancer Prevention
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• 제13권8호
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• pp.3905-3909
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• 2012

Ovarian cancer is the fifth leading cause of cancer death in women aged 35 to 74 years. Although there are several popular hypothesis of ovarian cancer pathogenesis, the genetic mechanisms are far from being clear. Recently, systems biology approaches such as network-based methods have been successfully applied to elucidate the mechanisms of diseases. In this study, we constructed a crosstalk network among ovarian cancer related pathways by integrating protein-protein interactions and KEGG pathway information. Several significant pathways were identified to crosstalk with each other in ovarian cancer, such as the chemokine, Notch, Wnt and NOD-like receptor signaling pathways. Results from these studies will provide the groundwork for a combination therapy approach targeting multiple pathways which will likely be more effective than targeting one pathway alone.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제12권2호
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• pp.167-172
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• 2012

In biology the advent of the high-throughput technology for sequencing, probing, or screening has produced huge volume of data which could not be manually handled. Biologists have resorted to software tools in order to effectively handle them. This paper introduces a bioinformatics tool to help biologists find potentially interesting pathway maps from a transcriptome data set in which the expression levels of genes are described for both case and control samples. The tool accepts a transcriptome data set, and then selects and categorizes some of genes into four classes using a fuzzy filtering technique where classes are defined by membership functions. It collects and edits the pathway maps related to those selected genes without analyst' intervention. It invokes a sequence of web service functions from KEGG, which an online pathway database system, in order to retrieve related information, locate pathway maps, and manipulate them. It maintains all retrieved pathway maps in a local database and presents them to the analysts with graphical user interface. The tool has been successfully used in identifying target genes for further analysis in transcriptome study of human cytomegalovirous. The tool is very helpful in that it can considerably save analysts' time and efforts by collecting and presenting the pathway maps that contain some interesting genes, once a transcriptome data set is just given.

• Asian-Australasian Journal of Animal Sciences
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• 제32권2호
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• pp.290-296
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• 2019

Objective: Pigs share many physiological, anatomical and genomic similarities with humans, which make them suitable models for biomedical researches. Understanding the genetic status of Yucatan miniature pigs (YMPs) and their association with human diseases will help to assess their potential as biomedical model animals. This study was performed to identify non-synonymous single nucleotide polymorphisms (nsSNPs) in selective sweep regions of the genome of YMPs and present the genetic nsSNP distributions that are potentially associated with disease occurrence in humans. Methods: nsSNPs in whole genome resequencing data from 12 YMPs were identified and annotated to predict their possible effects on protein function. Sorting intolerant from tolerant (SIFT) and polymorphism phenotyping v2 analyses were used, and gene ontology (GO) network and Kyoto encyclopedia of genes and genomes (KEGG) pathway analyses were performed. Results: The results showed that 8,462 genes, encompassing 72,067 nsSNPs were identified, and 118 nsSNPs in 46 genes were predicted as deleterious. GO network analysis classified 13 genes into 5 GO terms (p<0.05) that were associated with kidney development and metabolic processes. Seven genes encompassing nsSNPs were classified into the term associated with Alzheimer's disease by referencing the genetic association database. The KEGG pathway analysis identified only one significantly enriched pathway (p<0.05), hsa04080: Neuroactive ligand-receptor interaction, among the transcripts. Conclusion: The number of deleterious nsSNPs in YMPs was identified and then these variants-containing genes in YMPs data were adopted as the putative human diseases-related genes. The results revealed that many genes encompassing nsSNPs in YMPs were related to the various human genes which are potentially associated with kidney development and metabolic processes as well as human disease occurrence.

• Genomics & Informatics
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• 제4권3호
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• pp.118-124
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• 2006

For the direct understanding of flow, pathway data are usually represented as directed graphs in biological journals and texts. Databases of metabolic pathways or signal transduction pathways inevitably contain these kinds of graphs to show the flow. KEGG, one of the representative pathway databases, uses the manually drawn figure which can not be easily maintained. Graph layout algorithms are applied for visualizing metabolic pathways in some databases, such as EcoCyc. Although these can express any changes of data in the real time, it exponentially increases the edge crossings according to the increase of nodes. For the understanding of genome scale flow of metabolism, it is very important to reduce the unnecessary edge crossings which exist in the automatic graph layout. We propose a metabolic pathway drawing algorithm for reducing the number of edge crossings by considering the fact that metabolic pathway graph is scale-free network. The experimental results show that the number of edge crossings is reduced about $37{\sim}40%$ by the consideration of scale-free network in contrast with non-considering scale-free network. And also we found that the increase of nodes do not always mean that there is an increase of edge crossings.

• 대한치과보철학회지
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• 제55권4호
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• pp.361-371
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• 2017

목적: 마이크로그루브 상 인간치은섬유아세포의 유전자발현감식을 DNA microarray를 이용하여 연구하는 것이다. 재료 및 방법: Grade II 티타늄 시편을 이용하여 표면에 마이크로그루브(폭/깊이: $60{\mu}m/10{\mu}m$, E60/10)를 형성하고 불산으로 산에칭하여 실험군으로 사용하였다. 표면처리를 하지 않은 평활한 티타늄 표면(NE0)을 대조군으로 사용하였다. 실험군과 대조군에 인간치은섬유아세포를 배양한 후 total RNA를 추출하였다. Oligonucleotide microarray를 시행하여 실험군과 대조군 간 다양한 유전자 발현량의 변화를 확인하였다. KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis를 통해 DNA chip의 발현 결과를 mapping하여 실험 조건에 따른 유전자 발현량의 변화를 pathway 수준에서 파악하였다. 결과: E60/10 마이크로그루브 표면과 NE0 표면에 대한 유전자 발현량 비교분석 결과, NE0 표면에 비하여 E60/10 마이크로그루브 표면에서 1.5배 이상 유의한 발현 차이를 보인 유전자는 123개, 2배 이상 유의한 발현 차이를 보인 유전자는 19개였다. 실험 조건에 따른 유전자 발현량의 변화를 KEGG pathway analysis를 통하여 확인하였고, 다양한 유전자 발현 결과들 중 대표적인 세포접착, 증식, 활성 관련 세포신호전달을 규명하였다. 결론: 마이크로그루브 표면은 다양한 유전자 발현 변화를 유도하고 관련 세포신호 전달을 유도한다. 본 연구의 결과에 따라서, 마이크로그루브는 유전자 발현 변화 및 세포신호 전달 활성화 등을 통한 세포활성도 증진을 필요로 하는 다양한 생체재료들의 표면으로 사용될 수 있다.

• 한방안이비인후피부과학회지
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• 제22권3호
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• pp.47-62
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• 2009

Glenditsia spina (GS) can resolve carbuncle, relive swelling, dispel wind and destroy parasites. For these reasons, GS has been widely used as dermatologic agent clinically. In this study, the specific pathways of anti-proliferative effect of GS on human derived melanoma cells were identified. The molecular profile was measured using microarray technique to identify up- or down-regulated genes in SK-MEL-2 cell line. Pathway analysis was done by listing percentage of pathway involvement, and the represented pathways were obtained from KEGG. The transcription factor binding sequences were obtained by Transfac database. By the promoter analysis, up-regulated genes by GS were mainly associated with MAPK, Regulation of actin cytoskeleton, Wnt, Focal adhesion and Long term potentiation pathway. Down-regulated genes by GS were mainly associated with MAPK and Antigen processing and presentation pathway. And some of the transcription factors like Sp1 and NF-Y in up-regulated genes and Oct-1 in down-regulated genes by GS also identified.

• Genomics & Informatics
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• 제10권1호
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• pp.9-15
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• 2012

Horizontal gene transfer (HGT) is the movement of genetic material between kingdoms and is considered to play a positive role in adaptation. $Cryptosporidium$ $parvum$ is a parasitic protozoan that causes an infectious disease. Its genome sequencing reported 14 bacteria-like proteins in the nuclear genome. Among them, cgd2_1810, which has been annotated as CysQ, a sulfite synthesis pathway protein, is listed as one of the candidates of genes horizontally transferred from bacterial origin. In this report, we examined this issue using phylogenetic analysis. Our BLAST search showed that $C.$ $parvum$ CysQ protein had the highest similarity with that of proteobacteria. Analysis with NCBI's Conserved Domain Tree showed phylogenetic incongruence, in that $C.$ $parvum$ CysQ protein was located within a branch of proteobacteria in the cd01638 domain, a bacterial member of the inositol monophosphatase family. According to Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, the sulfate assimilation pathway, where CysQ plays an important role, is well conserved in most eukaryotes as well as prokaryotes. However, the Apicomplexa, including $C.$ $parvum$, largely lack orthologous genes of the pathway, suggesting its loss in those protozoan lineages. Therefore, we conclude that $C.$ $parvum$ regained cysQ from proteobacteria by HGT, although its functional role is elusive.

• 대한본초학회지
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• 제38권1호
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• pp.45-53
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• 2023

Objectives : Network pharmacology-based research is one of useful tool to predict the possible efficacy and molecular mechanisms of natural materials with multi compounds-multi targeting effects. In this study, we investigated the functional underlying mechanisms of Astragalus membranaceus Bunge (AM) on its anti-obesity effects using a network pharmacology analysis. Methods : The constituents of AM were collected from public databases and its target genes were gathered from PubChem database. The target genes of AM were compared with the gene set of obesity to find the correlation. Then, the network was constructed by Cytoscape 3.9.1. and functional enrichment analysis was conducted to predict the most relevant pathway of AM. Results : The result showed that AM network contained the 707 nodes and 6867 edges, and 525 intersecting genes were exhibited between AM and obesity gene set, indicating that high correlation with the effects of AM on obesity. Based on GO biological process and KEGG Pathway, 'Response to lipid', 'Cellular response to lipid', 'Lipid metabolic process', 'Regulation of chemokine production', 'Regulation of lipase activity', 'Chemokine signaling pathway', 'Regulation of lipolysis in adipocytes' and 'PPAR signaling pathway' were predicted as functional pathways of AM on obesity. Conclusions : AM showed high relevance with the lipid metabolism related with the chemokine production and lipolysis pathways. This study could be a basis that AM has promising effects on obesity via network pharmacology analysis.

• Genomics & Informatics
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• 제2권4호
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• pp.191-194
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• 2004

Exponentially increasing biopathway data in recent years provide us with means to elucidate the large-scale modular organization of the cell. Given the existing information on metabolic and regulatory networks, inferring biopathway information through scientific reasoning or data mining of large scale array data or proteomics data get great attention. Naturally, there is a need for a user-friendly system allowing the user to combine large and diverse pathway data sets from different resources. We built a data warehouse - BIOWAY - for analyzing and visualizing biological pathways, by integrating and customizing resources. We have collected many different types of data in regards to pathway information, including metabolic pathway data from KEGG/LIGAND, signaling pathway data from BIND, and protein information data from SWISS-PROT. In addition to providing general data retrieval mechanism, a successful user interface should provide convenient visualization mechanism since biological pathway data is difficult to conceptualize without graphical representations. Still, the visual interface in the previous systems, at best, uses static images only for the specific categorized pathways. Thus, it is difficult to cope with more complex pathways. In the BIOWAY system, all the pathway data can be displayed in computer generated graphical networks, rather than manually drawn image data. Furthermore, it is designed in such a way that all the pathway maps can be expanded or shrinked, by introducing the concept of super node. A subtle graphic layout algorithm has been applied to best display the pathway data.