• Asian Pacific Journal of Cancer Prevention
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• v.15 no.22
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• pp.9791-9795
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• 2014

To study the gene expression change and possible signal pathway during androgen-dependent prostate cancer (ADPC) becoming androgen-independent prostate cancer (AIPC), an LNCaP cell model of AIPC was established using flutamide in combination with androgen-free environment inducement, and differential expression genes were screened by microarray. Then the biological process, molecular function and KEGG pathway of differential expression genes are analyzed by Molecule Annotation System (MAS). By comparison of 12,207 expression genes, 347 expression genes were acquired, of which 156 were up-ragulated and 191 down-regulated. After analyzing the biological process and molecule function of differential expression genes, these genes are found to play crucial roles in cell proliferation, differntiation, cell cycle control, protein metabolism and modification and other biological process, serve as signal molecules, enzymes, peptide hormones, cytokines, cytoskeletal proteins and adhesion molecules. The analysis of KEGG show that the relevant genes of AIPC transformation participate in glutathione metabolism, cell cycle, P53 signal pathway, cytochrome P450 metabolism, Hedgehog signal pathway, MAPK signal pathway, adipocytokines signal pathway, PPAR signal pathway, TGF-${\beta}$ signal pathway and JAK-STAT signal pathway. In conclusion, during the process of ADPC becoming AIPC, it is not only one specific gene or pathway, but multiple genes and pathways that change. The findings above lay the foundation for study of AIPC mechanism and development of AIPC targeting drugs.

• Journal of Microbiology and Biotechnology
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• v.31 no.4
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• pp.621-629
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• 2021

Shigella flexneri is a facultative intracellular pathogen that causes bacillary dysentery in humans. Infection with S. flexneri can result in more than a million deaths yearly and most of the victims are children in developing countries. Therefore, identifying novel and unique drug targets against this pathogen is instrumental to overcome the problem of drug resistance to the antibiotics given to patients as the current therapy. In this study, a comparative analysis of the metabolic pathways of the host and pathogen was performed to identify this pathogen's essential enzymes for the survival and propose potential drug targets. First, we extracted the metabolic pathways of the host, Homo sapiens, and pathogen, S. flexneri, from the KEGG database. Next, we manually compared the pathways to categorize those that were exclusive to the pathogen. Further, all enzymes for the 26 unique pathways were extracted and submitted to the Geptop tool to identify essential enzymes for further screening in determining the feasibility of the therapeutic targets that were predicted and analyzed using PPI network analysis, subcellular localization, druggability testing, gene ontology and epitope mapping. Using these various criteria, we narrowed it down to prioritize 5 novel drug targets against S. flexneri and one vaccine drug targets against all strains of Shigella. Hence, we suggest the identified enzymes as the best putative drug targets for the effective treatment of S. flexneri.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.8
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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.

• Journal of Plant Biotechnology
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• v.43 no.2
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• pp.189-203
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• 2016

Brassica oleraceae var capitata is a member of the Brassicaceae family and is widely used as an horticultural crop. In the present study, transcriptome analysis of B. oleraceae L. var capitata was done for the first time using eight-week old seedlings treated with $50{\mu}m$ MeJA, versus mock-treated samples. The complete transcripts for both samples were obtained using the GS-FLX sequencer. Overall, we obtained 275,570 and 266,457 reads from seedlings treated with or without $50{\mu}m$ MeJA, respectively. All the obtained reads were annotated using biological databases and functionally classified using gene ontology (GO), the Kyoto Encyclopedia of Genes and Genomics (KEGG). By using GO analyses, putative transcripts were examined in terms of biotic and abiotic stresses, cellular component organization, biogenesis, and secondary metabolic processes. The KEGG pathways for most of the transcripts were involved in carbohydrate metabolism, energy metabolism, and secondary metabolite synthesis. In order to double the sequenced data, we randomly chose two putative genes involved in terpene biosynthetic pathways and studied their transcript patterns under MeJA treatment. This study will provide us a platform to further characterize the genes in B. oleracea var capitata.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.2
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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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• v.32 no.2
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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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• v.6 no.4
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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.

• The Journal of Korean Medicine Ophthalmology and Otolaryngology and Dermatology
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• v.22 no.3
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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.

• The Korea Journal of Herbology
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• v.38 no.1
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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.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.4
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• pp.19-26
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• 2008

Gene set enrichment analysis (GSEA) is a computational method to identify statistically significant gene sets showing significant differences between two groups of microarray expression profiles and simultaneously uncover their biological meanings in an elegant way by employing gene annotation databases, such as Cytogenetic Band, KEGG pathways, gene ontology, and etc. For the gone set enrichment analysis, all the genes in a given dataset are first ordered by the signal-to-noise ratio between the groups and then further analyses are proceeded. Despite of its impressive results in several previous studies, however, gene ranking by the signal-to-noise ratio makes it difficult to consider highly up-regulated genes and highly down-regulated genes at the same time as the candidates of significant genes, which possibly reflect certain situations incurred in metabolic and signaling pathways. To deal with this problem, in this article, we investigate the gene set enrichment analysis method with Fisher criterion for gene ranking and also evaluate its effects in Leukemia related pathway analyses.