• Asian Pacific Journal of Cancer Prevention
• /
• v.16 no.10
• /
• pp.4251-4256
• /
• 2015

Background: Exposure to cigarette may affect human health and increase risk of a wide range of diseases including pulmonary diseases, such as chronic obstructive pulmonary disease (COPD), asthma, lung fibrosis and lung cancer. However, the molecular mechanisms of pathogenesis induced by cigarettes still remain obscure even with extensive studies. With systemic view, we attempted to identify the specific gene modules that might relate to injury caused by cigarette smoke and identify hub genes for potential therapeutic targets or biomarkers from specific gene modules. Materials and Methods: The dataset GSE18344 was downloaded from the Gene Expression Omnibus (GEO) and divided into mouse cigarette smoke exposure and control groups. Subsequently, weighted gene co-expression network analysis (WGCNA) was used to construct a gene co-expression network for each group and detected specific gene modules of cigarette smoke exposure by comparison. Results: A total of ten specific gene modules were identified only in the cigarette smoke exposure group but not in the control group. Seven hub genes were identified as well, including Fip1l1, Anp32a, Acsl4, Evl, Sdc1, Arap3 and Cd52. Conclusions: Specific gene modules may provide better understanding of molecular mechanisms, and hub genes are potential candidates of therapeutic targets that may possible improve development of novel treatment approaches.

• Genomics & Informatics
• /
• v.9 no.4
• /
• pp.194-196
• /
• 2011

Promoter prediction is a very important problem and is closely related to the main problems of bioinformatics such as the construction of gene regulatory networks and gene function annotation. In this context, we developed an integrated promoter prediction program using hybrid methods, PromoterWizard, which can be employed to detect the core promoter region and the transcription start site (TSS) in vertebrate genomic DNA sequences, an issue of obvious importance for genome annotation efforts. PromoterWizard consists of three main modules and two auxiliary modules. The three main modules include CDRM (Composite Dependency Reflecting Model) module, SVM (Support Vector Machine) module, and ICM (Interpolated Context Model) module. The two auxiliary modules are CpG Island Detector and GCPlot that may contribute to improving the predictive accuracy of the three main modules and facilitating human curator to decide on the final annotation.

• Reproductive and Developmental Biology
• /
• v.37 no.4
• /
• pp.185-192
• /
• 2013

Many countries have implemented genetic evaluation for fertility traits in recent years. In particular, reproductive trait is a complex trait and need to require a system-level approach for identifying candidate genes related to the trait. To find the candidate gene associated with reproductive trait, we applied a weighted gene co-expression network analysis from expression value of bovine genes. We identified three co-expressed modules associated with reproductive trait from bovine microarray data. Hub genes (ZP4, FHL2 and EGR4) were determined in each module; they were topologically centered with statistically significant value in the gene co-expression network. We were able to find the highly co-expressed gene pairs with a correlation coefficient. Finally, the crucial functions of co-expressed modules were reported from functional enrichment analysis. We suggest that the network-based approach in livestock may an important method for analyzing the complex effects of candidate genes associated with economic traits like reproduction.

• Journal of the Korea Society of Computer and Information
• /
• v.15 no.12
• /
• pp.197-207
• /
• 2010

Using a variety of data-mining methods on high-throughput cDNA microarray data, the level of gene expression in two different tissues can be compared, and DEG(Differentially Expressed Gene) genes in between normal cell and tumor cell can be detected. Diagnosis can be made with these genes, and also treatment strategy can be determined according to the cancer stages. Existing cancer classification methods using machine learning select the marker genes which are differential expressed in normal and tumor samples, and build a classifier using those marker genes. However, in addition to the differences in gene expression levels, the difference in gene-gene correlations between two conditions could be a good marker in disease diagnosis. In this study, we identify gene pairs with a big correlation difference in two sets of samples, build gene classification modules using these gene pairs. This cancer classification method using gene modules achieves higher accuracy than current methods. The implementing clinical kit can be considered since the number of genes in classification module is small. For future study, Authors plan to identify novel cancer-related genes with functionality analysis on the genes in a classification module through GO(Gene Ontology) enrichment validation, and to extend the classification module into gene regulatory networks.

• Genomics & Informatics
• /
• v.7 no.2
• /
• pp.122-130
• /
• 2009

A systems biology approach for the identification of perturbed molecular functions is required to understand the complex progressive disease such as breast cancer. In this study, we analyze the microarray data with Gene Ontology terms of molecular functions to select perturbed molecular functional modules in breast cancer tissues based on the definition of Gene ontology Functional Code. The Gene Ontology is three structured vocabularies describing genes and its products in terms of their associated biological processes, cellular components and molecular functions. The Gene Ontology is hierarchically classified as a directed acyclic graph. However, it is difficult to visualize Gene Ontology as a directed tree since a Gene Ontology term may have more than one parent by providing multiple paths from the root. Therefore, we applied the definition of Gene Ontology codes by defining one or more GO code(s) to each GO term to visualize the hierarchical classification of GO terms as a network. The selected molecular functions could be considered as perturbed molecular functional modules that putatively contributes to the progression of disease. We evaluated the method by analyzing microarray dataset of breast cancer tissues; i.e., normal and invasive breast cancer tissues. Based on the integration approach, we selected several interesting perturbed molecular functions that are implicated in the progression of breast cancers. Moreover, these selected molecular functions include several known breast cancer-related genes. It is concluded from this study that the present strategy is capable of selecting perturbed molecular functions that putatively play roles in the progression of diseases and provides an improved interpretability of GO terms based on the definition of Gene Ontology codes.

• Journal of Microbiology and Biotechnology
• /
• v.26 no.9
• /
• pp.1636-1642
• /
• 2016

Phenalamide is a bioactive secondary metabolite produced by Myxococcus stipitatus. We identified a 56 kb phenalamide biosynthetic gene cluster from M. stipitatus DSM 14675 by genomic sequence analysis and mutational analysis. The cluster is comprised of 12 genes (MYSTI_04318- MYSTI_04329) encoding three pyruvate dehydrogenase subunits, eight polyketide synthase modules, a non-ribosomal peptide synthase module, a hypothetical protein, and a putative flavin adenine dinucleotide-binding protein. Disruption of the MYSTI_04324 or MYSTI_04325 genes by plasmid insertion resulted in a defect in phenalamide production. The organization of the phenalamide biosynthetic modules encoded by the fifth to tenth genes (MYSTI_04320-MYSTI_04325) was very similar to that of the myxalamid biosynthetic gene cluster from Stigmatella aurantiaca Sg a15, as expected from similar backbone structures of the two substances. However, the loading module and the first extension module of the phenalamide synthase encoded by the first to fourth genes (MYSTI_04326-MYSTI_04329) were found only in the phenalamide biosynthetic gene cluster from M. stipitatus DSM 14675.

• Journal of Microbiology and Biotechnology
• /
• v.17 no.10
• /
• pp.1675-1681
• /
• 2007

The pseudodisaccharide salbostatin, which consists of valienamine linked to 2-amino-1,5-anhydro-2-deoxyglucitol, is a strong trehalase inhibitor. From our Streptomyces albus ATCC 21838 genomic library, we identified thirty-two ORFs in a 37-kb gene cluster. Twenty-one genes are supposed to be a complete set of modules responsible for the salbostatin biosynthesis. Through sequence analysis of the gene cluster, some of the upstream gene products (SalB, SalC, SalD, SalE, and SalF) revealed functional resemblance with trehalose biosynthetic enzymes. On the basis of this rationale, we isolated the five genes (salB, salC, salD, salE, and salF) from the S. albus ATCC 21838 and cloned them into the expression vector pWHM3. We demonstrated the noticeable expression and accumulation of trehalose, using only the five upstream biosynthetic gene cluster of salbostatin, in the transformed Streptomyces lividans TK24. Finally, 490 mg/l trehalose was produced by fermentation of the transformant with sucrosedepleted R2YE media.

• Asian Pacific Journal of Cancer Prevention
• /
• v.14 no.3
• /
• pp.1731-1735
• /
• 2013

Objective: We aimed to identify key genes, pathways and function modules in the development of diffuse large B-cell lymphoma (DLBCL) with microarray data and interaction network analysis. Methods: Microarray data sets for 7 DLBCL samples and 7 normal controls was downloaded from the Gene Expression Omnibus (GEO) database and differentially expressed genes (DEGs) were identified with Student's t-test. KEGG functional enrichment analysis was performed to uncover their biological functions. Three global networks were established for immune system, signaling molecules and interactions and cancer genes. The DEGs were compared with the networks to observe their distributions and determine important key genes, pathways and modules. Results: A total of 945 DEGs were obtained, 272 up-regulated and 673 down-regulated. KEGG analysis revealed that two groups of pathways were significantly enriched: immune function and signaling molecules and interactions. Following interaction network analysis further confirmed the association of DEGs in immune system, signaling molecules and interactions and cancer genes. Conclusions: Our study could systemically characterize gene expression changes in DLBCL with microarray technology. A range of key genes, pathways and function modules were revealed. Utility in diagnosis and treatment may be expected with further focused research.

• Genomics & Informatics
• /
• v.10 no.4
• /
• pp.234-238
• /
• 2012

Genetic epidemiology studies have established that the natural variation of gene expression profiles is heritable and has genetic bases. A number of proximal and remote DNA variations, known as expression quantitative trait loci (eQTLs), that are associated with the expression phenotypes have been identified, first in Epstein-Barr virus-transformed lymphoblastoid cell lines and later expanded to other cell and tissue types. Integration of the eQTL information and the network analysis of transcription modules may lead to a better understanding of gene expression regulation. As these network modules have relevance to biological or disease pathways, these findings may be useful in predicting disease susceptibility.

• Proceedings of the Microbiological Society of Korea Conference
• /
• 2005.05a
• /
• pp.174-174
• /
• 2005