• 치위생과학회지
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• 제16권6호
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• pp.401-408
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• 2016

Periodontal disease is one of the major dental diseases. Currently, various methods are used for healing and successful regeneration of periodontal tissue damaged by periodontal disease. The periodontal ligament and alveolar bone have received considerable interest for use in periodontal tissue regeneration and induction. However, as the functions of the factors required for tooth attachment and key regulatory factors for periodontal tissue regeneration in the cementum have recently been identified, interest in cementum formation and regeneration has increased. Dental cementum forms in the late phase of tooth development because of the reciprocal regulatory interaction between cervical loop epithelial cells and surrounding mesenchymal cells, which is regulated by various gene signaling networks. Many attempts have been made to understand the regulatory factors and cellular and molecular mechanisms associated with new cementum formation. In this paper, we reviewed the study outcomes to date on the regulatory factors that induce cementum formation and regeneration, focusing on understanding the roles and functions of Wnt signaling in the regulation of cementum formation. In addition, we aimed to obtain information on the useful reciprocal regulatory factors that mediate cementum formation and regeneration through a series of molecular mechanisms.

• 한국작물학회:학술대회논문집
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• 한국작물학회 2017년도 9th Asian Crop Science Association conference
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• pp.164-164
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• 2017

High throughput transcriptome investigations of immunity in plants highlight the complexity of gene networks leading to incompatible interaction. To identify genes crucial to resistance against Xanthomonas oryzae pv oryzae, functional genetic analysis of selected differentially expressed genes from our microarray data set was carried out. A total of 13 overexpression vector constructs were made using 35S CaMV promoter which drive constitutive expression in rice. Most of the genes are developmentally expressed especially during maximum tillering stage and are commonly highly expressed in the leaves. When screened against Xoo strain K2, the transgenic plants displayed shorter lesion length compared with wild type Dongjin which indicates partial resistance. The levels of ROS continuously magnified after inoculation which indicates robust cellular sensing necessary to initiate cell death. Elevated transcripts levels of several defense-related genes at the downstream of defense signal network also corroborate the phenotype reaction of the transgenic plants. Moreover, expression assays revealed regulation of these genes by cross-communicating signal-transductions pathways mediated by salicylic and jasmonic acid. These collective findings revealed the key immune signaling conduits critical to mount full defense against Xoo.

• Asian Pacific Journal of Cancer Prevention
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• 제15권5호
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• pp.2273-2278
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• 2014

Purpose: The purpose of our study was to explore the molecular mechanisms in the process of oral squamous cells carcinoma (OSCC) development. Method: We downloaded the affymetrix microarray data GSE31853 and identified differentially expressed genes (DEGs) between OSCC and normal tissues. Then Gene Ontology (GO) and Protein-Protein interaction (PPI) networks analysis was conducted to investigate the DEGs at the function level. Results: A total 372 DEGs with logFCI >1 and P value < 0.05 were obtained, including NNMT, BAX, MMP9 and VEGF. The enriched GO terms mainly were associated with the nucleoplasm, response to DNA damage stimuli and DNA repair. PPI network analysis indicated that GMNN and TSPO were significant hub proteins and steroid biosynthesis and synthesis and degradation of ketone bodies were significantly dysregulated pathways. Conclusion: It is concluded that the genes and pathways identified in our work may play critical roles in OSCC development. Our data provides a comprehensive perspective to understand mechanisms underlying OSCC and the significant genes (proteins) and pathways may be targets for therapy in the future.

• BMB Reports
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• 제55권9호
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• pp.417-428
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• 2022

Herbal medicine, a multi-component treatment, has been extensively practiced for treating various symptoms and diseases. However, its molecular mechanism of action on the human body is unknown, which impedes the development and application of herbal medicine. To address this, recent studies are increasingly adopting systems pharmacology, which interprets pharmacological effects of drugs from consequences of the interaction networks that drugs might have. Most conventional network-based approaches collect associations of herb-compound, compound-target, and target-disease from individual databases, respectively, and construct an integrated network of herb-compound-target-disease to study the complex mechanisms underlying herbal treatment. More recently, rapid advances in high-throughput omics technology have led numerous studies to exploring gene expression profiles induced by herbal treatments to elicit information on direct associations between herbs and genes at the genome-wide scale. In this review, we summarize key databases and computational methods utilized in systems pharmacology for studying herbal medicine. We also highlight recent studies that identify modes of action or novel indications of herbal medicine by harnessing drug-induced transcriptome data.

• Journal of Plant Biotechnology
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• 제37권2호
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• pp.144-152
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• 2010

As the International Rice Genome Sequencing Project (IRGSP) was completed in 2005 and opened to the public, many countries are making a lot of investments in researches on the utilization of sequence information along with system development. Also, the necessity of the functional genomics researches using microarray is increased currently to secure unique genes related with major agricultural traits and analyze metabolic pathways. Microrarray enables efficient analysis of large scale gene expression and related transcription regulation. This review aims to introduce available microarrays made based on rice genome information and current status of gene expression analysis using these microarrays integrated with the databases available to the public. Also, we introduce the researches on the large scale functional analysis of genes related with useful traits and genetic networks. Understanding of the mechanism related with mutual interaction between proteins with co-expression among rice genes can be utilized in the researches for improving major agricultural traits. The direct and indirect interactions of various genes would provide new functionality of rice. The recent results of the various expression profiling analysis in rice will promote functional genomic researches in plants including rice and provide the scientists involved in applications researches with wide variety of expression informations.

• Asian-Australasian Journal of Animal Sciences
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• 제32권4호
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• pp.508-518
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• 2019

Objective: This research aimed to determine biological pathways and protein-protein interaction (PPI) networks for 305-d milk yield (MY), 305-d fat yield (FY), and age at first calving (AFC) in the Thai multibreed dairy population. Methods: Genotypic information contained 75,776 imputed and actual single nucleotide polymorphisms (SNP) from 2,661 animals. Single-step genomic best linear unbiased predictions were utilized to estimate SNP genetic variances for MY, FY, and AFC. Fixed effects included herd-year-season, breed regression and heterosis regression effects. Random effects were animal additive genetic and residual. Individual SNP explaining at least 0.001% of the genetic variance for each trait were used to identify nearby genes in the National Center for Biotechnology Information database. Pathway enrichment analysis was performed. The PPI of genes were identified and visualized of the PPI network. Results: Identified genes were involved in 16 enriched pathways related to MY, FY, and AFC. Most genes had two or more connections with other genes in the PPI network. Genes associated with MY, FY, and AFC based on the biological pathways and PPI were primarily involved in cellular processes. The percent of the genetic variance explained by genes in enriched pathways (303) was 2.63% for MY, 2.59% for FY, and 2.49% for AFC. Genes in the PPI network (265) explained 2.28% of the genetic variance for MY, 2.26% for FY, and 2.12% for AFC. Conclusion: These sets of SNP associated with genes in the set enriched pathways and the PPI network could be used as genomic selection targets in the Thai multibreed dairy population. This study should be continued both in this and other populations subject to a variety of environmental conditions because predicted SNP values will likely differ across populations subject to different environmental conditions and changes over time.

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2005년도 BIOINFO 2005
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• pp.267-271
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• 2005

The goal of data mining is to extract new and useful knowledge from large scale datasets. As the amount of available data grows explosively, it became vitally important to develop faster data mining algorithms for various types of data. Recently, an interest in developing data mining algorithms that operate on graphs has been increased. Especially, mining frequent patterns from structured data such as graphs has been concerned by many research groups. A graph is a highly adaptable representation scheme that used in many domains including chemistry, bioinformatics and physics. For example, the chemical structure of a given substance can be modelled by an undirected labelled graph in which each node corresponds to an atom and each edge corresponds to a chemical bond between atoms. Internet can also be modelled as a directed graph in which each node corresponds to an web site and each edge corresponds to a hypertext link between web sites. Notably in bioinformatics area, various kinds of newly discovered data such as gene regulation networks or protein interaction networks could be modelled as graphs. There have been a number of attempts to find useful knowledge from these graph structured data. One of the most powerful analysis tool for graph structured data is frequent subgraph analysis. Recurring patterns in graph data can provide incomparable insights into that graph data. However, to find recurring subgraphs is extremely expensive in computational side. At the core of the problem, there are two computationally challenging problems. 1) Subgraph isomorphism and 2) Enumeration of subgraphs. Problems related to the former are subgraph isomorphism problem (Is graph A contains graph B?) and graph isomorphism problem(Are two graphs A and B the same or not?). Even these simplified versions of the subgraph mining problem are known to be NP-complete or Polymorphism-complete and no polynomial time algorithm has been existed so far. The later is also a difficult problem. We should generate all of 2$^n$ subgraphs if there is no constraint where n is the number of vertices of the input graph. In order to find frequent subgraphs from larger graph database, it is essential to give appropriate constraint to the subgraphs to find. Most of the current approaches are focus on the frequencies of a subgraph: the higher the frequency of a graph is, the more attentions should be given to that graph. Recently, several algorithms which use level by level approaches to find frequent subgraphs have been developed. Some of the recently emerging applications suggest that other constraints such as connectivity also could be useful in mining subgraphs : more strongly connected parts of a graph are more informative. If we restrict the set of subgraphs to mine to more strongly connected parts, its computational complexity could be decreased significantly. In this paper, we present an efficient algorithm to mine frequent subgraphs that are more strongly connected. Experimental study shows that the algorithm is scaling to larger graphs which have more than ten thousand vertices.