• Journal of Genetic Medicine
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• v.20 no.1
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• pp.6-14
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• 2023

Fabry disease (FD), a rare X-linked lysosomal storage disorder, is caused by mutations in the α-galactosidase A gene gene encoding α-galactosidase A (α-Gal A). The functional deficiency of α-Gal A results in progressive accumulation of neutral glycosphingolipids, causing multi-organ damages including cardiac, renal, cerebrovascular systems. The current treatment is comprised of enzyme replacement therapy (ERT), oral pharmacological chaperone therapy and adjunctive supportive therapy. ERT has been introduced 20 years ago, changing the outcome of FD patients with proven effectiveness. However, FD patients have many unmet needs. ERT needs a life-long intravenous therapy, inefficient bio-distribution, and generation of anti-drug antibodies. Migalastat, a pharmacological chaperone, augmenting α-Gal A enzyme activity only in patients with mutations amenable to the therapy, is now available for clinical practice. Furthermore, these therapies should be initiated before the organ damage becomes irreversible. Development of novel drugs aim at improving the clinical effectiveness and convenience of therapy. Clinical trial of next generation ERT is underway. Polyethylene glycolylated enzyme has a longer half-life and potentially reduced antigenicity, compared with standard preparations with longer dosing interval. Moss-derived enzyme has a higher affinity for mannose receptors, and seems to have more efficient access to podocytes of kidney which is relatively resistant to reach by conventional ERT. Substrate reduction therapy is currently under clinical trial. Gene therapy has now been started in several clinical trials using in vivo and ex vivo technologies. Early results are emerging. Other strategic approaches at preclinical research level are stem cell-based therapy with genome editing and systemic mRNA therapy.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.187-187
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• 2022

Purple-discoloration of the uppermost leaves has been observed in some soybean cultivars in recent years. The purpose of this study was to characterize the novel phenotypic changes between the uppermost and middle leaves via multiple approaches. First, quantitative trait loci mapping was conducted to detect loci associated with the novel phenotype using 85 recombinant inbred lines (RILs) of the 'Daepung' × PI 96983 population. 180K SNP data, a major quantitative trait locus (QTL) was identified at around 60 cM of chromosome 6, which accounts for 56% of total phenotypic variance. The genomic interval is about ~700kb, and a list of annotated genes includes the T-gene which is known to control pubescence and seed coat color and is presumed to encode flavonoid 35-hydroxylase (F3'H). Based on Hyperspectral imaging, the reflectance at 528-554 nm wavelength band was extremely reduced in the uppermost leaves compared to the middle (green leaves), which is presumed die to the accumulation of anthocyanins. In addition, purple-discolored leaf tissues were observed and compared to normal leaves using a transmission electronic microscope (TEM). Base on observations of the cell organelles, the purple-discolored uppermost leaves had many pigments formed in the epidermal cells unlike the normal middle leaves, and the cell wall thickness was twice as thick in the discolored leaves. The thickness of the thylakoid layer in the chloroplast the number of starch grains, the size of starch all decreased in the discolored leaves, while the number of plastoglobule and mitochondria increased.

• Journal of KIISE:Software and Applications
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• v.33 no.4
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• pp.365-377
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• 2006

Proteins are known to perform a biological function by interacting with other proteins or compounds. Since protein interaction is intrinsic to most cellular processes, prediction of protein interaction is an important issue in post-genomic biology where abundant interaction data have been produced by many research groups. In this paper, we present an associative feature mining method to predict implicit protein-protein interactions of Saccharomyces cerevisiae from public protein interaction data. We discretized continuous-valued features by maximal interdependence-based discretization approach. We also employed feature dimension reduction filter (FDRF) method which is based on the information theory to select optimal informative features, to boost prediction accuracy and overall mining speed, and to overcome the dimensionality problem of conventional data mining approaches. We used association rule discovery algorithm for associative feature and rule mining to predict protein interaction. Using the discovered associative feature we predicted implicit protein interactions which have not been observed in training data. According to the experimental results, the proposed method accomplished about 96.5% prediction accuracy with reduced computation time which is about 29.4% faster than conventional method with no feature filter in association rule mining.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2006.02a
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• pp.83-89
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• 2006

Cellular functions are carried out by a concerted action of biochemical pathways whose components have genetic interactions. Abnormalities in the activity of the genes that constitute or modulate these pathways frequently have oncogenic implications. Therefore, identifying the upstream regulatory genes for major biochemical pathways and defining their roles in carcinogenesis can have important consequences in establishing an effective target-oriented antitumor strategy We have analyzed the gene expression profiles of human liver cancer samples using cDNA microarray chips enriched in liver and/or stomach-expressed cDNA elements, and identified groups of genes that can tell tumors from non-tumors or normal liver, or classify tumors according to clinical parameters such as tumor grade, age, and inflammation grade. We also set up a high-throughput cell-based assay system (cell chip) that can monitor the activity of major biochemical pathways through a reporter assay. Then, we applied the cell chip platform for the analysis of the HCC-associated genes discovered from transcriptome profiling, and found a number of cancer marker genes having a potential of modulating the activity of cancer-related biochemical pathways such as E2F, TCF, p53, Stat, Smad, AP-1, c-Myc, HIF and NF-kB. Some of these marker genes were previously blown to modulate these pathways, while most of the others not. Upon a fast-track phenotype analysis, a subset of the genes showed increased colony forming abilities in soft agar and altered cell morphology or adherence characteristics in the presence of purified matrix proteins. We are currently analyzing these selected marker genes in more detail for their effects on various biological Processes and for Possible clinical roles in liver cancer development.

• The Plant Pathology Journal
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• v.36 no.6
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• pp.536-557
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• 2020

Sugarcane yellow leaf virus (SCYLV) is a distinct member of the Polerovirus genus of the Luteoviridae family. SCYLV is the major limitation to sugarcane production worldwide and presently occurring in most of the sugarcane growing countries. SCYLV having high genetic diversity within the species and presently ten genotypes are known to occur based on the complete genome sequence information. SCYLV is present in almost all the states of India where sugarcane is grown. Virion comprises of 180 coat protein units and are 24-29 nm in diameter. The genome of SCYLV is a monopartite and comprised of single-stranded (ss) positive-sense (+) linear RNA of about 6 kb in size. Virus genome consists of six open reading frames (ORFs) that are expressed by sub-genomic RNAs. The SCYLV is phloem-limited and transmitted by sugarcane aphid Melanaphis sacchari in a circulative and non-propagative manner. The other aphid species namely, Ceratovacuna lanigera, Rhopalosiphum rufiabdominalis, and R. maidis also been reported to transmit the virus. The virus is not transmitted mechanically, therefore, its transmission by M. sacchari has been studied in different countries. SCYLV has a limited natural host range and mainly infect sugarcane (Sachharum hybrid), grain sorghum (Sorghum bicolor), and Columbus grass (Sorghum almum). Recent insights in the protein-protein interactions of Polerovirus through protein interaction reporter (PIR) technology enable us to understand viral encoded proteins during virus replication, assembly, plant defence mechanism, short and long-distance travel of the virus. This review presents the recent understandings on virus biology, diagnosis, genetic diversity, virus-vector and host-virus interactions and conventional and next generation management approaches.

• KSBB Journal
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• v.24 no.4
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• pp.403-409
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• 2009

H2AX, a crucial component of chromatin, is implicated in DNA repair, cell cycle check point and tumor suppression. The aim of this study was to identify direct binding partners of H2AX to regulate cellular responses to above mechanisms. Literature reviews and bioinformatical tools were attempted intensively to find binding partners of H2AX, which resulted in identifying two potential proteins, breast cancer-1 (BRCA1) and BRCA1-associated RING domain 1 (BARD1). Although it has been reported in vivo that BRCA1 co-localizes with H2AX at the site of DNA damage, their biochemical mechanism for H2AX were however only known that the complex monoubiquitinates histone monomers, including unphosphorylated H2AX in vitro. Therefore, it is important to know whether the complex directly interacts with H2AX, and also which regions of these are specifically mediated for the interaction. Using in vitro GST pull-down assay, we present here that BRCA1 and BARD1 directly bind to H2AX. Moreover, through combinational approaches of domain analysis, fragment clonings and in vitro binding assay, we revealed molecular details of the BRCA1-H2AX and BARD1-H2AX complex. These data provide the potential evidence that each of the BRCA1 nuclear localization signal (NLS) and BARD1 BRCA1 C-terminal (BRCT) repeat domain is the novel mediator of H2AX recognition.

• Journal of Life Science
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• v.26 no.8
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• pp.904-910
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• 2016

Complex traits are determined by the combined effects of many loci and are affected by gene networks or biological pathways. Systems biology approaches have an important role in the identification of candidate genes related to complex diseases or traits at the system level. The gene network analysis has been performed by diverse types of methods such as gene co-expression, gene regulatory relationships, protein-protein interaction (PPI) and genetic networks. Moreover, the network-based methods were described for predicting gene functions such as graph theoretic method, neighborhood counting based methods and weighted function. However, there are a limited number of researches in livestock. The present study systemically analyzed genes associated with 102 types of economic traits based on the Animal Trait Ontology (ATO) and identified their relationships based on the gene co-expression network and PPI network in cattle. Then, we constructed the two types of gene network databases and network visualization system (http://www.nabc.go.kr/cg). We used a gene co-expression network analysis from the bovine expression value of bovine genes to generate gene co-expression network. PPI network was constructed from Human protein reference database based on the orthologous relationship between human and cattle. Finally, candidate genes and their network relationships were identified in each trait. They were typologically centered with large degree and betweenness centrality (BC) value in the gene network. The ontle program was applied to generate the database and to visualize the gene network results. This information would serve as valuable resources for exploiting genomic functions that influence economically and agriculturally important traits in cattle.