• Journal of Life Science
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• v.20 no.4
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• pp.467-473
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• 2010

Gangliosides are a major component of the plasma membrane of mammalian cells, which are directly involved in a variety of immunological events, including cell-to cell or cell-to-protein interactions. In this study, we investigated whether gangliosides, sialic acid-containing glycosphingolipids, are related to rejection during the xenotransplantation of NIH-miniature pig livers and hearts to humans. Both high performance thin-layer chromatography and immunohistochemistry analyses revealed that the expression of gangliosides in the liver tissue of NIH-miniature pigs was higher than that in the heart. Gangliosides GD3, GD1a, GD1b, GT1b and GQ1b were observed in both the liver and heart, whereas GQ1b was detected only in the liver, indicating that the ganglioside expression profiles are tissue specific. Moreover, other ganglio-series gangliosides, including GM3, were not detected in the livers and hearts of NIH-miniature pigs. Taken together, these results suggest that gangliosides may play important roles in immune responses in clinical xenotransplants of pig livers and hearts.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.3
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• pp.157-165
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• 2006

The change of external/internal factors of the cell rquires specific biological functions to maintain life. Such functions encourage particular genes to jnteract/regulate each other in multiple ways. Accordingly, we applied a linear decomposition model IFSA, which derives hidden variables, called the 'expression mode' that corresponds to the functions. To interpret gene interaction/regulation, we used a cross-correlation method given an expression mode. Linear decomposition models such as principal component analysis (PCA) and independent component analysis (ICA) were shown to be useful in analyzing high dimensional DNA microarray data, compared to clustering methods. These methods assume that gene expression is controlled by a linear combination of uncorrelated/indepdendent latent variables. However these methods have some difficulty in grouping similar patterns which are slightly time-delayed or asymmetric since only exactly matched Patterns are considered. In order to overcome this, we employ the (IFSA) method of [1] to locate phase- and shut-invariant features. Membership scoring functions play an important role to classify genes since linear decomposition models basically aim at data reduction not but at grouping data. We address a new function essential to the IFSA method. In this paper we stress that IFSA is useful in grouping functionally-related genes in the presence of time-shift and expression phase variance. Ultimately, we propose a new approach to investigate the multiple interaction information of genes.

• Korean Journal of Poultry Science
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• v.41 no.4
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• pp.287-296
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• 2014

Chicken eggs undergo various physiological changes during egg maturation. To study genes associated with the egg maturation in pre-ovulation (immature) and post-ovulation (mature), we compared gene expression patterns between in the immature egg and mature egg using RNA sequencing data. Mature and immature eggs were obtained from a Heuksaek Jaerae-jong of Korean native chicken. Total RNAs obtained from the eggs were sequenced by Illumina HiSeq 2000 platform, and the generated sequence reads were mapped to Galgal4 reference sequence assembly using Tuxedo Protocol. From the comparison of the RNA sequencing data, 315 genes were differentially expressed between mature and immature eggs, and 46 genes were only detected in immature egg. Further gene ontology (GO) analysis was performed for the differentially expressed genes using DAVID, showing that 29 and 28 GO terms were independently clustered from mature and immature, respectively. From those clustered GO terms, genes related to germ cell development, sex differentiation and defense response to bacterium were mainly expressed in the immature egg, while genes related to regulation of apoptosis, steroid metabolic process and lipid homeostasis were mainly detected in the mature egg. Our results could contribute to understand egg maturation before and after ovulation, and develop genetic markers for improving egg quality and productivity.