• Journal of Life Science
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• v.33 no.1
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• pp.64-72
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• 2023

Despite several advances in identification of cardiac transcription factors, there are still needs to find new bioactive molecules that promote cardiomyogenesis from stem cells to highly efficient myocardial differentiation. We analyzed Illumina expression microarray data of mouse embryonic stem cells (mESCs)-derived cardiomyocytes. 276 genes were upregulated (≥ 4fold) in mESCs-derived cardiomyocytes compared undifferentiated ESCs. Secreted phosphoprotein 2 (Spp2) is one of candidates and is known to inhibit bone morphogenetic protein 2 (BMP2) signal transduction as a pseudoreceptor for BMP2. However, its function in cardiomyogenesis is unknown. We confirmed that Spp2 expression increased during the differentiation into functional cardiomyocytes using mESCs, TC-1/Kh2 and E14. Interestingly, Spp2 secretion transiently increased 3 days after formation of embryoid bodies (EBs), indicating that the extracellular secretion of Spp2 is involved in the differentiation of ESCs into cardiomyocytes. To characterize Spp2, we performed experiments using the C2C12 mouse myoblast cell line, which has the property of shifting the differentiation pathway from myoblastic to osteoblastic by treatment with BMP2. Similar to the differentiation of ESCs, transcription of Spp2 increased as C2C12 myoblasts differentiated into myotubes. In particular, Spp2 secretion increased dramatically in the early stage of differentiation. Furthermore, treatment with Spp2-Flag recombinant protein promoted the differentiation of C2C12 myoblasts into myotubes. Taken together, we suggest a novel bioactive protein Spp2 that differentiates ESCs into cardiomyocytes. This may be useful for understanding the molecular pathways of cardiomyogenesis and for experimental or clinical promotion of stem cell therapy for ischemic heart diseases.

• Korean Journal of Poultry Science
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• v.50 no.4
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• pp.193-202
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• 2023

Influenza IAVs are encapsulated negative-strand RNA viruses that infect many bird species' respiratory systems and can spread to other animals, including humans. This work reanalyzed previous microarray datasets to identify common and specific differentially expressed genes (DEGs) in chickens, as well as their biological activities. There were 760 and 405 DEGs detected in HPAIV and LPAIV-infected chicken cells, respectively. HPAIV and LPAIV have 670 and 315 DEGs, respectively, with both viruses sharing 90 DEGs. Because of HPAIV infection, numerous genes were implicated in a fundamental biological function of the cell cycle, according to the functional annotation of DEGs. Of the targeted genes, expressions of CDC Like Kinase 3 (CLK3), Nucleic Acid Binding Protein 1 (NABP1), Interferon-Inducible Protein 6 (IFI6), PIN2 (TERF1) Interacting Telomerase Inhibitor 1 (PINX1), and Cellular Communication Network Factor 4 (WISP1) were altered in DF-1 cells treated with polyinosinic:polycytidylic acid (PIC), a toll-like receptor 3 (TLR3) ligand, suggesting that transcription of these genes be controlled by TLR3 signaling. To gain a better understanding of the pathophysiology of AIVs in chickens, it is crucial to focus more research on unraveling the mechanisms through which AIV infections may manipulate host responses during the infection process. Insights into these mechanisms could facilitate the development of novel therapeutic strategies.

• 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.