• Journal of Digestive Cancer Research
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• 제10권2호
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• pp.82-91
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• 2022

Gastrointestinal cancer accounts for one-third of the overall cancer occurrence worldwide. Pancreatic ductal adenocarcinoma (PDAC) is a type of gastrointestinal cancer that is known to be one of the most fatal among all cancer types, with a 5-year survival rate of less than 8%. Chemotherapy combined with surgical resection is its probable curative option. However, surgery is accessible for only 10-15% of patients diagnosed with PDAC. Organoids show self-organizing capacities and resemble the original tissue in terms of morphology and function. Organoids can also be cultured with high effectiveness from tumor tissues derived from each patient, making them an extremely fitting model for translational uses and improving personalized cancer medicine. Enhancing drug screening platforms is necessary to apply personalized medicinebased organoids in clinical settings.

• Mass Spectrometry Letters
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• 제15권2호
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• pp.69 -78
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• 2024

The advancement of high-throughput omics technologies and systems biology is essential for understanding complex biological mechanisms and diseases. The integration of proteomics and metabolomics provides comprehensive insights into cellular functions and disease pathology, driven by developments in mass spectrometry (MS) technologies, including electrospray ionization (ESI). These advancements are crucial for interpreting biological systems effectively. However, integrating these technologies poses challenges. Compared to genomic, proteomics and metabolomics have limitations in throughput, and data integration. This review examines developments in MS equipped electrospray ionization (ESI), and their importance in the effective interpretation of biological mechanisms. The review also discusses developments in sample preparation, such as Simultaneous Metabolite, Protein, Lipid Extraction (SIMPLEX), analytical techniques, and data analysis, highlighting the application of these technologies in the study of cancer or Huntington's disease, underscoring the potential for personalized medicine and diagnostic accuracy. Efforts by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and integrative data analysis methods such as O2PLS and OnPLS extract statistical similarities between metabolomic and proteomic data. System modeling techniques that mathematically explain and predict system responses are also covered. This practical application also shows significant improvements in cancer research, diagnostic accuracy and therapeutic targeting for diseases like pancreatic ductal adenocarcinoma, non-small cell lung cancer, and Huntington's disease. These approaches enable researchers to develop standardized protocols, and interoperable software and databases, expanding multi-omics research application in clinical practice.

• Journal of Microbiology and Biotechnology
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• 제25권8호
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• pp.1275-1280
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• 2015

Previously, we performed de novo RNA sequencing of Scolopendra subspinipes mutilans using high-throughput sequencing technology and identified several antimicrobial peptide candidates. Among them, a cationic antimicrobial peptide, scolopendrasin VII, was selected based on its physicochemical properties, such as length, charge, and isoelectric point. Here, we assessed the anticancer activities of scolopendrasin VII against U937 and Jurkat leukemia cell lines. The results showed that scolopendrasin VII decreased the viability of the leukemia cells in MTS assays. Furthermore, flow cytometric analysis and acridine orange/ethidium bromide staining revealed that scolopendrasin VII induced necrosis in the leukemia cells. Scolopendrasin VII-induced necrosis was mediated by specific interaction with phosphatidylserine, which is enriched in the membrane of cancer cells. Taken together, these data indicated that scolopendrasin VII induced necrotic cell death in leukemia cells, probably through interaction with phosphatidylserine. The results provide a useful anticancer peptide candidate and an efficient strategy for new anticancer peptide development.

• Molecules and Cells
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• 제40권11호
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• pp.814-822
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• 2017

Meiotic homologous recombination generates new combinations of preexisting genetic variation and is a crucial process in plant breeding. Within the last decade, our understanding of plant meiotic recombination and genome diversity has advanced considerably. Innovation in DNA sequencing technology has led to the exploration of high-resolution genetic and epigenetic information in plant genomes, which has helped to accelerate plant breeding practices via high-throughput genotyping, and linkage and association mapping. In addition, great advances toward understanding the genetic and epigenetic control mechanisms of meiotic recombination have enabled the expansion of breeding programs and the unlocking of genetic diversity that can be used for crop improvement. This review highlights the recent literature on plant meiotic recombination and discusses the translation of this knowledge to the manipulation of meiotic recombination frequency and location with regards to crop plant breeding.

• Journal of Applied Biological Chemistry
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• 제43권4호
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• pp.197-206
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• 2000

This paper summarizes recent developments in the field of molecular biology and its application to plant breeding, particularly in rice. Plant breeding in the past mostly depended on the time-consuming crossing of known genomes limited to certain traits. Plant breeding has now benefited from marker-assisted selection and genetic engineering to widen the gene pool, improve plant protection, and increase yield. Future plant breeding will expand based on functional and nutritional genomics, in which gene discovery and high-throughput transformation will accelerate crop design and benefits will accrue to human health, in the form of nutritional food for poor people to reduce malnutrition, or food enriched with antioxidants and with high food value for rich people. Agricultural biotechnology for food is no longer a dream but a reality that will dominate the 21st century for agriculture and human welfare.

• BMB Reports
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• 제52권10호
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• pp.589-594
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• 2019

Single-molecule techniques have been used successfully to visualize real-time enzymatic activities, revealing transient complex properties and heterogeneity of various biological events. Especially, conventional force spectroscopy including optical tweezers and magnetic tweezers has been widely used to monitor change in DNA length by enzymes with high spatiotemporal resolutions of ~nanometers and ~milliseconds. However, DNA metabolism results from coordination of a number of components during the processes, requiring efficient monitoring of a complex of proteins catalyzing DNA substrates. In this min-review, we will introduce a simple and multiplexed single-molecule assay to detect DNA substrates catalyzed by enzymes with high-throughput data collection. We conclude with a perspective of possible directions that enhance capability of the assay to reveal complex biological events with higher resolution.

• Molecules and Cells
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• 제44권12호
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• pp.883-892
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• 2021

Genome-wide chromosome conformation capture (3C)-based high-throughput sequencing (Hi-C) has enabled identification of genome-wide chromatin loops. Because the Hi-C map with restriction fragment resolution is intrinsically associated with sparsity and stochastic noise, Hi-C data are usually binned at particular intervals; however, the binning method has limited reliability, especially at high resolution. Here, we describe a new method called HiCORE, which provides simple pipelines and algorithms to overcome the limitations of single-layered binning and predict core chromatin regions with three-dimensional physical interactions. In this approach, multiple layers of binning with slightly shifted genome coverage are generated, and interacting bins at each layer are integrated to infer narrower regions of chromatin interactions. HiCORE predicts chromatin looping regions with higher resolution, both in human and Arabidopsis genomes, and contributes to the identification of the precise positions of potential genomic elements in an unbiased manner.

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2003년도 제2차 연례학술대회 발표논문집
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• pp.1-1
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• 2003

It is clear that computers will play a key role in the biology of the future. Even now, it is virtually impossible to keep track of the key proteins, their names and associated gene names, physical constants(e.g. binding constants, reaction constants, etc.), and hewn physical and genetic interactions without computational assistance. In this sense, computers act as an auxiliary brain, allowing one to keep track of thousands of complex molecules and their interactions. With the advent of gene expression array technology, many experiments are simply impossible without this computer assistance. In the future, as we seek to integrate the reductionist description of life provided by genomic sequencing into complex and sophisticated models of living systems, computers will play an increasingly important role in both analyzing data and generating experimentally testable hypotheses. The future of bioinformatics is thus being driven by potent technological and scientific forces. On the technological side, new experimental technologies such as microarrays, protein arrays, high-throughput expression and three-dimensional structure determination prove rapidly increasing amounts of detailed experimental information on a genomic scale. On the computational side, faster computers, ubiquitous computing systems, high-speed networks provide a powerful but rapidly changing environment of potentially immense power. The challenges we face are enormous: How do we create stable data resources when both the science and computational technology change rapidly? How do integrate and synthesize information from many disparate subdisciplines, each with their own vocabulary and viewpoint? How do we 'liberate' the scientific literature so that it can be incorporated into electronic resources? How do we take advantage of advances in computing and networking to build the international infrastructure needed to support a complete understanding of biological systems. The seeds to the solutions of these problems exist, at least partially, today. These solutions emphasize ubiquitous high-speed computation, database interoperation, federation, and integration, and the development of research networks that capture scientific knowledge rather than just the ABCs of genomic sequence. 1 will discuss a number of these solutions, with examples from existing resources, as well as area where solutions do not currently exist with a view to defining what bioinformatics and biology will look like in the future.

• Molecules and Cells
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• 제27권3호
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• pp.271-277
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• 2009

Proteins interact with each other within a cell, and those interactions give rise to the biological function and dynamical behavior of cellular systems. Generally, the protein interactions are temporal, spatial, or condition dependent in a specific cell, where only a small part of interactions usually take place under certain conditions. Recently, although a large amount of protein interaction data have been collected by high-throughput technologies, the interactions are recorded or summarized under various or different conditions and therefore cannot be directly used to identify signaling pathways or active networks, which are believed to work in specific cells under specific conditions. However, protein interactions activated under specific conditions may give hints to the biological process underlying corresponding phenotypes. In particular, responsive functional modules consist of protein interactions activated under specific conditions can provide insight into the mechanism underlying biological systems, e.g. protein interaction subnetworks found for certain diseases rather than normal conditions may help to discover potential biomarkers. From computational viewpoint, identifying responsive functional modules can be formulated as an optimization problem. Therefore, efficient computational methods for extracting responsive functional modules are strongly demanded due to the NP-hard nature of such a combinatorial problem. In this review, we first report recent advances in development of computational methods for extracting responsive functional modules or active pathways from protein interaction network and microarray data. Then from computational aspect, we discuss remaining obstacles and perspectives for this attractive and challenging topic in the area of systems biology.

• Asian Pacific Journal of Cancer Prevention
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• 제15권5호
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• pp.2345-2351
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• 2014

Lysyl oxidase-like 2 (LOXL2), a member of the lysyl oxidase (LOX) family, is a copper-dependent enzyme that catalyzes oxidative deamination of lysine residues on protein substrates. LOXL2 was found to be overexpressed in esophageal squamous cell carcinoma (ESCC) in our previous research. We later identified a LOXL2 splicing variant LOXL2-delta72 and we overexpressed LOXL2-delta72 and its wild type counterpart in ESCC cells following microarray analyses. First, the differentially expressed genes (DEGs) of LOXL2 and LOXL2-delta72 compared to empty plasmid were applied to generate protein-protein interaction (PPI) sub-networks. Comparison of these two sub-networks showed hundreds of different proteins. To reveal the potential specific roles of LOXL2- delta72 compared to its wild type, the DEGs of LOXL2-delta72 vs LOXL2 were also applied to construct a PPI sub-network which was annotated by Gene Ontology. The functional annotation map indicated the third PPI sub-network involved hundreds of GO terms, such as "cell cycle arrest", "G1/S transition of mitotic cell cycle", "interphase", "cell-matrix adhesion" and "cell-substrate adhesion", as well as significant "immunity" related terms, such as "innate immune response", "regulation of defense response" and "Toll signaling pathway". These results provide important clues for experimental identification of the specific biological roles and molecular mechanisms of LOXL2-delta72. This study also provided a work flow to test the different roles of a splicing variant with high-throughput data.