• Journal of Plant Biotechnology
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• v.45 no.3
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• pp.196-206
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• 2018

Dramatic increase in global population accompanied by rapid industrialization in developing countries has led to serious environmental, food, energy, and health problems. The Food and Agriculture Organization of the United Nations has estimated world population will increase to 9.7 billion by 2050 and require approximately 1.7 times more food, and more than 3.5 times energy than that of today. Particularly, sweetpotato is easy to cultivate in unfavorable conditions such as heat, drought, high salt, and marginal lands. In this respect, sweetpotato is an industrially valuable starch crop. To replace crops associated with these food and energy problems, it is necessary to develop new crops with improved nutrients and productivity, that can be grown on marginal lands, including desertification areas using plant biotechnology. For this purpose, exploring useful genes and developing genetically modified crops are essential strategies. Currently, sweetpotato [Ipomoea batatas (L.) Lam.] have been re-evaluated as the best health food and industrial crop that produces starch and low molecular weight antioxidants, such as vitamin A, vitamin E, anthocyanins and carotenoids. This review will focus on the current status of research on sweetpotato biotechnology on omics including genome sequencing, transcriptome, proteomics and molecular breeding. In addition, prospects on molecular breeding of sweetpotato on marginal lands for sustainable development were described.

• Molecules and Cells
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• v.37 no.6
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• pp.449-456
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• 2014

The use of synovial fluid-derived mesenchymal stem cells (SFMSCs) obtained from patients with degenerative arthropathy may serve as an alternative therapeutic strategy in osteoarthritis (OA) and rheumatoid arthritis (RA). For treatment of OA and RA patients, autologous transplantation of differentiated MSCs has several beneficial effects for cartilage regeneration including immunomodulatory activity. In this study, we induced chondrogenic differentiation of SFMSCs by inhibiting protein kinase A (PKA) with a small molecule and microRNA (miRNA). Chondrogenic differentiation was confirmed by PCR and immunocytochemistry using probes specific for aggrecan, the major cartilaginous proteoglycan gene. Absorbance of alcian blue stain to detect chondrogenic differentiation was increased in H-89 and/or miRNA-23b-transfected cells. Furthermore, expression of matrix metalloproteinase (MMP)-9 and MMP-2 was decreased in treated1 cells. Therefore, differentiation of SFMSCs into chondrocytes through inhibition of PKA signaling may be a therapeutic option for OA or RA patients.

• Genomics & Informatics
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• v.4 no.1
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• pp.40-44
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• 2006

Toxicogenomics combines transcriptome, proteome and metabolome profiling with conventional toxicology to investigate the interaction between biological molecules and toxicant or environmental stress in disease caution. Toxicogenomics faces the problems of comparison and integration across different sources of data. Cause of unusual characteristics of toxicogenomic data, researcher should be assisted by data analysis and annotation for getting meaningful information. There are already existing repositories which claim to stand for toxicogenomics database. However, those just contain limited abilities for toxicogenomic research. For supporting toxicologist who comes up against toxicogenomic data flood, now we propose novel toxicogenomics knowledgebase system, XPERANTO-TOX. XPERANTO-TOX is an integrated system for toxicogenomic data management and analysis. It is composed of three distinct but closely connected parts. Firstly, Data Storage System is for reposit many kinds of '-omics' data and conventional toxicology data. Secondly, Data Analysis System consists of analytical modules for integrated toxicogenomics data. At last, Data Annotation System is for giving extensive insight of data to researcher.

• Food Science of Animal Resources
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• v.40 no.1
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• pp.1-10
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• 2020

Various processing methods have a great impact on the physiochemical and nutritional properties of meat that are of health concern. Hence, the postmortem processing of meat by different methods is likely to intensify the potential effects on protein oxidation. The influence of meat protein oxidation on the modulation of the systemic redox status and underlying mechanism is well known. However, the effects of processed meat proteins isolated from different sources on gut microbiota, oxidative stress biomarkers, and metabolomic markers associated with metabolic syndromes are of growing interest. The application of advanced methodological approaches based on OMICS, and mass spectrometric technologies has enabled to better understand the molecular basis of the effect of processed meat oxidation on human health and the aging process. Animal studies indicate the involvement of dietary proteins isolated from different sources on health disorders, which emphasizes the impact of processed meat protein on the richness of bacterial taxa such as (Mucispirillum, Oscillibacter), accompanied by increased expression of lipogenic genes. This review explores the most recent evidences on meat processing techniques, meat protein oxidation, underlying mechanisms, and their potential effects on nutritional value, gut microbiota composition and possible implications on human health.

• BMB Reports
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• v.50 no.12
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• pp.621-627
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• 2017

We previously reported the involvement of zinc-finger protein 143 (ZNF143) on cancer cell motility in colon cancer cells. Here, ZNF143 was further characterized in breast cancer. Immunohistochemistry was used to determine the expression of ZNF143 in normal tissues and in tissues from metastatic breast cancer at various stages. Notably, ZNF143 was selectively expressed in duct and gland epithelium of normal breast tissues, which decreased when the tissue became malignant. To determine the molecular mechanism how ZNF143 affects breast cancer progression, it was knocked down by infecting benign breast cancer cells with short-hairpin (sh) RNA-lentiviral particles against ZNF143 (MCF7 sh-ZNF143). MCF7 sh-ZNF143 cells showed different cell-cell contacts and actin filament (F-actin) structures when compared with MCF7 sh-Control cells. In migration and invasion assays, ZNF143 knockdown induced increased cellular motility in breast carcinoma cells. This was reduced by the recovery of ZNF143 expression. Taken together, these results suggest that ZNF143 expression contributes to breast cancer progression.

• Genomics & Informatics
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• v.17 no.4
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• pp.41.1-41.12
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• 2019

Survival analysis mainly deals with the time to event, including death, onset of disease, and bankruptcy. The common characteristic of survival analysis is that it contains "censored" data, in which the time to event cannot be completely observed, but instead represents the lower bound of the time to event. Only the occurrence of either time to event or censoring time is observed. Many traditional statistical methods have been effectively used for analyzing survival data with censored observations. However, with the development of high-throughput technologies for producing "omics" data, more advanced statistical methods, such as regularization, should be required to construct the predictive survival model with high-dimensional genomic data. Furthermore, machine learning approaches have been adapted for survival analysis, to fit nonlinear and complex interaction effects between predictors, and achieve more accurate prediction of individual survival probability. Presently, since most clinicians and medical researchers can easily assess statistical programs for analyzing survival data, a review article is helpful for understanding statistical methods used in survival analysis. We review traditional survival methods and regularization methods, with various penalty functions, for the analysis of high-dimensional genomics, and describe machine learning techniques that have been adapted to survival analysis.

• Proceedings of the Korean Society of Food Science and Nutrition Conference
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• 2004.11a
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• pp.17-23
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• 2004

Not all individuals respond identically, or at times in the same direction, to dietary interventions. These inconsistencies likely arise because of diet and genomic interactions (nutrigenomics effects). A host of factors may influence the response to bioactive food components including specific polymorphisms (nutrigenetic effect), DNA methylation patterns and other epigenomic factors (nutritional epigenomic effects), capacity to induce anuo. suppress specific mRNA expression and patterns (nutritional transcriptomics), the occurrence and activity of proteins (proteomic effects), and/or the dose and temporal changes in cellular small molecular weight compounds will not only provide clues about specificity in response to food components, but assist in the identification of surrogate tissues and biomarkers that can predict a response. While this 'discovery' phase is critical for defining mechanisms and targets, and thus those who will benefit most from intervention, its true usefulness depends on moving this understanding into 'development' (interventions for better prevention, detection, diagnosis, and treatment) and a 'delivery' phase where information is provided to those most in need. It is incumbent on those involved with food and nutrition to embrace the 'omics' that relate to nutrition when considering not only the nutritional value of foods and their food components, but also when addressing acceptability and safety. The future of 'Nutrigenomics and Health Promotion' depends on the ability of the scientific community to identity appropriate biomarkers and susceptibility variants, effective communications about the merits of such undertakings with the health care community and with consumers, and doing all of this within a responsible bioethical framework.

• Genomics & Informatics
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• v.8 no.4
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• pp.165-169
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• 2010

The Korean Bioinformation Center (KOBIC) is a national bioinformatics research center in Korea. We developed many bioinformatics algorithms and applications to facilitate the biological interpretation of OMICS data. Here we present an introduction to major bioinformatics resources of databases and tools developed at KOBIC. These resources are classified into three main fields: genome, proteome, and literature. In the genomic resources, we constructed several pipelines for next generation sequencing (NGS) data processing and developed analysis algorithms and web-based database servers including miRGator, ESTpass, and CleanEST. We also built integrated databases and servers for microarray expression data such as MDCDP. As for the proteome data, VnD database, WDAC, Localizome, and CHARMM_HM web servers are available for various purposes. We constructed IntoPub server and Patome database in the literature field. We continue constructing and maintaining the bioinformatics infrastructure and developing algorithms.

• Biotechnology and Bioprocess Engineering:BBE
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• v.10 no.5
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• pp.408-417
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• 2005

Increasing numbers of value added chemicals are being produced using microbial fermentation strategies. Computational modeling and simulation of microbial metabolism is rapidly becoming an enabling technology that is driving a new paradigm to accelerate the bioprocess development cycle. In particular, constraint-based modeling and the development of genome-scale models of industrial microbes are finding increasing utility across many phases of the bioprocess development workflow. Herein, we review and discuss the requirements and trends in the industrial application of this technology as we build toward integrated computational/experimental platforms for bioprocess engineering. Specifically we cover the following topics: (1) genome-scale models as genetically and biochemically consistent representations of metabolic networks; (2) the ability of these models to predict, assess, and interpret metabolic physiology and flux states of metabolism; (3) the model-guided integrative analysis of high throughput 'omics' data; (4) the reconciliation and analysis of on- and off-line fermentation data as well as flux tracing data; (5) model-aided strain design strategies and the integration of calculated biotransformation routes; and (6) control and optimization of the fermentation processes. Collectively, constraint-based modeling strategies are impacting the iterative characterization of metabolic flux states throughout the bioprocess development cycle, while also driving metabolic engineering strategies and fermentation optimization.

• BMB Reports
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• v.44 no.2
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• pp.107-112
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• 2011

We have developed a biologist-friendly, stand-alone Java GUI application, IdBean, for ID conversion. Our tool integrated most of the widely used ID conversion services that provide programmatic access. It is the first GUI ID conversion application that supports the direct merging as well as comparison of conversion results from multiple ID conversion services without manual effort. This tool will greatly help biologists who handle multiple ID types for the analyses of gene or gene product lists. By referring to multiple conversion services, the number of failed IDs can be reduced. By accessing ID conversion service online, it will potentially provide the most up-to-date conversion results. The application was developed in modular form; however, it can be re-packaged into plug-in form. For the development of a bioinformatics analysis tool, the module can be used as a built-in ID conversion component. It is available at http://neon.gachon.ac.kr/IdBean/.