• Microbiology and Biotechnology Letters
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• v.51 no.3
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• pp.317-324
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• 2023

Endometrium receptivity is a complex mechanism of intricate pathways that lead to the shift from the proliferative to the secretory phase. Our goal was to identify high-ranking differentially expressed genes and study the pathways associated with the phenomenon. Raw data were retrieved from six GEO datasets and 705 DEGs were identified through robust ranking aggregation after the integration of five datasets. 20 key genes were identified that were further re-validated in an additional dataset. Supporting evidence through the experimental references confirms them as major biomarkers of the shift from the proliferative to the secretory phase.

• BMB Reports
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• v.53 no.8
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• pp.393-399
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• 2020

Recent advancements in the resolution and throughput of single-cell analyses, including single-cell RNA sequencing (scRNA-seq), have achieved significant progress in biomedical research in the last decade. These techniques have been used to understand cellular heterogeneity by identifying many rare and novel cell types and characterizing subpopulations of cells that make up organs and tissues. Analysis across various datasets can elucidate temporal patterning in gene expression and developmental cues and is also employed to examine the response of cells to acute injury, damage, or disruption. Specifically, scRNA-seq and spatially resolved transcriptomics have been used to describe the identity of novel or rare cell subpopulations and transcriptional variations that are related to normal and pathological conditions in mammalian models and human tissues. These applications have critically contributed to advance basic cardiovascular research in the past decade by identifying novel cell types implicated in development and disease. In this review, we describe current scRNA-seq technologies and how current scRNA-seq and spatial transcriptomic (ST) techniques have advanced our understanding of cardiovascular development and disease.

• Food Science and Industry
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• v.52 no.3
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• pp.220-228
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• 2019

Rapid development of next-generation sequencing (NGS) technology is available to study microbes in genomic level. This NGS has been widely used in DNA/RNA sequencing for genome sequencing, metagenomics, and transcriptomics. The food microbiology area could be categorized into three groups. Food microbes including probiotics and food-borne pathogens are studied in genomic level using NGS for microbial genomics. While food fermentation or food spoilage are more complicated, their genomic study needs to be done with metagenomics using NGS for compositional analysis. Furthermore, because microbial response in food environments are also important to understand their roles in food fermentation or spoilage, pattern analysis of RNA expression in the specific food microbe is conducted using RNA-Seq. These microbial genomics, metagenomics, and transcriptomics for food fermentation and spoilage would extend our knowledge on effective utilization of fermenting bacteria for health promotion as well as efficient control of food-borne pathogens for food safety.

• BMB Reports
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• v.55 no.3
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• pp.113-124
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• 2022

Single-cell RNA sequencing (scRNA-seq) has greatly advanced our understanding of cellular heterogeneity by profiling individual cell transcriptomes. However, cell dissociation from the tissue structure causes a loss of spatial information, which hinders the identification of intercellular communication networks and global transcriptional patterns present in the tissue architecture. To overcome this limitation, novel transcriptomic platforms that preserve spatial information have been actively developed. Significant achievements in imaging technologies have enabled in situ targeted transcriptomic profiling in single cells at single-molecule resolution. In addition, technologies based on mRNA capture followed by sequencing have made possible profiling of the genome-wide transcriptome at the 55-100 ㎛ resolution. Unfortunately, neither imaging-based technology nor capture-based method elucidates a complete picture of the spatial transcriptome in a tissue. Therefore, addressing specific biological questions requires balancing experimental throughput and spatial resolution, mandating the efforts to develop computational algorithms that are pivotal to circumvent technology-specific limitations. In this review, we focus on the current state-of-the-art spatially resolved transcriptomic technologies, describe their applications in a variety of biological domains, and explore recent discoveries demonstrating their enormous potential in biomedical research. We further highlight novel integrative computational methodologies with other data modalities that provide a framework to derive biological insight into heterogeneous and complex tissue organization.

• Journal of Digital Contents Society
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• v.11 no.1
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• pp.99-104
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• 2010

Numerous bio-digital contents have been produced by new technology using biochip and others for analyzing early chemical-induced genes. These contents have little meaning by themselves, and so they should be modified and extracted after consideration of biological meaning. These include genomics, transcriptomics, protenomics, metabolomics, which combined into omics. Omics tools could be applied into toxicology, forming a new field of toxicogenomics. It is possible that approach of toxicogenomics can estimate toxicity more quickly and accurately by analyzing gene/protein/metabolite profiles. These approaches should help not only to discover highly sensitive and predictive biomarkers but also to understand molecular mechanism(s) of toxicity, based on the development of analysing technology. Furthermore, it is important that bio-digital contents should be obtained from specific cells having biological events more than from whole cells. Taken together, many bio-digital contents should be analyzed by careful calculating algorism under well-designed experimental protocols, network analysis using computational algorism and related profound databases.

• Biomolecules & Therapeutics
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• v.27 no.2
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• pp.134-144
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• 2019

The prevalence of nonalcoholic fatty liver disease (NAFLD) has increased with the incidence of obesity; however, the underlying mechanisms are unknown. In this study, high-resolution metabolomics (HRM) along with transcriptomics were applied on animal models to draw a mechanistic insight of NAFLD. Wild type (WT) and catalase knockout (CKO) mice were fed with normal fat diet (NFD) or high fat diet (HFD) to identify the changes in metabolic and transcriptomic profiles caused by catalase gene deletion in correspondence with HFD. Integrated omics analysis revealed that cholic acid and $3{\beta}$, $7{\alpha}$-dihydroxy-5-cholestenoate along with cyp7b1 gene involved in primary bile acid biosynthesis were strongly affected by HFD. The analysis also showed that CKO significantly changed all-trans-5,6-epoxy-retinoic acid or all-trans-4-hydroxy-retinoic acid and all-trans-4-oxo-retinoic acid along with cyp3a41b gene in retinol metabolism, and ${\alpha}/{\gamma}$-linolenic acid, eicosapentaenoic acid and thromboxane A2 along with ptgs1 and tbxas1 genes in linolenic acid metabolism. Our results suggest that dysregulated primary bile acid biosynthesis may contribute to liver steatohepatitis, while up-regulated retinol metabolism and linolenic acid metabolism may have contributed to oxidative stress and inflammatory phenomena in our NAFLD model created using CKO mice fed with HFD.

• Journal of Plant Biotechnology
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• v.42 no.4
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• pp.312-325
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• 2015

In this review, we summarized the trends of genomics and transcriptomics research on peach, a model species of Rosaceae. Peach genome maps have been developed from various progeny groups with many next-generation sequencing (NGS) based single nucleotide polymorphism markers. Molecular markers of qualitative traits and quantitative trait loci (QTL) such as fruit characteristics, blooming date, and disease resistance have been analyzed. Among many characteristics, markers related to flesh softening and flesh adhesion are useful for marker assisted selection. Through comparative genomics, peach genome has been compared to the genome of Arabidopsis, Populus, Malus, and Fragaria species. Through transcriptomics and proteomics, fruit growth and development, and flavonoid synthesis, postharvest related transcriptomes and disease resistance related proteins have been reported. Recently, development of NGS based markers, construction of core collection of germplasm, and genotyping of various progenies have been preceded. In the near future, accurate QTL analysis and identification of useful genes are expected to establish a foundation for effective molecular breeding.

• IMMUNE NETWORK
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• v.14 no.2
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• pp.73-80
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• 2014

Because autoimmune diseases (AIDs) result from a complex combination of genetic and epigenetic factors, as well as an altered immune response to endogenous or exogenous antigens, systems biology approaches have been widely applied. The use of multi-omics approaches, including blood transcriptomics, genomics, epigenetics, proteomics, and metabolomics, not only allow for the discovery of a number of biomarkers but also will provide new directions for further translational AIDs applications. Systems biology approaches rely on high-throughput techniques with data analysis platforms that leverage the assessment of genes, proteins, metabolites, and network analysis of complex biologic or pathways implicated in specific AID conditions. To facilitate the discovery of validated and qualified biomarkers, better-coordinated multi-omics approaches and standardized translational research, in combination with the skills of biologists, clinicians, engineers, and bioinformaticians, are required.

• Journal of Plant Biotechnology
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• v.33 no.3
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• pp.209-222
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• 2006

The whole genome sequence was completed in arabidopsis and rice. Large amounts of EST data have been available from many other plants. Also, vast quantities of diverse biological data have been generated by various '-omics' technologies such as transcriptomics, proteomics, and metabolomics. Bioinformatics plays an essential role in extracting useful information from these tremendous amounts of biological data. In this review we introduced experimental methods to generate massive data, applications to plant science such as plant disease resistance and molecular breeding and bioinformatics tools and web sites available in plant biotechnology R&D. We concluded that new experimental methods and bioinfomation analysis techniques have made major contributions to the development of plant biotechnology and that bioinformatics has become a critical factor in plant biotechnology R&D.

• YAKHAK HOEJI
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• v.52 no.5
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• pp.345-354
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• 2008

This study was designed to investigate the anti-diabetic effect and mechanism of Korean red ginseng extract through transcriptomics in C57BL/KsJ db/db mice. The db/db mice were randomly divided into six groups: diabetic control group (DC), red ginseng extract low dose group (RGL, 100 mg/kg), red ginseng extract high dose group (RGH, 200 mg/kg), metformin group (MET, 300 mg/kg), glipizide group (GPZ, 15 mg/kg) and pioglitazone group (PIO, 30 mg/kg), and treated with drugs once per day for 10 weeks. At the end of treatment, we measured blood glucose, insulin, hemoglobin A1c (HbA1c), triglyceride (TG), adiponectin, leptin, non-esterified fatty acid (NEFA). RGL-treated group lowered the blood glucose and HbA1c levels by 19.6% and 11.4% compared to those in diabetic control group. In addition, plasma adiponectin and leptin levels in RGL-treated groups were increased by 20% and 12%, respectively, compared to those in diabetic control. Morphological analyses of liver, pancreas and epidydimal adipose tissue were done by hematoxylin-eosin staining, and pancreatic islet insulin and glucagon levels were detected by double-immunofluorescence staining. RGL-treated group revealed higher insulin contents and lower glucagon contents compared to diabetic control. To elucidate an action mechanism of Korean red ginseng, DNA microarray analyses were performed in liver and fat tissues, and western blot and RT-PCR were conducted in liver for validation. According to hierarchical clustering and principal component analysis of gene expression Korean red ginseng treated groups were close to metformin treated group. In summary, Korean red ginseng lowered the blood glucose level through protecting destruction of islet cells and shifting glucose metabolism from hepatic glucose production to glucose utilization and improving insulin sensitivity through enhancing plasma adiponectin and leptin levels.