• Journal of Ginseng Research
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• v.48 no.1
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• pp.103-111
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• 2024

Background: Ginseng (Panax ginseng Mayer) is an important natural medicine. However, a long culture period and challenging quality control requirements limit its further use. Although artificial cultivation can yield a sustainable medicinal supply, research on the association between the transplantation and chaining of metabolic networks, especially the regulation of ginsenoside biosynthetic pathways, is limited. Methods: Herein, we performed Liquid chromatography tandem mass spectrometry based metabolomic measurements to evaluate ginsenoside accumulation and categorise differentially abundant metabolites (DAMs). Transcriptome measurements using an Illumina Platform were then conducted to probe the landscape of genetic alterations in ginseng at various ages in transplantation mode. Using pathway data and crosstalk DAMs obtained by MapMan, we constructed a metabolic profile of transplantation Ginseng. Results: Accumulation of active ingredients was not obvious during the first 4 years (in the field), but following transplantation, the ginsenoside content increased significantly from 6-8 years (in the wild). Glycerolipid metabolism and Glycerophospholipid metabolism were the most significant metabolic pathways, as Lipids and lipid-like molecule affected the yield of ginsenosides. Starch and sucrose were the most active metabolic pathways during transplantation Ginseng growth. Conclusion: This study expands our understanding of metabolic network features and the accumulation of specific compounds during different growth stages of this perennial herbaceous plant when growing in transplantation mode. The findings provide a basis for selecting the optimal transplanting time.

• Genomics & Informatics
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• v.2 no.4
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• pp.191-194
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• 2004

Exponentially increasing biopathway data in recent years provide us with means to elucidate the large-scale modular organization of the cell. Given the existing information on metabolic and regulatory networks, inferring biopathway information through scientific reasoning or data mining of large scale array data or proteomics data get great attention. Naturally, there is a need for a user-friendly system allowing the user to combine large and diverse pathway data sets from different resources. We built a data warehouse - BIOWAY - for analyzing and visualizing biological pathways, by integrating and customizing resources. We have collected many different types of data in regards to pathway information, including metabolic pathway data from KEGG/LIGAND, signaling pathway data from BIND, and protein information data from SWISS-PROT. In addition to providing general data retrieval mechanism, a successful user interface should provide convenient visualization mechanism since biological pathway data is difficult to conceptualize without graphical representations. Still, the visual interface in the previous systems, at best, uses static images only for the specific categorized pathways. Thus, it is difficult to cope with more complex pathways. In the BIOWAY system, all the pathway data can be displayed in computer generated graphical networks, rather than manually drawn image data. Furthermore, it is designed in such a way that all the pathway maps can be expanded or shrinked, by introducing the concept of super node. A subtle graphic layout algorithm has been applied to best display the pathway data.

• Genomics & Informatics
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• v.6 no.4
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• pp.210-222
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• 2008

Due to the polygenic nature of cancer, it is believed that breast cancer is caused by the perturbation of multiple genes and their complex interactions, which contribute to the wide aspects of disease phenotypes. A systems biology approach for the identification of subnetworks of interconnected genes as functional modules is required to understand the complex nature of diseases such as breast cancer. In this study, we apply a 3-step strategy for the interpretation of microarray data, focusing on identifying significantly perturbed metabolic pathways rather than analyzing a large amount of overexpressed and underexpressed individual genes. The selected pathways are considered to be dysregulated functional modules that putatively contribute to the progression of disease. The subnetwork of protein-protein interactions for these dysregulated pathways are constructed for further detailed analysis. We evaluated the method by analyzing microarray datasets of breast cancer tissues; i.e., normal and invasive breast cancer tissues. Using the strategy of microarray analysis, we selected several significantly perturbed pathways that are implicated in the regulation of progression of breast cancers, including the extracellular matrix-receptor interaction pathway and the focal adhesion pathway. Moreover, these selected pathways include several known breast cancer-related genes. It is concluded from this study that the present strategy is capable of selecting interesting perturbed pathways that putatively play a role in the progression of breast cancer and provides an improved interpretability of networks of protein-protein interactions.

• Journal of Microbiology and Biotechnology
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• v.15 no.1
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• pp.161-174
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• 2005

Abstract Glycolysis has a main function to provide ATP and precursor metabolites for biomass production. Although glycolysis is one of the most important pathways in cellular metabolism, the details of its regulation mechanism and regulating chemicals are not well known yet. The regulation of the glycolytic pathway is very robust to allow for large fluxes at almost constant metabolite levels in spite of changing environmental conditions and many reaction effectors like inhibitors, activating compounds, cofactors, and related metal ions. These changing environmental conditions and metabolic reaction effectors were focused on to understand their roles in the metabolic networks. In this study, we have investigated for construction of the regulatory map of the glycolytic metabolic network and tried to collect all the effectors as much as possible which might affect the glycolysis metabolic pathway. Using the results of this study, it is expected that a complex metabolic situation can be more precisely analyzed and simulated by using available programs and appropriate kinetic data.

• Genomics & Informatics
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• v.1 no.1
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• pp.1-6
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• 2003

In conclusion, the seemingly fuzzy and disorganized data of biology with thousands of different layers ranging from molecule to the Internet have refused so far to be mapped precisely and predicted successfully by mathematicians, physicists or computer scientists. Genomics and bioinformatics are the fields that process such complex data. The insights on the nature of biological entities as complex interaction networks are opening a door toward a generalization of the representation of biological entities. The main challenge of genomics and bioinformatics now lies in 1) how to data mine the networks of the domains of bioinformatics, namely, the literature, metabolic pathways, and proteome and structures, in terms of interaction; and 2) how to generalize the networks in order to integrate the information into computable genomic data for computers regardless of the levels of layer. Once bioinformatists succeed to find a general principle on the way components interact each other to form any organic interaction network at genomic scale, true simulation and prediction of life in silico will be possible.

• Proceedings of the Korean Society for Applied Microbiology Conference
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• 2004.06a
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• pp.60-61
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• 2004

Metabolic engineering is now a well established discipline, used extensively to determine and execute rational strategies of strain development to improve the performance of micro-organisms employed in industrial fermentations. The basic principle of this approach is that performance of the microbial catalyst should be adequately characterised metabolically so as to clearlyidentify the metabolic network constraints, thereby identifying the most probable targets for genetic engineering and the extent to which improvements can be realistically achieved. In order to harness correctly this potential, it is clear that the physiological analysis of each strain studied needs to be undertaken under conditions as close as possible to the physico-chemical environment in which the strain evolves within the full-scale process. Furthermore, this analysis needs to be undertaken throughoutthe entire fermentation so as to take into account the changing environment in an essentially dynamic situation in which metabolic stress is accentuated by the microbial activity itself, leading to increasingly important stress response at a metabolic level. All too often these industrial fermentation constraints are overlooked, leading to identification of targets whose validity within the industrial context is at best limited. Thus the conceptual error is linked to experimental design rather than inadequate methodology. New tools are becoming available which open up new possibilities in metabolic engineering and the characterisation of complex metabolic networks. Traditionally metabolic analysis was targeted towards pre-identified genes and their corresponding enzymatic activities within pre-selected metabolic pathways. Those pathways not included at the onset were intrinsically removed from the network giving a fundamentally localised vision of pathway functionality. New tools from genome research extend this reductive approach so as to include the global characteristics of a given biological model which can now be seen as an integrated functional unit rather than a specific sub-group of biochemical reactions, thereby facilitating the resolution of complexnetworks whose exact composition cannot be estimated at the onset. This global overview of whole cell physiology enables new targets to be identified which would classically not have been suspected previously. Of course, as with all powerful analytical tools, post-genomic technology must be used carefully so as to avoid expensive errors. This is not always the case and the data obtained need to be examined carefully to avoid embarking on the study of artefacts due to poor understanding of cell biology. These basic developments and the underlying concepts will be illustrated with examples from the author's laboratory concerning the industrial production of commodity chemicals using a number of industrially important bacteria. The different levels of possibleinvestigation and the extent to which the data can be extrapolated will be highlighted together with the extent to which realistic yield targets can be attained. Genetic engineering strategies and the performance of the resulting strains will be examined within the context of the prevailing experimental conditions encountered in the industrial fermentor. Examples used will include the production of amino acids, vitamins and polysaccharides. In each case metabolic constraints can be identified and the extent to which performance can be enhanced predicted

• Journal of Veterinary Science
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• v.19 no.6
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• pp.798-807
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• 2018

Several studies have reported the effect of absorption of procyanidins and their contribution to the small intestine. However, differences between dietary interventions of procyanidins and interventions via antibiotic feeding in pigs are rarely reported. Following 16S rRNA gene Illumina MiSeq sequencing, we observed that both procyanidin administration for 2 months (procyanidin-1 group) and continuous antibiotic feeding for 1 month followed by procyanidin for 1 month (procyanidin-2 group) increased the number of operational taxonomic units, as well as the Chao 1 and ACE indices, compared to those in pigs undergoing antibiotic administration for 2 months (antibiotic group). The genera Fibrobacter and Spirochaete were more abundant in the antibiotic group than in the procyanidin-1 and procyanidin-2 groups. Principal component analysis revealed clear separations among the three groups. Additionally, using the online Molecular Ecological Network Analyses pipeline, three co-occurrence networks were constructed; Lactobacillus was in a co-occurrence relationship with Trichococcus and Desulfovibrio and a co-exclusion relationship with Bacillus and Spharerochaeta. Furthermore, metabolic function analysis by phylogenetic investigation of communities by reconstruction of unobserved states demonstrated modulation of pathways involved in the metabolism of carbohydrates, amino acids, energy, and nucleotides. These data suggest that procyanidin influences the gut microbiota and the intestinal metabolic function to produce beneficial effects on metabolic homeostasis.

• Genomics & Informatics
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• v.12 no.4
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• pp.187-194
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• 2014

Metabolic syndrome (METS) is a disorder of energy utilization and storage and increases the risk of developing cardiovascular disease and diabetes. To identify the genetic risk factors of METS, we carried out a genome-wide association study (GWAS) for 2,657 cases and 5,917 controls in Korean populations. As a result, we could identify 2 single nucleotide polymorphisms (SNPs) with genome-wide significance level p-values (< $5{\times}10^{-8}$), 8 SNPs with genome-wide suggestive p-values ($5{\times}10^{-8}{\leq}$ p < $1{\times}10^{-5}$), and 2 SNPs of more functional variants with borderline p-values ($5{\times}10^{-5}{\leq}$ p < $1{\times}10^{-4}$). On the other hand, the multiple correction criteria of conventional GWASs exclude false-positive loci, but simultaneously, they discard many true-positive loci. To reconsider the discarded true-positive loci, we attempted to include the functional variants (nonsynonymous SNPs [nsSNPs] and expression quantitative trait loci [eQTL]) among the top 5,000 SNPs based on the proportion of phenotypic variance explained by genotypic variance. In total, 159 eQTLs and 18 nsSNPs were presented in the top 5,000 SNPs. Although they should be replicated in other independent populations, 6 eQTLs and 2 nsSNP loci were located in the molecular pathways of LPL, APOA5, and CHRM2, which were the significant or suggestive loci in the METS GWAS. Conclusively, our approach using the conventional GWAS, reconsidering functional variants and pathway-based interpretation, suggests a useful method to understand the GWAS results of complex traits and can be expanded in other genomewide association studies.

• Molecules and Cells
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• v.39 no.8
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• pp.587-593
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• 2016

As sessile organisms, plants must be able to adapt to the environment. Plants respond to the environment by adjusting their growth and development, which is mediated by sophisticated signaling networks that integrate multiple environmental and endogenous signals. Recently, increasing evidence has shown that a bHLH transcription factor PIF4 plays a major role in the multiple signal integration for plant growth regulation. PIF4 is a positive regulator in cell elongation and its activity is regulated by various environmental signals, including light and temperature, and hormonal signals, including auxin, gibberellic acid and brassinosteroid, both transcriptionally and post-translationally. Moreover, recent studies have shown that the circadian clock and metabolic status regulate endogenous PIF4 level. The PIF4 transcription factor cooperatively regulates the target genes involved in cell elongation with hormone-regulated transcription factors. Therefore, PIF4 is a key integrator of multiple signaling pathways, which optimizes growth in the environment. This review will discuss our current understanding of the PIF4-mediated signaling networks that control plant growth.

• Journal of Microbiology and Biotechnology
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• v.33 no.10
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• pp.1257-1267
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• 2023

Although rational genetic engineering is nowadays the favored method for microbial strain improvement, building up mutant libraries based on directed evolution for improvement is still in many cases the better option. In this regard, the demand for precise and efficient screening methods for mutants with high performance has stimulated the development of biosensor-based high-throughput screening strategies. Genetically encoded biosensors provide powerful tools to couple the desired phenotype to a detectable signal, such as fluorescence and growth rate. Herein, we review recent advances in engineering several classes of biosensors and their applications in directed evolution. Furthermore, we compare and discuss the screening advantages and limitations of two-component biosensors, transcription-factor-based biosensors, and RNA-based biosensors. Engineering these biosensors has focused mainly on modifying the expression level or structure of the biosensor components to optimize the dynamic range, specificity, and detection range. Finally, the applications of biosensors in the evolution of proteins, metabolic pathways, and genome-scale metabolic networks are described. This review provides potential guidance in the design of biosensors and their applications in improving the bioproduction of microbial cell factories through directed evolution.