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RISS 인기검색어
- 검색키워드
- 저자 : Junil Kim (검색결과 135 건)
- 무료
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- 유료
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Oral Supplementation with Cocoa Extract Reduces UVB-Induced Wrinkles in Hairless Mouse Skin
Kim, Jong-Eun,Song, Dasom,Kim, Junil,Choi, Jina,Kim, Jong Rhan,Yoon, Hyun-Sun,Bae, Jung-Soo,Han, Mira,Lee, Sein,Hong, Ji Sun,Song, Dayoung,Kim, Seong-Jin,Son, Myoung-Jin,Choi, Sang-Woon,Chung, Jin Ho Elsevier 2016 The Journal of investigative dermatology Vol.136 No.5
<P>Cacao beans contain various bioactive phytochemicals that could modify the pathogeneses of certain diseases. Here, we report that oral administration of cacao powder (CP) attenuates UVB-induced skin wrinkling by the regulation of genes involved in dermal matrix production and maintenance. Transcriptome analysis revealed that 788 genes are down-or upregulated in the CP supplemented group, compared with the UVB-irradiated mouse skin controls. Among the differentially expressed genes, cathepsin G and serpin B6c play important roles in UVB-induced skin wrinkle formation. Gene regulatory network analysis also identified several candidate regulators responsible for the protective effects of CP supplementation against UVB-induced skin damage. CP also elicited antiwrinkle effects via inhibition of UVB-induced matrix metalloproteinases-1 expression in both the human skin equivalent model and human dermal fibroblasts. Inhibition of UVB-induced activator protein-1 via CP supplementation is likely to affect the expression of matrix metalloproteinases-1. CP supplementation also downregulates the expression of cathepsin G in human dermal fibroblasts. 5-(3',4'-Dihydroxyphenyl)-gamma-valerolactone, a major in vivo metabolite of CP, showed effects similar to CP supplementation. These results suggest that cacao extract may offer a protective effect against photoaging by inhibiting the breakdown of dermal matrix, which leads to an overall reduction in wrinkle formation.</P>
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The co-regulation mechanism of transcription factors in the human gene regulatory network
Kim, Junil,Choi, Minsoo,Kim, Jeong-Rae,Jin, Hua,Kim, V. Narry,Cho, Kwang-Hyun Oxford University Press 2012 Nucleic acids research Vol.40 No.18
<P>The co-regulation of transcription factors (TFs) has been widely observed in various species. Why is such a co-regulation mechanism needed for transcriptional regulation? To answer this question, the following experiments and analyses were performed. First, examination of the human gene regulatory network (GRN) indicated that co-regulation was significantly enriched in the human GRN. Second, mathematical simulation of an artificial regulatory network showed that the co-regulation mechanism was related to the biphasic dose–response patterns of TFs. Third, the relationship between the co-regulation mechanism and the biphasic dose–response pattern was confirmed using microarray experiments examining different time points and different doses of the toxicant tetrachlorodibenzodioxin. Finally, two mathematical models were constructed to mimic highly co-regulated networks (HCNs) and little co-regulated networks (LCNs), and we found that HCNs were more robust to parameter perturbation than LCNs, whereas LCNs were faster in adaptation to environmental changes than HCNs.</P>
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Kim, Junil,Kim, Tae-Geon,Jung, Sung Hoon,Kim, Jeong-Rae,Park, Taesung,Heslop-Harrison, Pat,Cho, Kwang-Hyun Oxford University Press 2008 Bioinformatics Vol.24 No.13
<P>MOTIVATION: Gene regulatory networks (GRNs) govern cellular differentiation processes and enable construction of multicellular organisms from single cells. Although such networks are complex, there must be evolutionary design principles that shape the network to its present form, gaining complexity from simple modules. RESULTS: To isolate particular design principles, we have computationally evolved random regulatory networks with a preference to result either in hysteresis (switching threshold depending on current state), or in multistationarity (having multiple steady states), two commonly observed dynamical features of GRNs related to differentiation processes. We have analyzed the resulting evolved networks and compared their structures and characteristics with real GRNs reported from experiments. Conclusion: We found that the artificially evolved networks have particular topologies and it was notable that these topologies share important features and similarities with the real GRNs, particularly in contrasting properties of positive and negative feedback loops. We conclude that the structures of real GRNs are consistent with selection to favor one or other of the dynamical features of multistationarity or hysteresis. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.</P>
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Robustness and Evolvability of the Human Signaling Network
Kim, Junil,Vandamme, Drieke,Kim, Jeong-Rae,Munoz, Amaya Garcia,Kolch, Walter,Cho, Kwang-Hyun Public Library of Science 2014 PLoS computational biology Vol.10 No.7
<▼1><P>Biological systems are known to be both robust and evolvable to internal and external perturbations, but what causes these apparently contradictory properties? We used Boolean network modeling and attractor landscape analysis to investigate the evolvability and robustness of the human signaling network. Our results show that the human signaling network can be divided into an evolvable core where perturbations change the attractor landscape in state space, and a robust neighbor where perturbations have no effect on the attractor landscape. Using chemical inhibition and overexpression of nodes, we validated that perturbations affect the evolvable core more strongly than the robust neighbor. We also found that the evolvable core has a distinct network structure, which is enriched in feedback loops, and features a higher degree of scale-freeness and longer path lengths connecting the nodes. In addition, the genes with high evolvability scores are associated with evolvability-related properties such as rapid evolvability, low species broadness, and immunity whereas the genes with high robustness scores are associated with robustness-related properties such as slow evolvability, high species broadness, and oncogenes. Intriguingly, US Food and Drug Administration-approved drug targets have high evolvability scores whereas experimental drug targets have high robustness scores.</P></▼1><▼2><P><B>Author Summary</B></P><P>Biological systems are known to be robust and evolvable to internal mutations and external environmental changes. What causes these apparently contradictory properties? This study shows that the human signaling network can be decomposed into two structurally distinct subgroups of links that provide both evolvability to environmental changes and robustness against internal mutations. The decomposition of the human signaling network reveals an evolutionary design principle of the network, and also facilitates the identification of potential drug targets.</P></▼2>
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Fabrication Methods for Nanowire Tunnel FET with Locally Concentrated Silicon-germanium Channel
Junil Lee,Ryoongbin Lee,Sihyun Kim,Euyhwan Park,Hyun-Min Kim,Kitae Lee,Sangwan Kim,Byung-Gook Park 대한전자공학회 2019 Journal of semiconductor technology and science Vol.19 No.1
This paper proposes a method to fabricate tunnel field-effect transistors (TFETs) which have Silicon Germanium (SiGe) nanowire channel with graded Ge concentration on Silicon-on-insulator (SOI). To obtain the concentration-graded SiGe channel, Ge condensation method which is a kind of oxidation is adopted. The rectangular shape of the channel becomes a rounded nanowire through the Ge condensation process. The TFET with the concentration-graded SiGe channel can improve drive current due to a smaller band gap at the Gecondensed surface of the channel compared to Si or non-condensed SiGe channel TFETs. The electrical characteristics of the proposed TFET are verified by technology computer-aided design (TCAD) simulation.
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A review on gene regulatory network reconstruction algorithms based on single cell RNA sequencing
Kim Hyeonkyu,Choi Hwisoo,Lee Daewon,Kim Junil 한국유전학회 2024 Genes & Genomics Vol.46 No.1
Background Understanding gene regulatory networks (GRNs) is essential for unraveling the molecular mechanisms governing cellular behavior. With the advent of high-throughput transcriptome measurement technology, researchers have aimed to reverse engineer the biological systems, extracting gene regulatory rules from their outputs, which represented by gene expression data. Bulk RNA sequencing, a widely used method for measuring gene expression, has been employed for GRN reconstruction. However, it falls short in capturing dynamic changes in gene expression at the level of individual cells since it averages gene expression across mixed cell populations. Objective In this review, we provide an overview of 15 GRN reconstruction tools and discuss their respective strengths and limitations, particularly in the context of single cell RNA sequencing (scRNA-seq). Methods Recent advancements in scRNA-seq break new ground of GRN reconstruction. They offer snapshots of the individual cell transcriptomes and capturing dynamic changes. We emphasize how these technological breakthroughs have enhanced GRN reconstruction. Conclusion GRN reconstructors can be classified based on their requirement for cellular trajectory, which represents a dynamical cellular process including differentiation, aging, or disease progression. Benchmarking studies support the superiority of GRN reconstructors that do not require trajectory analysis in identifying regulator-target relationships. However, methods equipped with trajectory analysis demonstrate better performance in identifying key regulatory factors. In conclusion, researchers should select a suitable GRN reconstructor based on their specific research objectives. Background Understanding gene regulatory networks (GRNs) is essential for unraveling the molecular mechanisms governing cellular behavior. With the advent of high-throughput transcriptome measurement technology, researchers have aimed to reverse engineer the biological systems, extracting gene regulatory rules from their outputs, which represented by gene expression data. Bulk RNA sequencing, a widely used method for measuring gene expression, has been employed for GRN reconstruction. However, it falls short in capturing dynamic changes in gene expression at the level of individual cells since it averages gene expression across mixed cell populations. Objective In this review, we provide an overview of 15 GRN reconstruction tools and discuss their respective strengths and limitations, particularly in the context of single cell RNA sequencing (scRNA-seq). Methods Recent advancements in scRNA-seq break new ground of GRN reconstruction. They offer snapshots of the individual cell transcriptomes and capturing dynamic changes. We emphasize how these technological breakthroughs have enhanced GRN reconstruction. Conclusion GRN reconstructors can be classified based on their requirement for cellular trajectory, which represents a dynamical cellular process including differentiation, aging, or disease progression. Benchmarking studies support the superiority of GRN reconstructors that do not require trajectory analysis in identifying regulator-target relationships. However, methods equipped with trajectory analysis demonstrate better performance in identifying key regulatory factors. In conclusion, researchers should select a suitable GRN reconstructor based on their specific research objectives.
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