• Title/Summary/Keyword: predictive toxicology

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Computational approaches for prediction of protein-protein interaction between Foot-and-mouth disease virus and Sus scrofa based on RNA-Seq

  • Park, Tamina;Kang, Myung-gyun;Nah, Jinju;Ryoo, Soyoon;Wee, Sunghwan;Baek, Seung-hwa;Ku, Bokkyung;Oh, Yeonsu;Cho, Ho-seong;Park, Daeui
    • Korean Journal of Veterinary Service
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    • v.42 no.2
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    • pp.73-83
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    • 2019
  • Foot-and-Mouth Disease (FMD) is a highly contagious trans-boundary viral disease caused by FMD virus, which causes huge economic losses. FMDV infects cloven hoofed (two-toed) mammals such as cattle, sheep, goats, pigs and various wildlife species. To control the FMDV, it is necessary to understand the life cycle and the pathogenesis of FMDV in host. Especially, the protein-protein interaction between FMDV and host will help to understand the survival cycle of viruses in host cell and establish new therapeutic strategies. However, the computational approach for protein-protein interaction between FMDV and pig hosts have not been applied to studies of the onset mechanism of FMDV. In the present work, we have performed the prediction of the pig's proteins which interact with FMDV based on RNA-Seq data, protein sequence, and structure information. After identifying the virus-host interaction, we looked for meaningful pathways and anticipated changes in the host caused by infection with FMDV. A total of 78 proteins of pig were predicted as interacting with FMDV. The 156 interactions include 94 interactions predicted by sequence-based method and the 62 interactions predicted by structure-based method using domain information. The protein interaction network contained integrin as well as STYK1, VTCN1, IDO1, CDH3, SLA-DQB1, FER, and FGFR2 which were related to the up-regulation of inflammation and the down-regulation of cell adhesion and host defense systems such as macrophage and leukocytes. These results provide clues to the knowledge and mechanism of how FMDV affects the host cell.

GraPT: Genomic InteRpreter about Predictive Toxicology

  • Woo Jung-Hoon;Park Yu-Rang;Jung Yong;Kim Ji-Hun;Kim Ju-Han
    • Genomics & Informatics
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    • v.4 no.3
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    • pp.129-132
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    • 2006
  • Toxicogenomics has recently emerged in the field of toxicology and the DNA microarray technique has become common strategy for predictive toxicology which studies molecular mechanism caused by exposure of chemical or environmental stress. Although microarray experiment offers extensive genomic information to the researchers, yet high dimensional characteristic of the data often makes it hard to extract meaningful result. Therefore we developed toxicant enrichment analysis similar to the common enrichment approach. We also developed web-based system graPT to enable considerable prediction of toxic endpoints of experimental chemical.

Trend of In Silico Prediction Research Using Adverse Outcome Pathway (독성발현경로(Adverse Outcome Pathway)를 활용한 In Silico 예측기술 연구동향 분석)

  • Sujin Lee;Jongseo Park;Sunmi Kim;Myungwon Seo
    • Journal of Environmental Health Sciences
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    • v.50 no.2
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    • pp.113-124
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    • 2024
  • Background: The increasing need to minimize animal testing has sparked interest in alternative methods with more humane, cost-effective, and time-saving attributes. In particular, in silico-based computational toxicology is gaining prominence. Adverse outcome pathway (AOP) is a biological map depicting toxicological mechanisms, composed of molecular initiating events (MIEs), key events (KEs), and adverse outcomes (AOs). To understand toxicological mechanisms, predictive models are essential for AOP components in computational toxicology, including molecular structures. Objectives: This study reviewed the literature and investigated previous research cases related to AOP and in silico methodologies. We describe the results obtained from the analysis, including predictive techniques and approaches that can be used for future in silico-based alternative methods to animal testing using AOP. Methods: We analyzed in silico methods and databases used in the literature to identify trends in research on in silico prediction models. Results: We reviewed 26 studies related to AOP and in silico methodologies. The ToxCast/Tox21 database was commonly used for toxicity studies, and MIE was the most frequently used predictive factor among the AOP components. Machine learning was most widely used among prediction techniques, and various in silico methods, such as deep learning, molecular docking, and molecular dynamics, were also utilized. Conclusions: We analyzed the current research trends regarding in silico-based alternative methods for animal testing using AOPs. Developing predictive techniques that reflect toxicological mechanisms will be essential to replace animal testing with in silico methods. In the future, since the applicability of various predictive techniques is increasing, it will be necessary to continue monitoring the trend of predictive techniques and in silico-based approaches.

Effect of gemigliptin on cardiac ischemia/reperfusion and spontaneous hypertensive rat models

  • Nam, Dae-Hwan;Park, Jinsook;Park, Sun-Hyun;Kim, Ki-Suk;Baek, Eun Bok
    • The Korean Journal of Physiology and Pharmacology
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    • v.23 no.5
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    • pp.329-334
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    • 2019
  • Diabetes is associated with an increased risk of cardiovascular complications. Dipeptidyl peptidase-4 (DPP-IV) inhibitors are used clinically to reduce high blood glucose levels as an antidiabetic agent. However, the effect of the DPP-IV inhibitor gemigliptin on ischemia/reperfusion (I/R)-induced myocardial injury and hypertension is unknown. In this study, we assessed the effects and mechanisms of gemigliptin in rat models of myocardial I/R injury and spontaneous hypertension. Gemigliptin (20 and 100 mg/kg/d) or vehicle was administered intragastrically to Sprague-Dawley rats for 4 weeks before induction of I/R injury. Gemigliptin exerted a preventive effect on I/R injury by improving hemodynamic function and reducing infarct size compared to the vehicle control group. Moreover, administration of gemigliptin (0.03% and 0.15%) powder in food for 4 weeks reversed hypertrophy and improved diastolic function in spontaneously hypertensive rats. We report here a novel effect of the gemigliptin on I/R injury and hypertension.

cDNA Microarray gene expression profiling of hydroxyurea, paclitaxel and p-anisidine that are genotoxic compounds with differing tumorigenicity results

  • Lee, Michael;Jung Kwon;Kim, Se-Nyun;Kim, Ja-Eun;Koh, Woo-Suk;Song, Chang-Woo;Chung, Moon-Koo
    • Proceedings of the Korean Society of Toxicology Conference
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    • 2003.05a
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    • pp.36-37
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    • 2003
  • The potential application of toxicogenomics to predictive toxicology has been discussed widely, but the utility of the approach remains largely unproven. Using cDNA microarrays, we have compared the gene expression profiles produced in mouse lymphoma cells by three genotoxic compounds, hydroxyurea (a carcino- gen), p-anisidine (a noncarcinogen) and paclitaxel (carcinogenicity unknown). (omitted)

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Advancing Risk Assessment through the Application of Systems Toxicology

  • Sauer, John Michael;Kleensang, Andre;Peitsch, Manuel C.;Hayes, A. Wallace
    • Toxicological Research
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    • v.32 no.1
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    • pp.5-8
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    • 2016
  • Risk assessment is the process of quantifying the probability of a harmful effect to individuals or populations from human activities. Mechanistic approaches to risk assessment have been generally referred to as systems toxicology. Systems toxicology makes use of advanced analytical and computational tools to integrate classical toxicology and quantitative analysis of large networks of molecular and functional changes occurring across multiple levels of biological organization. Three presentations including two case studies involving both in vitro and in vivo approaches described the current state of systems toxicology and the potential for its future application in chemical risk assessment.