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Kang W, Jia Z, Tang D, Zhao X, Shi J, Jia Q, He K, Feng Q. Time-Course Transcriptome Analysis for Drug Repositioning in Fusobacterium nucleatum-Infected Human Gingival Fibroblasts. Front Cell Dev Biol 2019; 7:204. [PMID: 31608279 PMCID: PMC6771468 DOI: 10.3389/fcell.2019.00204] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Accepted: 09/05/2019] [Indexed: 12/20/2022] Open
Abstract
Fusobacterium nucleatum (F. nucleatum) is a crucial periodontal pathogen and human gingival fibroblasts (GFs) are the first line of defense against oral pathogens. However, the research on potential molecular mechanisms of host defense and effective treatment of F. nucleatum infection in GFs remains scarce. In this study, we undertook a time-series experiment and performed an RNA-seq analysis to explore gene expression profiles during the process of F. nucleatum infection in GFs. Differentially expressed genes (DEGs) could be divided into three coexpression clusters. Functional analysis revealed that the immune-related signaling pathways were more overrepresented at the early stage, while metabolic pathways were mainly enriched at the late stage. We computationally identified several U.S. Food and Drug Administration (FDA)-approved drugs that could protect the F. nucleatum infected GFs via a coexpression-based drug repositioning approach. Biologically, we confirmed that six drugs (etravirine, zalcitabine, wortmannin, calcium D-pantothenate, ellipticine, and tanespimycin) could significantly decrease F. nucleatum-induced reactive oxygen species (ROS) generation and block the Protein Kinase B (PKB/AKT)/mitogen-activated protein kinase signaling pathways. Our study provides more detailed molecular mechanisms of the process by which F. nucleatum infects GFs and illustrates the value of the cogena-based drug repositioning method and the potential therapeutic application of these tested drugs in the treatment of F. nucleatum infection.
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Affiliation(s)
- Wenyan Kang
- Department of Human Microbiome, School and Hospital of Stomatology, Shandong University and Shandong Provincial Key Laboratory of Oral Tissue Regeneration and Shandong Engineering Laboratory for Dental Materials and Oral Tissue Regeneration, Jinan, China
- Department of Periodontology, School of Stomatology, Shandong University, Jinan, China
| | - Zhilong Jia
- Laboratory of Translational Medicine, Chinese PLA General Hospital, Beijing, China
- Beijing Key Laboratory of Chronic Heart Failure Precision Medicine, Chinese PLA General Hospital, Beijing, China
| | - Di Tang
- Department of Human Microbiome, School and Hospital of Stomatology, Shandong University and Shandong Provincial Key Laboratory of Oral Tissue Regeneration and Shandong Engineering Laboratory for Dental Materials and Oral Tissue Regeneration, Jinan, China
| | - Xiaojing Zhao
- Laboratory of Translational Medicine, Chinese PLA General Hospital, Beijing, China
- Beijing Key Laboratory of Chronic Heart Failure Precision Medicine, Chinese PLA General Hospital, Beijing, China
| | - Jinlong Shi
- Beijing Key Laboratory of Chronic Heart Failure Precision Medicine, Chinese PLA General Hospital, Beijing, China
| | - Qian Jia
- Laboratory of Translational Medicine, Chinese PLA General Hospital, Beijing, China
- Beijing Key Laboratory of Chronic Heart Failure Precision Medicine, Chinese PLA General Hospital, Beijing, China
| | - Kunlun He
- Laboratory of Translational Medicine, Chinese PLA General Hospital, Beijing, China
- Beijing Key Laboratory of Chronic Heart Failure Precision Medicine, Chinese PLA General Hospital, Beijing, China
| | - Qiang Feng
- Department of Human Microbiome, School and Hospital of Stomatology, Shandong University and Shandong Provincial Key Laboratory of Oral Tissue Regeneration and Shandong Engineering Laboratory for Dental Materials and Oral Tissue Regeneration, Jinan, China
- State Key Laboratory of Microbial Technology, Shandong University, Qingdao, China
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Cheng T, Hao M, Takeda T, Bryant SH, Wang Y. Large-Scale Prediction of Drug-Target Interaction: a Data-Centric Review. AAPS J 2017; 19:1264-1275. [PMID: 28577120 PMCID: PMC11097213 DOI: 10.1208/s12248-017-0092-6] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2017] [Accepted: 04/25/2017] [Indexed: 11/30/2022] Open
Abstract
The prediction of drug-target interactions (DTIs) is of extraordinary significance to modern drug discovery in terms of suggesting new drug candidates and repositioning old drugs. Despite technological advances, large-scale experimental determination of DTIs is still expensive and laborious. Effective and low-cost computational alternatives remain in strong need. Meanwhile, open-access resources have been rapidly growing with massive amount of bioactivity data becoming available, creating unprecedented opportunities for the development of novel in silico models for large-scale DTI prediction. In this work, we review the state-of-the-art computational approaches for identifying DTIs from a data-centric perspective: what the underlying data are and how they are utilized in each study. We also summarize popular public data resources and online tools for DTI prediction. It is found that various types of data were employed including properties of chemical structures, drug therapeutic effects and side effects, drug-target binding, drug-drug interactions, bioactivity data of drug molecules across multiple biological targets, and drug-induced gene expressions. More often, the heterogeneous data were integrated to offer better performance. However, challenges remain such as handling data imbalance, incorporating negative samples and quantitative bioactivity data, as well as maintaining cross-links among different data sources, which are essential for large-scale and automated information integration.
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Affiliation(s)
- Tiejun Cheng
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA
| | - Ming Hao
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA
| | - Takako Takeda
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA
| | - Stephen H Bryant
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA
| | - Yanli Wang
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA.
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Yea SJ, Kim BY, Kim C, Yi MY. A framework for the targeted selection of herbs with similar efficacy by exploiting drug repositioning technique and curated biomedical knowledge. JOURNAL OF ETHNOPHARMACOLOGY 2017; 208:117-128. [PMID: 28687508 DOI: 10.1016/j.jep.2017.06.048] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2017] [Revised: 06/27/2017] [Accepted: 06/27/2017] [Indexed: 06/07/2023]
Abstract
ETHNO PHARMACOLOGICAL RELEVANCE Plants have been the most important natural resources for traditional medicine and for the modern pharmaceutical industry. They have been in demand in regards to finding alternative medicinal herbs with similar efficacy. Due to the very low probability of discovering useful compounds by random screening, researchers have advocated for using targeted selection approaches. Furthermore, because drug repositioning can speed up the process of drug development, an integrated technique that exploits chemical, genetic, and disease information has been recently developed. Building upon these findings, in this paper, we propose a novel framework for the targeted selection of herbs with similar efficacy by exploiting drug repositioning technique and curated modern scientific biomedical knowledge, with the goal of improving the possibility of inferring the traditional empirical ethno-pharmacological knowledge. MATERIALS AND METHODS To rank candidate herbs on the basis of similarities against target herb, we proposed and evaluated a framework that is comprised of the following four layers: links, extract, similarity, and model. In the framework, multiple databases are linked to build an herb-compound-protein-disease network which was composed of one tripartite network and two bipartite networks allowing comprehensive and detailed information to be extracted. Further, various similarity scores between herbs are calculated, and then prediction models are trained and tested on the basis of theses similarity features. RESULTS The proposed framework has been found to be feasible in terms of link loss. Out of the 50 similarities, the best one enhanced the performance of ranking herbs with similar efficacy by about 120-320% compared with our previous study. Also, the prediction model showed improved performance by about 180-480%. While building the prediction model, we identified the compound information as being the most important knowledge source and structural similarity as the most useful measure. CONCLUSIONS In the proposed framework, we took the knowledge of herbal medicine, chemistry, biology, and medicine into consideration to rank herbs with similar efficacy in candidates. The experimental results demonstrated that the performances of framework outperformed the baselines and identified the important knowledge source and useful similarity measure.
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Affiliation(s)
- Sang-Jun Yea
- Graduate School of Knowledge Service Engineering, Korea Advanced Institute of Science and Technology, Republic of Korea; K-herb Research Center, Korea Institute of Oriental Medicine, Republic of Korea
| | - Bu-Yeo Kim
- KM Convergence Research Division, Korea Institute of Oriental Medicine, Republic of Korea
| | - Chul Kim
- K-herb Research Center, Korea Institute of Oriental Medicine, Republic of Korea.
| | - Mun Yong Yi
- Graduate School of Knowledge Service Engineering, Korea Advanced Institute of Science and Technology, Republic of Korea.
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Chen FS, Jiang HY, Jiang Z. Prediction of drug–pathway interaction pairs with a disease-combined LSA-PU-KNN method. MOLECULAR BIOSYSTEMS 2017; 13:2583-2591. [DOI: 10.1039/c7mb00441a] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
This paper proposes a prediction of potential associations between drugs and pathways based on a disease-related LSA-PU-KNN method.
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Affiliation(s)
- Fan-Shu Chen
- Shanghai Key Laboratory of Multidimensional Information Processing
- East China Normal University
- Shanghai 200262
- China
- Department of Computer Science and Technology
| | - Hui-Yan Jiang
- Shanghai Key Laboratory of Multidimensional Information Processing
- East China Normal University
- Shanghai 200262
- China
- Department of Computer Science and Technology
| | - Zhenran Jiang
- Shanghai Key Laboratory of Multidimensional Information Processing
- East China Normal University
- Shanghai 200262
- China
- Department of Computer Science and Technology
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Wang C, Liu J, Luo F, Hu QN. Multi-fields model for predicting target–ligand interaction. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2016.03.079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Peng L, Liao B, Zhu W, Li Z, Li K. Predicting Drug-Target Interactions With Multi-Information Fusion. IEEE J Biomed Health Inform 2015; 21:561-572. [PMID: 26731781 DOI: 10.1109/jbhi.2015.2513200] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Identifying potential associations between drugs and targets is a critical prerequisite for modern drug discovery and repurposing. However, predicting these associations is difficult because of the limitations of existing computational methods. Most models only consider chemical structures and protein sequences, and other models are oversimplified. Moreover, datasets used for analysis contain only true-positive interactions, and experimentally validated negative samples are unavailable. To overcome these limitations, we developed a semi-supervised based learning framework called NormMulInf through collaborative filtering theory by using labeled and unlabeled interaction information. The proposed method initially determines similarity measures, such as similarities among samples and local correlations among the labels of the samples, by integrating biological information. The similarity information is then integrated into a robust principal component analysis model, which is solved using augmented Lagrange multipliers. Experimental results on four classes of drug-target interaction networks suggest that the proposed approach can accurately classify and predict drug-target interactions. Part of the predicted interactions are reported in public databases. The proposed method can also predict possible targets for new drugs and can be used to determine whether atropine may interact with alpha1B- and beta1- adrenergic receptors. Furthermore, the developed technique identifies potential drugs for new targets and can be used to assess whether olanzapine and propiomazine may target 5HT2B. Finally, the proposed method can potentially address limitations on studies of multitarget drugs and multidrug targets.
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Multiscale Modeling of Drug-induced Effects of ReDuNing Injection on Human Disease: From Drug Molecules to Clinical Symptoms of Disease. Sci Rep 2015; 5:10064. [PMID: 25973739 PMCID: PMC4431313 DOI: 10.1038/srep10064] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2014] [Accepted: 03/26/2015] [Indexed: 12/16/2022] Open
Abstract
ReDuNing injection (RDN) is a patented traditional Chinese medicine, and the components of it were proven to have antiviral and important anti-inflammatory activities. Several reports showed that RDN had potential effects in the treatment of influenza and pneumonia. Though there were several experimental reports about RDN, the experimental results were not enough and complete due to that it was difficult to predict and verify the effect of RDN for a large number of human diseases. Here we employed multiscale model by integrating molecular docking, network pharmacology and the clinical symptoms information of diseases and explored the interaction mechanism of RDN on human diseases. Meanwhile, we analyzed the relation among the drug molecules, target proteins, biological pathways, human diseases and the clinical symptoms about it. Then we predicted potential active ingredients of RDN, the potential target proteins, the key pathways and related diseases. These attempts may offer several new insights to understand the pharmacological properties of RDN and provide benefit for its new clinical applications and research.
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Jiang Y, Zhang P, Li LP, He YC, Gao RJ, Gao YF. Identification of novel thyroid cancer-related genes and chemicals using shortest path algorithm. BIOMED RESEARCH INTERNATIONAL 2015; 2015:964795. [PMID: 25874234 PMCID: PMC4385622 DOI: 10.1155/2015/964795] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2014] [Accepted: 12/05/2014] [Indexed: 02/07/2023]
Abstract
Thyroid cancer is a typical endocrine malignancy. In the past three decades, the continued growth of its incidence has made it urgent to design effective treatments to treat this disease. To this end, it is necessary to uncover the mechanism underlying this disease. Identification of thyroid cancer-related genes and chemicals is helpful to understand the mechanism of thyroid cancer. In this study, we generalized some previous methods to discover both disease genes and chemicals. The method was based on shortest path algorithm and applied to discover novel thyroid cancer-related genes and chemicals. The analysis of the final obtained genes and chemicals suggests that some of them are crucial to the formation and development of thyroid cancer. It is indicated that the proposed method is effective for the discovery of novel disease genes and chemicals.
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Affiliation(s)
- Yang Jiang
- Department of Surgery, China-Japan Union Hospital of Jilin University, Changchun 130033, China
| | - Peiwei Zhang
- The Key Laboratory of Stem Cell Biology, Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Li-Peng Li
- Department of Surgery, China-Japan Union Hospital of Jilin University, Changchun 130033, China
| | - Yi-Chun He
- Department of Surgery, China-Japan Union Hospital of Jilin University, Changchun 130033, China
| | - Ru-jian Gao
- Department of Surgery, China-Japan Union Hospital of Jilin University, Changchun 130033, China
| | - Yu-Fei Gao
- Department of Surgery, China-Japan Union Hospital of Jilin University, Changchun 130033, China
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Kuang Q, Wang M, Li R, Dong Y, Li Y, Li M. A systematic investigation of computation models for predicting Adverse Drug Reactions (ADRs). PLoS One 2014; 9:e105889. [PMID: 25180585 PMCID: PMC4152017 DOI: 10.1371/journal.pone.0105889] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2014] [Accepted: 07/25/2014] [Indexed: 02/03/2023] Open
Abstract
Background Early and accurate identification of adverse drug reactions (ADRs) is critically important for drug development and clinical safety. Computer-aided prediction of ADRs has attracted increasing attention in recent years, and many computational models have been proposed. However, because of the lack of systematic analysis and comparison of the different computational models, there remain limitations in designing more effective algorithms and selecting more useful features. There is therefore an urgent need to review and analyze previous computation models to obtain general conclusions that can provide useful guidance to construct more effective computational models to predict ADRs. Principal Findings In the current study, the main work is to compare and analyze the performance of existing computational methods to predict ADRs, by implementing and evaluating additional algorithms that have been earlier used for predicting drug targets. Our results indicated that topological and intrinsic features were complementary to an extent and the Jaccard coefficient had an important and general effect on the prediction of drug-ADR associations. By comparing the structure of each algorithm, final formulas of these algorithms were all converted to linear model in form, based on this finding we propose a new algorithm called the general weighted profile method and it yielded the best overall performance among the algorithms investigated in this paper. Conclusion Several meaningful conclusions and useful findings regarding the prediction of ADRs are provided for selecting optimal features and algorithms.
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Affiliation(s)
- Qifan Kuang
- College of Chemistry, Sichuan University, Chengdu, China
| | - MinQi Wang
- College of Chemistry, Sichuan University, Chengdu, China
| | - Rong Li
- College of Computer Science, Sichuan University, Chengdu, China
| | - YongCheng Dong
- College of Life Science, Sichuan University, Chengdu, China
| | - Yizhou Li
- College of Chemistry, Sichuan University, Chengdu, China
| | - Menglong Li
- College of Chemistry, Sichuan University, Chengdu, China
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Song M, Yan Y, Jiang Z. Drug–pathway interaction prediction via multiple feature fusion. ACTA ACUST UNITED AC 2014; 10:2907-13. [DOI: 10.1039/c4mb00199k] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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