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Mohammadi E, Benfeitas R, Turkez H, Boren J, Nielsen J, Uhlen M, Mardinoglu A. Applications of Genome-Wide Screening and Systems Biology Approaches in Drug Repositioning. Cancers (Basel) 2020; 12:E2694. [PMID: 32967266 PMCID: PMC7563533 DOI: 10.3390/cancers12092694] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 09/14/2020] [Accepted: 09/16/2020] [Indexed: 12/24/2022] Open
Abstract
Modern drug discovery through de novo drug discovery entails high financial costs, low success rates, and lengthy trial periods. Drug repositioning presents a suitable approach for overcoming these issues by re-evaluating biological targets and modes of action of approved drugs. Coupling high-throughput technologies with genome-wide essentiality screens, network analysis, genome-scale metabolic modeling, and machine learning techniques enables the proposal of new drug-target signatures and uncovers unanticipated modes of action for available drugs. Here, we discuss the current issues associated with drug repositioning in light of curated high-throughput multi-omic databases, genome-wide screening technologies, and their application in systems biology/medicine approaches.
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Affiliation(s)
- Elyas Mohammadi
- Science for Life Laboratory, KTH–Royal Institute of Technology, SE-17121 Stockholm, Sweden; (E.M.); (M.U.)
- Department of Animal Science, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran
| | - Rui Benfeitas
- National Bioinformatics Infrastructure Sweden (NBIS), Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, SE-10691 Stockholm, Sweden;
| | - Hasan Turkez
- Department of Medical Biology, Faculty of Medicine, Atatürk University, 25240 Erzurum, Turkey;
| | - Jan Boren
- Department of Molecular and Clinical Medicine, University of Gothenburg, The Wallenberg Laboratory, Sahlgrenska University Hospital, SE-41345 Gothenburg, Sweden;
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE-41296 Gothenburg, Sweden;
- BioInnovation Institute, DK-2200 Copenhagen N, Denmark
| | - Mathias Uhlen
- Science for Life Laboratory, KTH–Royal Institute of Technology, SE-17121 Stockholm, Sweden; (E.M.); (M.U.)
| | - Adil Mardinoglu
- Science for Life Laboratory, KTH–Royal Institute of Technology, SE-17121 Stockholm, Sweden; (E.M.); (M.U.)
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London SE1 9RT, UK
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Chen X, Zheng C, Wang C, Guo Z, Gao S, Ning Z, Huang C, Lu C, Fu Y, Guan D, Lu A, Wang Y. Systems-Mapping of Herbal Effects on Complex Diseases Using the Network-Perturbation Signatures. Front Pharmacol 2018; 9:1174. [PMID: 30405409 PMCID: PMC6201628 DOI: 10.3389/fphar.2018.01174] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2018] [Accepted: 09/28/2018] [Indexed: 01/15/2023] Open
Abstract
The herbs have proven to hold great potential to improve people's health and wellness during clinical practice over the past millennia. However, herbal medicine for the personalized treatment of disease is still under investigation owing to the complex multi-component interactions in herbs. To reveal the valuable insights for herbal synergistic therapy, we have chosen Traditional Chinese Medicine (TCM) as a case to illustrate the art and science behind the complicated multi-molecular, multi-genes interaction systems, and how the good practices of herbal combination therapy are applicable to personalized treatment. Here, we design system-wide interaction map strategy to provide a generic solution to establish the links between diseases and herbs based on comprehensive testing of molecular signatures in herb-disease pairs. Firstly, we integrated gene expression profiles from 189 diseases to characterize the disease-pathological feature. Then, we generated the perturbation signatures from the huge chemical informatics data and pharmacological data for each herb, which were represented the targets affected by the ingredients in the herb. So that we could assess the effects of herbs on the individual. Finally, we integrated the data of 189 diseases and 502 herbs, yielding the optimal herbal combinations for the diseases based on the strategy, and verifying the reliability of the strategy through the permutation testing and literature verification. Furthermore, we propose a novel formula as a candidate therapeutic drugs of rheumatoid arthritis and demonstrate its therapeutic mechanism through the systematic analysis of the influencing targets and biological processes. Overall, this computational method provides a systematic approach, which blended herbal medicine and omics data sets, allowing for the development of novel drug combinations for complex human diseases.
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Affiliation(s)
- Xuetong Chen
- Center of Bioinformatics, College of Life Science, Northwest A & F University, Yangling, China.,School of Chinese Medicine, Institute of Integrated Bioinformedicine and Translational Science, Hong Kong Baptist University, Hong Kong, Hong Kong
| | - Chunli Zheng
- Center of Bioinformatics, College of Life Science, Northwest A & F University, Yangling, China
| | - Chun Wang
- School of Chinese Medicine, Institute of Integrated Bioinformedicine and Translational Science, Hong Kong Baptist University, Hong Kong, Hong Kong.,Institute of Basic Theory for Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Zihu Guo
- Center of Bioinformatics, College of Life Science, Northwest A & F University, Yangling, China
| | - Shuo Gao
- Center of Bioinformatics, College of Life Science, Northwest A & F University, Yangling, China
| | - Zhangchi Ning
- Institute of Basic Theory for Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Chao Huang
- Center of Bioinformatics, College of Life Science, Northwest A & F University, Yangling, China
| | - Cheng Lu
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yingxue Fu
- Center of Bioinformatics, College of Life Science, Northwest A & F University, Yangling, China
| | - Daogang Guan
- School of Chinese Medicine, Institute of Integrated Bioinformedicine and Translational Science, Hong Kong Baptist University, Hong Kong, Hong Kong
| | - Aiping Lu
- School of Chinese Medicine, Institute of Integrated Bioinformedicine and Translational Science, Hong Kong Baptist University, Hong Kong, Hong Kong
| | - Yonghua Wang
- Center of Bioinformatics, College of Life Science, Northwest A & F University, Yangling, China
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