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Yuan Y, Hu R, Chen S, Zhang X, Liu Z, Zhou G. CKG-IMC: An inductive matrix completion method enhanced by CKG and GNN for Alzheimer's disease compound-protein interactions prediction. Comput Biol Med 2024; 177:108612. [PMID: 38838556 DOI: 10.1016/j.compbiomed.2024.108612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 04/17/2024] [Accepted: 05/11/2024] [Indexed: 06/07/2024]
Abstract
Alzheimer's disease (AD) is one of the most prevalent chronic neurodegenerative disorders globally, with a rapidly growing population of AD patients and currently no effective therapeutic interventions available. Consequently, the development of therapeutic anti-AD drugs and the identification of AD targets represent one of the most urgent tasks. In this study, in addition to considering known drugs and targets, we explore compound-protein interactions (CPIs) between compounds and proteins relevant to AD. We propose a deep learning model called CKG-IMC to predict Alzheimer's disease compound-protein interaction relationships. CKG-IMC comprises three modules: a collaborative knowledge graph (CKG), a principal neighborhood aggregation graph neural network (PNA), and an inductive matrix completion (IMC). The collaborative knowledge graph is used to learn semantic associations between entities, PNA is employed to extract structural features of the relationship network, and IMC is utilized for CPIs prediction. Compared with a total of 16 baseline models based on similarities, knowledge graphs, and graph neural networks, our model achieves state-of-the-art performance in experiments of 10-fold cross-validation and independent test. Furthermore, we use CKG-IMC to predict compounds interacting with two confirmed AD targets, 42-amino-acid β-amyloid (Aβ42) protein and microtubule-associated protein tau (tau protein), as well as proteins interacting with five FDA-approved anti-AD drugs. The results indicate that the majority of predictions are supported by literature, and molecular docking experiments demonstrate a strong affinity between the predicted compounds and targets.
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Affiliation(s)
- Yongna Yuan
- School of Information Science & Engineering, Lanzhou University, South Tianshui Road, Lanzhou, 730000, Gansu, China.
| | - Rizhen Hu
- School of Information Science & Engineering, Lanzhou University, South Tianshui Road, Lanzhou, 730000, Gansu, China
| | - Siming Chen
- School of Information Science & Engineering, Lanzhou University, South Tianshui Road, Lanzhou, 730000, Gansu, China
| | - Xiaopeng Zhang
- School of Information Science & Engineering, Lanzhou University, South Tianshui Road, Lanzhou, 730000, Gansu, China
| | - Zhenyu Liu
- School of Information Science & Engineering, Lanzhou University, South Tianshui Road, Lanzhou, 730000, Gansu, China; School of Cyberspace Security, Gansu University of Political Science and Law, Anning West Road, Lanzhou, 730070, Gansu, China
| | - Gonghai Zhou
- School of Information Science & Engineering, Lanzhou University, South Tianshui Road, Lanzhou, 730000, Gansu, China
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Lee MH, Lee B, Park SE, Yang GE, Cheon S, Lee DH, Kang S, Sun YJ, Kim Y, Jung DS, Kim W, Kang J, Kim YR, Choi JW. Transcriptome-based deep learning analysis identifies drug candidates targeting protein synthesis and autophagy for the treatment of muscle wasting disorder. Exp Mol Med 2024; 56:904-921. [PMID: 38556548 PMCID: PMC11059359 DOI: 10.1038/s12276-024-01189-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 12/28/2023] [Accepted: 01/22/2024] [Indexed: 04/02/2024] Open
Abstract
Sarcopenia, the progressive decline in skeletal muscle mass and function, is observed in various conditions, including cancer and aging. The complex molecular biology of sarcopenia has posed challenges for the development of FDA-approved medications, which have mainly focused on dietary supplementation. Targeting a single gene may not be sufficient to address the broad range of processes involved in muscle loss. This study analyzed the gene expression signatures associated with cancer formation and 5-FU chemotherapy-induced muscle wasting. Our findings suggest that dimenhydrinate, a combination of 8-chlorotheophylline and diphenhydramine, is a potential therapeutic for sarcopenia. In vitro experiments demonstrated that dimenhydrinate promotes muscle progenitor cell proliferation through the phosphorylation of Nrf2 by 8-chlorotheophylline and promotes myotube formation through diphenhydramine-induced autophagy. Furthermore, in various in vivo sarcopenia models, dimenhydrinate induced rapid muscle tissue regeneration. It improved muscle regeneration in animals with Duchenne muscular dystrophy (DMD) and facilitated muscle and fat recovery in animals with chemotherapy-induced sarcopenia. As an FDA-approved drug, dimenhydrinate could be applied for sarcopenia treatment after a relatively short development period, providing hope for individuals suffering from this debilitating condition.
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Affiliation(s)
- Min Hak Lee
- College of Pharmacy, Kyung Hee University, Seoul, 02447, Republic of Korea
- Department of Biomedical and Pharmaceutical Sciences, Kyung Hee University, Seoul, 02447, Republic of Korea
- Department of Pharmacology, Institute of Regulatory Innovation Through Science, Kyung Hee University, Seoul, 02447, Republic of Korea
| | - Bada Lee
- College of Pharmacy, Kyung Hee University, Seoul, 02447, Republic of Korea
- Department of Biomedical and Pharmaceutical Sciences, Kyung Hee University, Seoul, 02447, Republic of Korea
| | - Se Eun Park
- College of Pharmacy, Kyung Hee University, Seoul, 02447, Republic of Korea
| | - Ga Eul Yang
- Center for Research and Development, Oncocross Ltd, Seoul, 04168, Republic of Korea
| | - Seungwoo Cheon
- Center for Research and Development, Oncocross Ltd, Seoul, 04168, Republic of Korea
| | - Dae Hoon Lee
- College of Pharmacy, Kyung Hee University, Seoul, 02447, Republic of Korea
- Department of Biomedical and Pharmaceutical Sciences, Kyung Hee University, Seoul, 02447, Republic of Korea
| | - Sukyeong Kang
- Department of Biomedical and Pharmaceutical Sciences, Kyung Hee University, Seoul, 02447, Republic of Korea
| | - Ye Ji Sun
- College of Pharmacy, Kyung Hee University, Seoul, 02447, Republic of Korea
- Department of Biomedical and Pharmaceutical Sciences, Kyung Hee University, Seoul, 02447, Republic of Korea
- Department of Pharmacology, Institute of Regulatory Innovation Through Science, Kyung Hee University, Seoul, 02447, Republic of Korea
| | - Yongjin Kim
- Center for Research and Development, Oncocross Ltd, Seoul, 04168, Republic of Korea
| | - Dong-Sub Jung
- Center for Research and Development, Oncocross Ltd, Seoul, 04168, Republic of Korea
| | - Wonwoo Kim
- Center for Research and Development, Oncocross Ltd, Seoul, 04168, Republic of Korea
| | - Jihoon Kang
- Center for Research and Development, Oncocross Ltd, Seoul, 04168, Republic of Korea
| | - Yi Rang Kim
- Department of Pharmacology, Institute of Regulatory Innovation Through Science, Kyung Hee University, Seoul, 02447, Republic of Korea.
- Center for Research and Development, Oncocross Ltd, Seoul, 04168, Republic of Korea.
| | - Jin Woo Choi
- College of Pharmacy, Kyung Hee University, Seoul, 02447, Republic of Korea.
- Department of Biomedical and Pharmaceutical Sciences, Kyung Hee University, Seoul, 02447, Republic of Korea.
- Department of Pharmacology, Institute of Regulatory Innovation Through Science, Kyung Hee University, Seoul, 02447, Republic of Korea.
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Norollahi SE, Babaei K, Balooei V, Karouei SMH, Ashoobi MT, Asghari Gharakhyli E, Samadani AA. Bioinformatic-based Study to Investigate the Structure and Function of Pro-inflammatory Cytokines TNFα and IL-6 Involved in the Pathogenesis of COVID-19. IRANIAN JOURNAL OF PATHOLOGY 2024; 19:205-217. [PMID: 39118801 PMCID: PMC11304465 DOI: 10.30699/ijp.2024.2015557.3211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Accepted: 01/06/2024] [Indexed: 08/10/2024]
Abstract
Background & Objective Besides the clinical and laboratory research on the COVID-19 virus, the bioinformatics study in the field of genetics of immunity to COVID-19 is of particular importance. In this account, studies show that in patients with COVID-19, the level of tumor necrosis alpha (TNFα) and interleukin-6 (IL-6) is high and in severe cases of COVID-19, the production of IL-6, TNF-α, and other cytokines increases profoundly. On the other hand, investigating the molecular structure and receptors of IL-6 and TNFα and the structural analysis of the receptor proteins may potentially help to develop new therapeutic plans for COVID-19 infection. Methods To identify genes with significant and different expressions in patients with COVID-19 in a microarray data set containing transcriptional profiles from GEO as a functional genomic database the GEO query package version 2.64.2 in a programming language R version 4.2.1 was downloaded. In this way, functional enrichment analysis for DEGs, WikiPathways, REGO, gene ontology, and STRING database was also investigated and employed. Results The structure and function of pro-inflammatory cytokines TNFα and IL-6 involved in the pathogenesis of COVID-19 were investigated, and in general, after performing various analyses in this study and extracting A series of genes with different expressions from the KEGG database, the final 5 DEGs include CXCL14, CXCL6, CCL8, CXCR1, TNFRSF10, and the relationship and expression effects of them were observed in different pathways. Conclusion IL-6 and TNFα were involved in immunological processes that had a direct and indirect relationship with the activation of cytokines, including IL6 and TNF-a, and cytokine storm, and this indicates their role in the formation of problems and complications, including ARDS, in COVID-19 patients. Of course, determining the effectiveness of each of these genes requires more specialized and clinical studies.
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Affiliation(s)
- Seyedeh Elham Norollahi
- Cancer Research Center and Department of Immunology, Semnan University of Medical Sciences, Semnan, Iran
| | - Kosar Babaei
- Noncommunicable Diseases Research Center, Neyshabur University of Medical Sciences, Neyshabur, Iran
| | - Vida Balooei
- Department of Veterinary Medicine, Babol Branch, Islamic Azad University, Babol, Iran
| | | | - Mohammad Taghi Ashoobi
- Department of Surgery, School of Medicine, Razi Hospital, Guilan University of Medical Sciences, Rasht, Iran
| | | | - Ali Akbar Samadani
- Guilan Road Trauma Research Center, Guilan University of Medical Sciences, Rasht, Iran
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Zhou Y, Liu Y, Gupta S, Paramo MI, Hou Y, Mao C, Luo Y, Judd J, Wierbowski S, Bertolotti M, Nerkar M, Jehi L, Drayman N, Nicolaescu V, Gula H, Tay S, Randall G, Wang P, Lis JT, Feschotte C, Erzurum SC, Cheng F, Yu H. A comprehensive SARS-CoV-2-human protein-protein interactome reveals COVID-19 pathobiology and potential host therapeutic targets. Nat Biotechnol 2023; 41:128-139. [PMID: 36217030 PMCID: PMC9851973 DOI: 10.1038/s41587-022-01474-0] [Citation(s) in RCA: 64] [Impact Index Per Article: 64.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Accepted: 08/15/2022] [Indexed: 01/25/2023]
Abstract
Studying viral-host protein-protein interactions can facilitate the discovery of therapies for viral infection. We use high-throughput yeast two-hybrid experiments and mass spectrometry to generate a comprehensive SARS-CoV-2-human protein-protein interactome network consisting of 739 high-confidence binary and co-complex interactions, validating 218 known SARS-CoV-2 host factors and revealing 361 novel ones. Our results show the highest overlap of interaction partners between published datasets and of genes differentially expressed in samples from COVID-19 patients. We identify an interaction between the viral protein ORF3a and the human transcription factor ZNF579, illustrating a direct viral impact on host transcription. We perform network-based screens of >2,900 FDA-approved or investigational drugs and identify 23 with significant network proximity to SARS-CoV-2 host factors. One of these drugs, carvedilol, shows clinical benefits for COVID-19 patients in an electronic health records analysis and antiviral properties in a human lung cell line infected with SARS-CoV-2. Our study demonstrates the value of network systems biology to understand human-virus interactions and provides hits for further research on COVID-19 therapeutics.
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Affiliation(s)
- Yadi Zhou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Yuan Liu
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA
- Center for Advanced Proteomics, Cornell University, Ithaca, NY, USA
| | - Shagun Gupta
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA
- Center for Advanced Proteomics, Cornell University, Ithaca, NY, USA
- Department of Computational Biology, Cornell University, Ithaca, NY, USA
| | - Mauricio I Paramo
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA
- Center for Advanced Proteomics, Cornell University, Ithaca, NY, USA
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY, USA
| | - Yuan Hou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Chengsheng Mao
- Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University, Chicago, IL, USA
| | - Yuan Luo
- Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University, Chicago, IL, USA
| | - Julius Judd
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY, USA
| | - Shayne Wierbowski
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA
- Center for Advanced Proteomics, Cornell University, Ithaca, NY, USA
- Department of Computational Biology, Cornell University, Ithaca, NY, USA
| | - Marta Bertolotti
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA
- Center for Advanced Proteomics, Cornell University, Ithaca, NY, USA
| | - Mriganka Nerkar
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY, USA
| | - Lara Jehi
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Nir Drayman
- Department of Molecular Biology and Biochemistry, University of California, Irvine, Irvine, CA, USA
| | - Vlad Nicolaescu
- Department of Microbiology, Ricketts Laboratory, University of Chicago, Chicago, IL, USA
| | - Haley Gula
- Department of Microbiology, Ricketts Laboratory, University of Chicago, Chicago, IL, USA
| | - Savaş Tay
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, IL, USA
| | - Glenn Randall
- Department of Microbiology, Ricketts Laboratory, University of Chicago, Chicago, IL, USA
| | - Peihui Wang
- Key Laboratory for Experimental Teratology of Ministry of Education and Advanced Medical Research Institute, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - John T Lis
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY, USA
| | - Cédric Feschotte
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY, USA
| | | | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA.
- Case Comprehensive Cancer Center, School of Medicine, Case Western Reserve University, Cleveland, OH, USA.
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, USA.
| | - Haiyuan Yu
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA.
- Center for Advanced Proteomics, Cornell University, Ithaca, NY, USA.
- Department of Computational Biology, Cornell University, Ithaca, NY, USA.
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Jeevanandam J, Paramasivam E, Palanisamy A, Ragavendran S, Thangavel SN. Molecular insights on bioactive compounds againstCovid-19: A Network pharmacological and computational study. Curr Comput Aided Drug Des 2022; 18:CAD-EPUB-126303. [PMID: 36111763 DOI: 10.2174/1573409918666220914092145] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 07/04/2022] [Accepted: 07/20/2022] [Indexed: 11/22/2022]
Abstract
BACKGROUND Network pharmacology based identification of phytochemicals in the form of cocktails against off-targets can play a significant role in inhibition of SARS_CoV2 viral entry and its propagation. This study includes network pharmacology, virtual screening, docking and molecular dynamics to investigate the distinct antiviral mechanisms of effective phytochemicals against SARS_CoV2. METHODS SARS_CoV2 human-protein interaction network was explored from the BioGRID database and analysed using Cytoscape. Further analysis was performed to explore biological function, protein-phytochemical/drugs network and up-down regulation of pathological host target proteins. This lead to understand the antiviral mechanism of phytochemicals against SARS_CoV2. The network was explored through g:Profiler, EnrichR, CTD, SwissTarget, STITCH, DrugBank, BindingDB, STRING and SuperPred. Virtual screening of phytochemicals against potential antiviral targets such as M-Pro, NSP1, Receptor binding domain, RNA binding domain, and ACE2 discloses the effective interaction between them. Further, the binding energy calculations through simulation of the docked complex explains the efficiency and stability of the interactions. RESULTS The network analysis identified quercetin, genistein, luteolin, eugenol, berberine, isorhamnetin and cinnamaldehyde to be interacting with host proteins ACE2, DPP4, COMT, TUBGCP3, CENPF, BRD2 and HMOX1 which are involved in antiviral mechanisms such as viral entry, viral replication, host immune response, and antioxidant activity. Thus indicating that herbal cocktails can effectively tackle the viral hijacking of the crucial biological functions of human host. Further exploration through Virtual screening, docking and molecular dynamics recognizes the effective interaction of phytochemicals such as punicalagin, scutellarin, and solamargine with their respective potential targets. CONCLUSION This work illustrates probable strategy for identification of phytochemical based cocktails and off-targets which are effective against SARS_CoV 2.
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Affiliation(s)
- Jayanth Jeevanandam
- Molecular Biophysics lab, School of Chemical and Biotechnology, SASTRA Deemed to- be University, Thanjavur-613401, Tamilnadu, India
| | - Esackimuthu Paramasivam
- Molecular Biophysics lab, School of Chemical and Biotechnology, SASTRA Deemed to- be University, Thanjavur-613401, Tamilnadu, India
| | | | - Srikanth Ragavendran
- TATA-Realty Data science lab, School of Humanity and Science, SASTRA Deemed to-be University, Thanjavur-613401, Tamilnadu, India
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Shang L, Zhang Y, Liu Y, Jin C, Yuan Y, Tian C, Ni M, Bo X, Zhang L, Li D, He F, Wang J. A Yeast BiFC-seq Method for Genome-wide Interactome Mapping. GENOMICS, PROTEOMICS & BIOINFORMATICS 2022; 20:795-807. [PMID: 34314873 PMCID: PMC9880813 DOI: 10.1016/j.gpb.2021.02.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2020] [Revised: 12/14/2020] [Accepted: 03/10/2021] [Indexed: 01/31/2023]
Abstract
Genome-wide physical protein-protein interaction (PPI) mapping remains a major challenge for current technologies. Here, we reported a high-efficiency BiFC-seq method, yeast-enhanced green fluorescent protein-based bimolecular fluorescence complementation (yEGFP-BiFC) coupled with next-generation DNA sequencing, for interactome mapping. We first applied yEGFP-BiFC method to systematically investigate an intraviral network of the Ebola virus. Two-thirds (9/14) of known interactions of EBOV were recaptured, and five novel interactions were discovered. Next, we used the BiFC-seq method to map the interactome of the tumor protein p53. We identified 97 interactors of p53, more than three-quarters of which were novel. Furthermore, in a more complex background, we screened potential interactors by pooling two BiFC libraries together and revealed a network of 229 interactions among 205 proteins. These results show that BiFC-seq is a highly sensitive, rapid, and economical method for genome-wide interactome mapping.
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Affiliation(s)
- Limin Shang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Yuehui Zhang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Yuchen Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Chaozhi Jin
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Yanzhi Yuan
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Chunyan Tian
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Ming Ni
- Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Xiaochen Bo
- Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Li Zhang
- Department of Rehabilitation Medicine, Nan Lou; Key Laboratory of Wound Repair and Regeneration of PLA, College of Life Sciences, Chinese PLA General Hospital, Beijing 100853, China
| | - Dong Li
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Fuchu He
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China.
| | - Jian Wang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China; School of Basic Medical Sciences, Anhui Medical University, Hefei 230032, China.
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7
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Pan X, Lin X, Cao D, Zeng X, Yu PS, He L, Nussinov R, Cheng F. Deep learning for drug repurposing: Methods, databases, and applications. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2022. [DOI: 10.1002/wcms.1597] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Xiaoqin Pan
- School of Computer Science and Engineering Hunan University Changsha Hunan China
| | - Xuan Lin
- School of Computer Science Xiangtan University Xiangtan China
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan China
| | - Dongsheng Cao
- Xiangya School of Pharmaceutical Sciences Central South University Changsha China
| | - Xiangxiang Zeng
- School of Computer Science and Engineering Hunan University Changsha Hunan China
| | - Philip S. Yu
- Department of Computer Science University of Illinois at Chicago Chicago Illinois USA
| | - Lifang He
- Department of Computer Science and Engineering Lehigh University Bethlehem Pennsylvania USA
| | - Ruth Nussinov
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research National Cancer Institute at Frederick Frederick Maryland USA
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine Tel Aviv University Tel Aviv Israel
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic Cleveland Ohio USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine Case Western Reserve University Cleveland Ohio USA
- Case Comprehensive Cancer Center Case Western Reserve University School of Medicine Cleveland Ohio USA
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Ahmed F, Lee JW, Samantasinghar A, Kim YS, Kim KH, Kang IS, Memon FH, Lim JH, Choi KH. SperoPredictor: An Integrated Machine Learning and Molecular Docking-Based Drug Repurposing Framework With Use Case of COVID-19. Front Public Health 2022; 10:902123. [PMID: 35784208 PMCID: PMC9244710 DOI: 10.3389/fpubh.2022.902123] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 05/02/2022] [Indexed: 12/13/2022] Open
Abstract
The global spread of the SARS coronavirus 2 (SARS-CoV-2), its manifestation in human hosts as a contagious disease, and its variants have induced a pandemic resulting in the deaths of over 6,000,000 people. Extensive efforts have been devoted to drug research to cure and refrain the spread of COVID-19, but only one drug has received FDA approval yet. Traditional drug discovery is inefficient, costly, and unable to react to pandemic threats. Drug repurposing represents an effective strategy for drug discovery and reduces the time and cost compared to de novo drug discovery. In this study, a generic drug repurposing framework (SperoPredictor) has been developed which systematically integrates the various types of drugs and disease data and takes the advantage of machine learning (Random Forest, Tree Ensemble, and Gradient Boosted Trees) to repurpose potential drug candidates against any disease of interest. Drug and disease data for FDA-approved drugs (n = 2,865), containing four drug features and three disease features, were collected from chemical and biological databases and integrated with the form of drug-disease association tables. The resulting dataset was split into 70% for training, 15% for testing, and the remaining 15% for validation. The testing and validation accuracies of the models were 99.3% for Random Forest and 99.03% for Tree Ensemble. In practice, SperoPredictor identified 25 potential drug candidates against 6 human host-target proteomes identified from a systematic review of journals. Literature-based validation indicated 12 of 25 predicted drugs (48%) have been already used for COVID-19 followed by molecular docking and re-docking which indicated 4 of 13 drugs (30%) as potential candidates against COVID-19 to be pre-clinically and clinically validated. Finally, SperoPredictor results illustrated the ability of the platform to be rapidly deployed to repurpose the drugs as a rapid response to emergent situations (like COVID-19 and other pandemics).
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Affiliation(s)
- Faheem Ahmed
- Department of Mechatronics Engineering, Jeju National University, Jeju, South Korea
| | - Jae Wook Lee
- Department of Mechatronics Engineering, Jeju National University, Jeju, South Korea
- BioSpero, Inc., Jeju, South Korea
| | | | | | - Kyung Hwan Kim
- Department of Mechatronics Engineering, Jeju National University, Jeju, South Korea
| | - In Suk Kang
- Department of Mechatronics Engineering, Jeju National University, Jeju, South Korea
| | - Fida Hussain Memon
- Department of Mechatronics Engineering, Jeju National University, Jeju, South Korea
| | - Jong Hwan Lim
- Department of Mechatronics Engineering, Jeju National University, Jeju, South Korea
| | - Kyung Hyun Choi
- Department of Mechatronics Engineering, Jeju National University, Jeju, South Korea
- BioSpero, Inc., Jeju, South Korea
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Zhou Y, Liu Y, Gupta S, Paramo MI, Hou Y, Mao C, Luo Y, Judd J, Wierbowski S, Bertolotti M, Nerkar M, Jehi L, Drayman N, Nicolaescu V, Gula H, Tay S, Randall G, Lis JT, Feschotte C, Erzurum SC, Cheng F, Yu H. A comprehensive SARS-CoV-2-human protein-protein interactome network identifies pathobiology and host-targeting therapies for COVID-19. RESEARCH SQUARE 2022:rs.3.rs-1354127. [PMID: 35677070 PMCID: PMC9176654 DOI: 10.21203/rs.3.rs-1354127/v2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Physical interactions between viral and host proteins are responsible for almost all aspects of the viral life cycle and the host's immune response. Studying viral-host protein-protein interactions is thus crucial for identifying strategies for treatment and prevention of viral infection. Here, we use high-throughput yeast two-hybrid and affinity purification followed by mass spectrometry to generate a comprehensive SARS-CoV-2-human protein-protein interactome network consisting of both binary and co-complex interactions. We report a total of 739 high-confidence interactions, showing the highest overlap of interaction partners among published datasets as well as the highest overlap with genes differentially expressed in samples (such as upper airway and bronchial epithelial cells) from patients with SARS-CoV-2 infection. Showcasing the utility of our network, we describe a novel interaction between the viral accessory protein ORF3a and the host zinc finger transcription factor ZNF579 to illustrate a SARS-CoV-2 factor mediating a direct impact on host transcription. Leveraging our interactome, we performed network-based drug screens for over 2,900 FDA-approved/investigational drugs and obtained a curated list of 23 drugs that had significant network proximities to SARS-CoV-2 host factors, one of which, carvedilol, showed promising antiviral properties. We performed electronic health record-based validation using two independent large-scale, longitudinal COVID-19 patient databases and found that carvedilol usage was associated with a significantly lowered probability (17%-20%, P < 0.001) of obtaining a SARS-CoV-2 positive test after adjusting various confounding factors. Carvedilol additionally showed anti-viral activity against SARS-CoV-2 in a human lung epithelial cell line [half maximal effective concentration (EC 50 ) value of 4.1 µM], suggesting a mechanism for its beneficial effect in COVID-19. Our study demonstrates the value of large-scale network systems biology approaches for extracting biological insight from complex biological processes.
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Affiliation(s)
- Yadi Zhou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, US
| | - Yuan Liu
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, US
| | - Shagun Gupta
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, US
- Department of Computational Biology, Cornell University, Ithaca, NY 14853, US
| | - Mauricio I. Paramo
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, US
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853, US
| | - Yuan Hou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, US
| | - Chengsheng Mao
- Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University, Chicago, IL 60611, US
| | - Yuan Luo
- Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University, Chicago, IL 60611, US
| | - Julius Judd
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853, US
| | - Shayne Wierbowski
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, US
- Department of Computational Biology, Cornell University, Ithaca, NY 14853, US
| | - Marta Bertolotti
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, US
| | - Mriganka Nerkar
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853, US
| | - Lara Jehi
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, US
| | - Nir Drayman
- Pritzker School of Molecular Engineering, The University of Chicago, Chicago, IL 60637, US
| | - Vlad Nicolaescu
- Department of Microbiology, Ricketts Laboratory, University of Chicago, Chicago, IL 60637, US
| | - Haley Gula
- Department of Microbiology, Ricketts Laboratory, University of Chicago, Chicago, IL 60637, US
| | - Savaş Tay
- Pritzker School of Molecular Engineering, The University of Chicago, Chicago, IL 60637, US
| | - Glenn Randall
- Department of Microbiology, Ricketts Laboratory, University of Chicago, Chicago, IL 60637, US
| | - John T. Lis
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853, US
| | - Cédric Feschotte
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853, US
| | - Serpil C. Erzurum
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, US
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, US
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, US
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, US
| | - Haiyuan Yu
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, US
- Department of Computational Biology, Cornell University, Ithaca, NY 14853, US
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10
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Ye Q, Hsieh CY, Yang Z, Kang Y, Chen J, Cao D, He S, Hou T. A unified drug-target interaction prediction framework based on knowledge graph and recommendation system. Nat Commun 2021; 12:6775. [PMID: 34811351 PMCID: PMC8635420 DOI: 10.1038/s41467-021-27137-3] [Citation(s) in RCA: 68] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 11/05/2021] [Indexed: 02/06/2023] Open
Abstract
Prediction of drug-target interactions (DTI) plays a vital role in drug development in various areas, such as virtual screening, drug repurposing and identification of potential drug side effects. Despite extensive efforts have been invested in perfecting DTI prediction, existing methods still suffer from the high sparsity of DTI datasets and the cold start problem. Here, we develop KGE_NFM, a unified framework for DTI prediction by combining knowledge graph (KG) and recommendation system. This framework firstly learns a low-dimensional representation for various entities in the KG, and then integrates the multimodal information via neural factorization machine (NFM). KGE_NFM is evaluated under three realistic scenarios, and achieves accurate and robust predictions on four benchmark datasets, especially in the scenario of the cold start for proteins. Our results indicate that KGE_NFM provides valuable insight to integrate KG and recommendation system-based techniques into a unified framework for novel DTI discovery.
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Affiliation(s)
- Qing Ye
- grid.13402.340000 0004 1759 700XInnovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058 Zhejiang China ,grid.13402.340000 0004 1759 700XCollege of Control Science and Engineering, Zhejiang University, Hangzhou, 310027 Zhejiang China ,grid.13402.340000 0004 1759 700XState Key Lab of CAD&CG, Zhejiang University, Hangzhou, Zhejiang 310058 China
| | - Chang-Yu Hsieh
- Tencent Quantum Laboratory, Shenzhen, 518057 Guangdong China
| | - Ziyi Yang
- Tencent Quantum Laboratory, Shenzhen, 518057 Guangdong China
| | - Yu Kang
- grid.13402.340000 0004 1759 700XInnovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058 Zhejiang China
| | - Jiming Chen
- grid.13402.340000 0004 1759 700XCollege of Control Science and Engineering, Zhejiang University, Hangzhou, 310027 Zhejiang China
| | - Dongsheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410013, Hunan, China.
| | - Shibo He
- College of Control Science and Engineering, Zhejiang University, Hangzhou, 310027, Zhejiang, China.
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, China. .,State Key Lab of CAD&CG, Zhejiang University, Hangzhou, Zhejiang, 310058, China.
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11
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Kumar K, Bose S, Chakrabarti S. Identification of Cross-Pathway Connections via Protein-Protein Interactions Linked to Altered States of Metabolic Enzymes in Cervical Cancer. Front Med (Lausanne) 2021; 8:736495. [PMID: 34790674 PMCID: PMC8591138 DOI: 10.3389/fmed.2021.736495] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Accepted: 09/29/2021] [Indexed: 01/08/2023] Open
Abstract
Metabolic reprogramming is one of the emerging hallmarks of cancer cells. Various factors, such as signaling proteins (S), miRNA, and transcription factors (TFs), may play important roles in altering the metabolic status in cancer cells by interacting with metabolic enzymes either directly or via protein-protein interactions (PPIs). Therefore, it is important to understand the coordination among these cellular pathways, which may provide better insight into the molecular mechanism behind metabolic adaptations in cancer cells. In this study, we have designed a cervical cancer-specific supra-interaction network where signaling pathway proteins, TFs, and microRNAs (miRs) are connected to metabolic enzymes via PPIs to investigate novel molecular targets and connections/links/paths regulating the metabolic enzymes. Using publicly available omics data and PPIs, we have developed a Hidden Markov Model (HMM)-based mathematical model yielding 94, 236, and 27 probable links/paths connecting signaling pathway proteins, TFs, and miRNAs to metabolic enzymes, respectively, out of which 83 paths connect to six common metabolic enzymes (RRM2, NDUFA11, ENO2, EZH2, AKR1C2, and TYMS). Signaling proteins (e.g., PPARD, BAD, GNB5, CHECK1, PAK2, PLK1, BRCA1, MAML3, and SPP1), TFs (e.g., KAT2B, ING1, MED1, ZEB1, AR, NCOA2, EGR1, TWIST1, E2F1, ID4, RBL1, ESR1, and HSF2), and miR (e.g., mir-147a, mir-593-5p, mir-138-5p, mir-16-5p, and mir-15b-5p) were found to regulate two key metabolic enzymes, EZH2 and AKR1C2, with altered metabolites (L-lysine and tetrahydrodeoxycorticosterone, THDOC) status in cervical cancer. We believe, the biology-based approach of our system will pave the way for future studies, which could be aimed toward identifying novel signaling, transcriptional, and post-transcriptional regulators of metabolic alterations in cervical cancer.
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Affiliation(s)
- Krishna Kumar
- Structural Biology and Bioinformatics Division, Council of Scientific & Industrial Research (CSIR)-Indian Institute of Chemical Biology, Kolkata, India
| | - Sarpita Bose
- Structural Biology and Bioinformatics Division, Council of Scientific & Industrial Research (CSIR)-Indian Institute of Chemical Biology, Kolkata, India
| | - Saikat Chakrabarti
- Structural Biology and Bioinformatics Division, Council of Scientific & Industrial Research (CSIR)-Indian Institute of Chemical Biology, Kolkata, India
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12
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Cava C, Bertoli G, Castiglioni I. Potential drugs against COVID-19 revealed by gene expression profile, molecular docking and molecular dynamic simulation. Future Virol 2021; 16:10.2217/fvl-2020-0392. [PMID: 34306168 PMCID: PMC8293696 DOI: 10.2217/fvl-2020-0392] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 06/30/2021] [Indexed: 02/07/2023]
Abstract
Aim: SARS-CoV-2, an emerging betacoronavirus, is the causative agent of COVID-19. Currently, there are few specific and selective antiviral drugs for the treatment and vaccines to prevent contagion. However, their long-term effects can be revealed after several years, and new drugs for COVID-19 should continue to be investigated. Materials & methods: In the first step of our study we identified, through a gene expression analysis, several drugs that could act on the biological pathways altered in COVID-19. In the second step, we performed a docking simulation to test the properties of the identified drugs to target SARS-CoV-2. Results: The drugs that showed a higher binding affinity are bardoxolone (-8.78 kcal/mol), irinotecan (-8.40 kcal/mol) and pyrotinib (-8.40 kcal/mol). Conclusion: We suggested some drugs that could be efficient in treating COVID-19.
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Affiliation(s)
- Claudia Cava
- Institute of Molecular Bioimaging & Physiology, National Research Council (IBFM-CNR), Via F. Cervi 93, Segrate-Milan, Milan, 20090, Italy
| | - Gloria Bertoli
- Institute of Molecular Bioimaging & Physiology, National Research Council (IBFM-CNR), Via F. Cervi 93, Segrate-Milan, Milan, 20090, Italy
| | - Isabella Castiglioni
- Department of Physics “Giuseppe Occhialini”, University of Milan-Bicocca Piazza dell'Ateneo Nuovo, Milan, 20126, Italy
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13
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Yadav M, Dhagat S, Eswari JS. Emerging strategies on in silico drug development against COVID-19: challenges and opportunities. Eur J Pharm Sci 2020; 155:105522. [PMID: 32827661 PMCID: PMC7438372 DOI: 10.1016/j.ejps.2020.105522] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 08/14/2020] [Accepted: 08/18/2020] [Indexed: 12/22/2022]
Abstract
The importance of coronaviruses as human pathogen has been highlighted by the recent outbreak of SARS-CoV-2 leading to the search of suitable drugs to overcome respiratory infections caused by the virus. Due to the lack of specific drugs against coronavirus, the existing antiviral and antimalarial drugs are currently being administered to the patients infected with SARS-CoV-2. The scientists are also considering repurposing of some of the existing drugs as a suitable option in search of effective drugs against coronavirus till the establishment of a potent drug and/or vaccine. Computer-aided drug discovery provides a promising attempt to enable scientists to develop new and target specific drugs to combat any disease. The discovery of novel targets for COVID-19 using computer-aided drug discovery tools requires knowledge of the structure of coronavirus and various target proteins present in the virus. Targeting viral proteins will make the drug specific against the virus, thereby, increasing the chances of viral mortality. Hence, this review provides the structure of SARS-CoV-2 virus along with the important viral components involved in causing infection. It also focuses on the role of various target proteins in disease, the mechanism by which currently administered drugs act against the virus and the repurposing of few drugs. The gap arising from the absence of specific drugs is addressed by proposing potential antiviral drug targets which might provide insights into structure-based drug development against SARS-CoV-2.
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Affiliation(s)
- Manisha Yadav
- Department of Biotechnology, National Institute of Technology Raipur, C.G., 492010, India
| | - Swasti Dhagat
- Department of Biotechnology, National Institute of Technology Raipur, C.G., 492010, India
| | - J Satya Eswari
- Department of Biotechnology, National Institute of Technology Raipur, C.G., 492010, India.
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14
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Abstract
Drug repurposing or repositioning is a technique whereby existing drugs are used to treat emerging and challenging diseases, including COVID-19. Drug repurposing has become a promising approach because of the opportunity for reduced development timelines and overall costs. In the big data era, artificial intelligence (AI) and network medicine offer cutting-edge application of information science to defining disease, medicine, therapeutics, and identifying targets with the least error. In this Review, we introduce guidelines on how to use AI for accelerating drug repurposing or repositioning, for which AI approaches are not just formidable but are also necessary. We discuss how to use AI models in precision medicine, and as an example, how AI models can accelerate COVID-19 drug repurposing. Rapidly developing, powerful, and innovative AI and network medicine technologies can expedite therapeutic development. This Review provides a strong rationale for using AI-based assistive tools for drug repurposing medications for human disease, including during the COVID-19 pandemic.
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Affiliation(s)
- Yadi Zhou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medical College, Cornell University, New York, NY, USA
| | - Jian Tang
- Mila-Quebec Institute for Learning Algorithms and CIFAR AI Research Chair, HEC Montreal, Montréal, QC, Canada
| | - Ruth Nussinov
- Cancer and Inflammation Program, Leidos Biomedical Research, Frederick National Laboratory for Cancer Research, National Cancer Institute at Frederick, Frederick, MD, USA
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, and Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, OH, USA
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15
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Martin WR, Cheng F. Repurposing of FDA-Approved Toremifene to Treat COVID-19 by Blocking the Spike Glycoprotein and NSP14 of SARS-CoV-2. J Proteome Res 2020; 19:4670-4677. [PMID: 32907334 PMCID: PMC7640961 DOI: 10.1021/acs.jproteome.0c00397] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
The global pandemic of Coronavirus Disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to the death of more than 675,000 worldwide and over 150,000 in the United States alone. However, there are currently no approved effective pharmacotherapies for COVID-19. Here, we combine homology modeling, molecular docking, molecular dynamics simulation, and binding affinity calculations to determine potential targets for toremifene, a selective estrogen receptor modulator which we have previously identified as a SARS-CoV-2 inhibitor. Our results indicate the possibility of inhibition of the spike glycoprotein by toremifene, responsible for aiding in fusion of the viral membrane with the cell membrane, via a perturbation to the fusion core. An interaction between the dimethylamine end of toremifene and residues Q954 and N955 in heptad repeat 1 (HR1) perturbs the structure, causing a shift from what is normally a long, helical region to short helices connected by unstructured regions. Additionally, we found a strong interaction between toremifene and the methyltransferase nonstructural protein (NSP) 14, which could be inhibitory to viral replication via its active site. These results suggest potential structural mechanisms for toremifene by blocking the spike protein and NSP14 of SARS-CoV-2, offering a drug candidate for COVID-19.
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Affiliation(s)
- William R. Martin
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
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16
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Zeng X, Song X, Ma T, Pan X, Zhou Y, Hou Y, Zhang Z, Li K, Karypis G, Cheng F. Repurpose Open Data to Discover Therapeutics for COVID-19 Using Deep Learning. J Proteome Res 2020; 19:4624-4636. [PMID: 32654489 PMCID: PMC7384389 DOI: 10.1021/acs.jproteome.0c00316] [Citation(s) in RCA: 131] [Impact Index Per Article: 32.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Indexed: 02/08/2023]
Abstract
There have been more than 2.2 million confirmed cases and over 120 000 deaths from the human coronavirus disease 2019 (COVID-19) pandemic, caused by the novel severe acute respiratory syndrome coronavirus (SARS-CoV-2), in the United States alone. However, there is currently a lack of proven effective medications against COVID-19. Drug repurposing offers a promising route for the development of prevention and treatment strategies for COVID-19. This study reports an integrative, network-based deep-learning methodology to identify repurposable drugs for COVID-19 (termed CoV-KGE). Specifically, we built a comprehensive knowledge graph that includes 15 million edges across 39 types of relationships connecting drugs, diseases, proteins/genes, pathways, and expression from a large scientific corpus of 24 million PubMed publications. Using Amazon's AWS computing resources and a network-based, deep-learning framework, we identified 41 repurposable drugs (including dexamethasone, indomethacin, niclosamide, and toremifene) whose therapeutic associations with COVID-19 were validated by transcriptomic and proteomics data in SARS-CoV-2-infected human cells and data from ongoing clinical trials. Whereas this study by no means recommends specific drugs, it demonstrates a powerful deep-learning methodology to prioritize existing drugs for further investigation, which holds the potential to accelerate therapeutic development for COVID-19.
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Affiliation(s)
- Xiangxiang Zeng
- School of Computer Science and
Engineering, Hunan University, Changsha
410012, China
| | - Xiang Song
- AWS Shanghai AI
Lab, Shanghai 200335,
China
| | - Tengfei Ma
- School of Computer Science and
Engineering, Hunan University, Changsha
410012, China
| | - Xiaoqin Pan
- School of Computer Science and
Engineering, Hunan University, Changsha
410012, China
| | - Yadi Zhou
- Genomic Medicine Institute, Lerner
Research Institute, Cleveland Clinic,
Cleveland, Ohio 44106, United States
| | - Yuan Hou
- Genomic Medicine Institute, Lerner
Research Institute, Cleveland Clinic,
Cleveland, Ohio 44106, United States
| | - Zheng Zhang
- AWS Shanghai AI
Lab, Shanghai 200335,
China
- New York University
Shanghai, Shanghai 200122,
China
| | - Kenli Li
- School of Computer Science and
Engineering, Hunan University, Changsha
410012, China
| | - George Karypis
- AWS AI,
East Palo Alto, California 94303, United
States
- Department of Computer Science and
Engineering, University of Minnesota, 200
Union Street SE, Minneapolis, Minnesota 55455, United
States
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner
Research Institute, Cleveland Clinic,
Cleveland, Ohio 44106, United States
- Department of Molecular Medicine,
Cleveland Clinic Lerner College of Medicine, Case
Western Reserve University, Cleveland, Ohio 44195,
United States
- Case Comprehensive Cancer Center,
Case Western Reserve University School of
Medicine, Cleveland, Ohio 44106, United
States
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17
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Zhou Y, Hou Y, Shen J, Mehra R, Kallianpur A, Culver DA, Gack MU, Farha S, Zein J, Comhair S, Fiocchi C, Stappenbeck T, Chan T, Eng C, Jung JU, Jehi L, Erzurum S, Cheng F. A network medicine approach to investigation and population-based validation of disease manifestations and drug repurposing for COVID-19. PLoS Biol 2020; 18:e3000970. [PMID: 33156843 PMCID: PMC7728249 DOI: 10.1371/journal.pbio.3000970] [Citation(s) in RCA: 113] [Impact Index Per Article: 28.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 12/10/2020] [Accepted: 10/28/2020] [Indexed: 01/08/2023] Open
Abstract
The global coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to unprecedented social and economic consequences. The risk of morbidity and mortality due to COVID-19 increases dramatically in the presence of coexisting medical conditions, while the underlying mechanisms remain unclear. Furthermore, there are no approved therapies for COVID-19. This study aims to identify SARS-CoV-2 pathogenesis, disease manifestations, and COVID-19 therapies using network medicine methodologies along with clinical and multi-omics observations. We incorporate SARS-CoV-2 virus-host protein-protein interactions, transcriptomics, and proteomics into the human interactome. Network proximity measurement revealed underlying pathogenesis for broad COVID-19-associated disease manifestations. Analyses of single-cell RNA sequencing data show that co-expression of ACE2 and TMPRSS2 is elevated in absorptive enterocytes from the inflamed ileal tissues of Crohn disease patients compared to uninflamed tissues, revealing shared pathobiology between COVID-19 and inflammatory bowel disease. Integrative analyses of metabolomics and transcriptomics (bulk and single-cell) data from asthma patients indicate that COVID-19 shares an intermediate inflammatory molecular profile with asthma (including IRAK3 and ADRB2). To prioritize potential treatments, we combined network-based prediction and a propensity score (PS) matching observational study of 26,779 individuals from a COVID-19 registry. We identified that melatonin usage (odds ratio [OR] = 0.72, 95% CI 0.56-0.91) is significantly associated with a 28% reduced likelihood of a positive laboratory test result for SARS-CoV-2 confirmed by reverse transcription-polymerase chain reaction assay. Using a PS matching user active comparator design, we determined that melatonin usage was associated with a reduced likelihood of SARS-CoV-2 positive test result compared to use of angiotensin II receptor blockers (OR = 0.70, 95% CI 0.54-0.92) or angiotensin-converting enzyme inhibitors (OR = 0.69, 95% CI 0.52-0.90). Importantly, melatonin usage (OR = 0.48, 95% CI 0.31-0.75) is associated with a 52% reduced likelihood of a positive laboratory test result for SARS-CoV-2 in African Americans after adjusting for age, sex, race, smoking history, and various disease comorbidities using PS matching. In summary, this study presents an integrative network medicine platform for predicting disease manifestations associated with COVID-19 and identifying melatonin for potential prevention and treatment of COVID-19.
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Affiliation(s)
- Yadi Zhou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
| | - Yuan Hou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
| | - Jiayu Shen
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
| | - Reena Mehra
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio, United States of America
- Neurological Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
| | - Asha Kallianpur
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio, United States of America
| | - Daniel A. Culver
- Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
- Department of Pulmonary Medicine, Respiratory Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
| | - Michaela U. Gack
- Florida Research and Innovation Center, Cleveland Clinic, Port Saint Lucie, Florida, United States of America
| | - Samar Farha
- Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
- Department of Pulmonary Medicine, Respiratory Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
| | - Joe Zein
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio, United States of America
- Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
| | - Suzy Comhair
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio, United States of America
- Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
| | - Claudio Fiocchi
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio, United States of America
- Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
| | - Thaddeus Stappenbeck
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio, United States of America
- Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
| | - Timothy Chan
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio, United States of America
- Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
| | - Charis Eng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio, United States of America
- Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
- Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America
| | - Jae U. Jung
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio, United States of America
- Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
| | - Lara Jehi
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio, United States of America
- Neurological Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
| | - Serpil Erzurum
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio, United States of America
- Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio, United States of America
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America
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18
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Somadi G, Sivan SK. Identification of therapeutic target in S2 domain of SARS nCov-2 Spike glycoprotein: Key to design and discover drug candidates for inhibition of viral entry into host cell. JOURNAL OF THEORETICAL & COMPUTATIONAL CHEMISTRY 2020. [DOI: 10.1142/s0219633620500285] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Humanity is facing a grieve danger of coronavirus disease-19 caused by severe acute respiratory syndrome novel coronavirus-2 (SARS nCov-2). There is an urgent need of therapeutics that can help in overcoming this global pandemic. Identifying novel therapeutic target and screening already approved drug is a faster approach in this situation. Spike glycoprotein (Sgp) of SARS nCoV-2 is potentials target where in researchers have targeted receptor binding domain (RBD) of S1 domain. The S2 domain of Sgp also plays a pivotal role in viral entry, but the mechanism is less understood. We analyzed the structure of Sgp S2 domain in pre-fusion state and Heptad repeat region in its post-fusion state available from protein data bank. Sgp shows three major regions in S2 domain, the fusion peptide (FP), heptad repeat 1 (HR1) and central helical (CH) region. The HR1 region undergoes structural changes by flipping approximately 180∘ and coil up to form a rod like structure during fusion process implying its role in viral entry into the host cell. This structural change in S2 domain helices is crucial step, if this process is hindered by targeting the HR1 and CH region then the progression of virus can be stopped. Possible binding cavity was identified near the HR1 and CH region in S2 domain and docking-based virtual screening of FDA approved drugs was performed. Promising candidates like Troxerutin, Thymopentin and Daclatasvir can be used as therapeutics provided an immediate in-vitro and clinical studies are carried out by research groups.
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Affiliation(s)
- Gururaj Somadi
- Department of Chemistry, Nizam College, Osmania University, Hyderabad 500001, India
| | - Sree Kanth Sivan
- Department of Chemistry, Nizam College, Osmania University, Hyderabad 500001, India
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19
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Dhillon BK, Smith M, Baghela A, Lee AHY, Hancock REW. Systems Biology Approaches to Understanding the Human Immune System. Front Immunol 2020; 11:1683. [PMID: 32849587 PMCID: PMC7406790 DOI: 10.3389/fimmu.2020.01683] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Accepted: 06/24/2020] [Indexed: 12/18/2022] Open
Abstract
Systems biology is an approach to interrogate complex biological systems through large-scale quantification of numerous biomolecules. The immune system involves >1,500 genes/proteins in many interconnected pathways and processes, and a systems-level approach is critical in broadening our understanding of the immune response to vaccination. Changes in molecular pathways can be detected using high-throughput omics datasets (e.g., transcriptomics, proteomics, and metabolomics) by using methods such as pathway enrichment, network analysis, machine learning, etc. Importantly, integration of multiple omic datasets is becoming key to revealing novel biological insights. In this perspective article, we highlight the use of protein-protein interaction (PPI) networks as a multi-omics integration approach to unravel information flow and mechanisms during complex biological events, with a focus on the immune system. This involves a combination of tools, including: InnateDB, a database of curated interactions between genes and protein products involved in the innate immunity; NetworkAnalyst, a visualization and analysis platform for InnateDB interactions; and MetaBridge, a tool to integrate metabolite data into PPI networks. The application of these systems techniques is demonstrated for a variety of biological questions, including: the developmental trajectory of neonates during the first week of life, mechanisms in host-pathogen interaction, disease prognosis, biomarker discovery, and drug discovery and repurposing. Overall, systems biology analyses of omics data have been applied to a variety of immunology-related questions, and here we demonstrate the numerous ways in which PPI network analysis can be a powerful tool in contributing to our understanding of the immune system and the study of vaccines.
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Affiliation(s)
- Bhavjinder K. Dhillon
- Centre for Microbial Diseases and Immunity Research, University of British Columbia, Vancouver, BC, Canada
| | - Maren Smith
- Centre for Microbial Diseases and Immunity Research, University of British Columbia, Vancouver, BC, Canada
| | - Arjun Baghela
- Centre for Microbial Diseases and Immunity Research, University of British Columbia, Vancouver, BC, Canada
| | - Amy H. Y. Lee
- Centre for Microbial Diseases and Immunity Research, University of British Columbia, Vancouver, BC, Canada
- Molecular Biology & Biochemistry Department, Simon Fraser University, Burnaby, BC, Canada
| | - Robert E. W. Hancock
- Centre for Microbial Diseases and Immunity Research, University of British Columbia, Vancouver, BC, Canada
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20
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Zhou Y, Hou Y, Shen J, Kallianpur A, Zein J, Culver DA, Farha S, Comhair S, Fiocchi C, Gack MU, Mehra R, Stappenbeck T, Chan T, Eng C, Jung JU, Jehi L, Erzurum S, Cheng F. A Network Medicine Approach to Investigation and Population-based Validation of Disease Manifestations and Drug Repurposing for COVID-19. CHEMRXIV : THE PREPRINT SERVER FOR CHEMISTRY 2020:12579137. [PMID: 32676577 PMCID: PMC7350981 DOI: 10.26434/chemrxiv.12579137] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/12/2023]
Abstract
The global Coronavirus Disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to unprecedented social and economic consequences. The risk of morbidity and mortality due to COVID-19 increases dramatically in the presence of co-existing medical conditions while the underlying mechanisms remain unclear. Furthermore, there are no proven effective therapies for COVID-19. This study aims to identify SARS-CoV-2 pathogenesis, diseases manifestations, and COVID-19 therapies using network medicine methodologies along with clinical and multi-omics observations. We incorporate SARS-CoV-2 virus-host protein-protein interactions, transcriptomics, and proteomics into the human interactome. Network proximity measure revealed underlying pathogenesis for broad COVID-19-associated manifestations. Multi-modal analyses of single-cell RNA-sequencing data showed that co-expression of ACE2 and TMPRSS2 was elevated in absorptive enterocytes from the inflamed ileal tissues of Crohn's disease patients compared to uninflamed tissues, revealing shared pathobiology by COVID-19 and inflammatory bowel disease. Integrative analyses of metabolomics and transcriptomics (bulk and single-cell) data from asthma patients indicated that COVID-19 shared intermediate inflammatory endophenotypes with asthma (including IRAK3 and ADRB2). To prioritize potential treatment, we combined network-based prediction and propensity score (PS) matching observational study of 18,118 patients from a COVID-19 registry. We identified that melatonin (odds ratio (OR) = 0.36, 95% confidence interval (CI) 0.22-0.59) was associated with 64% reduced likelihood of a positive laboratory test result for SARS-CoV-2. Using PS-matching user active comparator design, melatonin was associated with 54% reduced likelihood of SARS-CoV-2 positive test result compared to angiotensin II receptor blockers or angiotensin-converting enzyme inhibitors (OR = 0.46, 95% CI 0.24-0.86).
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Affiliation(s)
- Yadi Zhou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Yuan Hou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Jiayu Shen
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Asha Kallianpur
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
| | - Joe Zein
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Daniel A. Culver
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Pulmonary Medicine, Respiratory Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Samar Farha
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Pulmonary Medicine, Respiratory Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Suzy Comhair
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Claudio Fiocchi
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Michaela U. Gack
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Reena Mehra
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Thaddeus Stappenbeck
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Timothy Chan
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Charis Eng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
| | - Jae U. Jung
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Lara Jehi
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Serpil Erzurum
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
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21
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Mangione W, Falls Z, Melendy T, Chopra G, Samudrala R. Shotgun drug repurposing biotechnology to tackle epidemics and pandemics. Drug Discov Today 2020; 25:1126-1128. [PMID: 32405249 PMCID: PMC7217781 DOI: 10.1016/j.drudis.2020.05.002] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 05/03/2020] [Accepted: 05/05/2020] [Indexed: 12/14/2022]
Affiliation(s)
- William Mangione
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, 14203, United States
| | - Zackary Falls
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, 14203, United States
| | - Thomas Melendy
- Department of Microbiology and Immunology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, 14203, United States
| | - Gaurav Chopra
- Department of Chemistry, Purdue Institute for Drug Discovery, Integrated Data Science Institute, Purdue University, West Lafayette, IN, 47907, United States.
| | - Ram Samudrala
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, 14203, United States.
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22
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Mangione W, Falls Z, Melendy T, Chopra G, Samudrala R. Shotgun drug repurposing biotechnology to tackle epidemics and pandemics. CHEMRXIV : THE PREPRINT SERVER FOR CHEMISTRY 2020:10.26434/chemrxiv.12045318.v2. [PMID: 32511286 PMCID: PMC7252447 DOI: 10.26434/chemrxiv.12045318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
In this manuscript we highlight consensus between the list of drugs currently in clinical trials to treat COVID-19, the worldwide pandemic caused by severe acute respiratory coronavirus 2 (SARS-CoV-2), and the list of predictions made using our shotgun drug discovery, repurposing, and design platform known as CANDO (Computational Analysis of Novel Drug Opportunities). We make the argument that increased funding and development for drug repurposing biotechnology like ours will help combat the inevitable pathogenic outbreaks of the future. <br>
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Affiliation(s)
- William Mangione
- Department of Biomedical Informatics, University at Buffalo, Buffalo, NY, 14120, United States
| | - Zackary Falls
- Department of Biomedical Informatics, University at Buffalo, Buffalo, NY, 14120, United States
| | - Thomas Melendy
- Department of Microbiology and Immunology, University at Buffalo, Buffalo, NY, 14120, United States
| | - Gaurav Chopra
- Department of Chemistry, Purdue Institute for Drug Discovery, Integrated Data Science Institute, Purdue University, West Lafayette, IN, 47907, United States
| | - Ram Samudrala
- Department of Biomedical Informatics, University at Buffalo, Buffalo, NY, 14120, United States
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23
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Cava C, Bertoli G, Castiglioni I. In Silico Discovery of Candidate Drugs against Covid-19. Viruses 2020; 12:E404. [PMID: 32268515 PMCID: PMC7232366 DOI: 10.3390/v12040404] [Citation(s) in RCA: 91] [Impact Index Per Article: 22.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Revised: 04/01/2020] [Accepted: 04/04/2020] [Indexed: 12/13/2022] Open
Abstract
Previous studies reported that Angiotensin converting enzyme 2 (ACE2) is the main cell receptor of SARS-CoV and SARS-CoV-2. It plays a key role in the access of the virus into the cell to produce the final infection. In the present study we investigated in silico the basic mechanism of ACE2 in the lung and provided evidences for new potentially effective drugs for Covid-19. Specifically, we used the gene expression profiles from public datasets including The Cancer Genome Atlas, Gene Expression Omnibus and Genotype-Tissue Expression, Gene Ontology and pathway enrichment analysis to investigate the main functions of ACE2-correlated genes. We constructed a protein-protein interaction network containing the genes co-expressed with ACE2. Finally, we focused on the genes in the network that are already associated with known drugs and evaluated their role for a potential treatment of Covid-19. Our results demonstrate that the genes correlated with ACE2 are mainly enriched in the sterol biosynthetic process, Aryldialkylphosphatase activity, adenosylhomocysteinase activity, trialkylsulfonium hydrolase activity, acetate-CoA and CoA ligase activity. We identified a network of 193 genes, 222 interactions and 36 potential drugs that could have a crucial role. Among possible interesting drugs for Covid-19 treatment, we found Nimesulide, Fluticasone Propionate, Thiabendazole, Photofrin, Didanosine and Flutamide.
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Affiliation(s)
- Claudia Cava
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Via F.Cervi 93, 20090 Segrate-Milan, Milan, Italy
| | - Gloria Bertoli
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Via F.Cervi 93, 20090 Segrate-Milan, Milan, Italy
| | - Isabella Castiglioni
- Department of Physics “Giuseppe Occhialini”, University of Milan-Bicocca Piazza dell’Ateneo Nuovo, 1 - 20126, Milan, Italy;
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24
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Zhou Y, Hou Y, Shen J, Huang Y, Martin W, Cheng F. Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2. Cell Discov 2020; 6:14. [PMID: 32194980 PMCID: PMC7073332 DOI: 10.1038/s41421-020-0153-3] [Citation(s) in RCA: 989] [Impact Index Per Article: 247.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Accepted: 03/02/2020] [Indexed: 02/07/2023] Open
Abstract
Human coronaviruses (HCoVs), including severe acute respiratory syndrome coronavirus (SARS-CoV) and 2019 novel coronavirus (2019-nCoV, also known as SARS-CoV-2), lead global epidemics with high morbidity and mortality. However, there are currently no effective drugs targeting 2019-nCoV/SARS-CoV-2. Drug repurposing, representing as an effective drug discovery strategy from existing drugs, could shorten the time and reduce the cost compared to de novo drug discovery. In this study, we present an integrative, antiviral drug repurposing methodology implementing a systems pharmacology-based network medicine platform, quantifying the interplay between the HCoV-host interactome and drug targets in the human protein-protein interaction network. Phylogenetic analyses of 15 HCoV whole genomes reveal that 2019-nCoV/SARS-CoV-2 shares the highest nucleotide sequence identity with SARS-CoV (79.7%). Specifically, the envelope and nucleocapsid proteins of 2019-nCoV/SARS-CoV-2 are two evolutionarily conserved regions, having the sequence identities of 96% and 89.6%, respectively, compared to SARS-CoV. Using network proximity analyses of drug targets and HCoV-host interactions in the human interactome, we prioritize 16 potential anti-HCoV repurposable drugs (e.g., melatonin, mercaptopurine, and sirolimus) that are further validated by enrichment analyses of drug-gene signatures and HCoV-induced transcriptomics data in human cell lines. We further identify three potential drug combinations (e.g., sirolimus plus dactinomycin, mercaptopurine plus melatonin, and toremifene plus emodin) captured by the "Complementary Exposure" pattern: the targets of the drugs both hit the HCoV-host subnetwork, but target separate neighborhoods in the human interactome network. In summary, this study offers powerful network-based methodologies for rapid identification of candidate repurposable drugs and potential drug combinations targeting 2019-nCoV/SARS-CoV-2.
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Affiliation(s)
- Yadi Zhou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195 USA
| | - Yuan Hou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195 USA
| | - Jiayu Shen
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195 USA
| | - Yin Huang
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195 USA
| | - William Martin
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195 USA
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195 USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195 USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106 USA
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25
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Cheng F, Lu W, Liu C, Fang J, Hou Y, Handy DE, Wang R, Zhao Y, Yang Y, Huang J, Hill DE, Vidal M, Eng C, Loscalzo J. A genome-wide positioning systems network algorithm for in silico drug repurposing. Nat Commun 2019; 10:3476. [PMID: 31375661 PMCID: PMC6677722 DOI: 10.1038/s41467-019-10744-6] [Citation(s) in RCA: 120] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Accepted: 05/26/2019] [Indexed: 01/28/2023] Open
Abstract
Recent advances in DNA/RNA sequencing have made it possible to identify new targets rapidly and to repurpose approved drugs for treating heterogeneous diseases by the 'precise' targeting of individualized disease modules. In this study, we develop a Genome-wide Positioning Systems network (GPSnet) algorithm for drug repurposing by specifically targeting disease modules derived from individual patient's DNA and RNA sequencing profiles mapped to the human protein-protein interactome network. We investigate whole-exome sequencing and transcriptome profiles from ~5,000 patients across 15 cancer types from The Cancer Genome Atlas. We show that GPSnet-predicted disease modules can predict drug responses and prioritize new indications for 140 approved drugs. Importantly, we experimentally validate that an approved cardiac arrhythmia and heart failure drug, ouabain, shows potential antitumor activities in lung adenocarcinoma by uniquely targeting a HIF1α/LEO1-mediated cell metabolism pathway. In summary, GPSnet offers a network-based, in silico drug repurposing framework for more efficacious therapeutic selections.
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Affiliation(s)
- Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, 44195, USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA
| | - Weiqiang Lu
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China
| | - Chuang Liu
- Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, 311121, Hangzhou, China
| | - Jiansong Fang
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
| | - Yuan Hou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
| | - Diane E Handy
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Ruisheng Wang
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Yuzheng Zhao
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 200237, Shanghai, China
- Synthetic Biology and Biotechnology Laboratory, State Key Laboratory of Bioreactor Engineering, Shanghai Collaborative Innovation Center for Biomanufacturing Technology, 200237, Shanghai, China
| | - Yi Yang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 200237, Shanghai, China
- Synthetic Biology and Biotechnology Laboratory, State Key Laboratory of Bioreactor Engineering, Shanghai Collaborative Innovation Center for Biomanufacturing Technology, 200237, Shanghai, China
| | - Jin Huang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 200237, Shanghai, China
| | - David E Hill
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA, 02115, USA
| | - Marc Vidal
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA, 02115, USA
| | - Charis Eng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, 44195, USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA
- Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
- Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA
| | - Joseph Loscalzo
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
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26
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Understanding Human-Virus Protein-Protein Interactions Using a Human Protein Complex-Based Analysis Framework. mSystems 2019; 4:mSystems00303-18. [PMID: 30984872 PMCID: PMC6456672 DOI: 10.1128/msystems.00303-18] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Accepted: 03/20/2019] [Indexed: 12/29/2022] Open
Abstract
Although human protein complexes have been reported to be directly related to viral infection, previous studies have not systematically investigated human-virus PPIs from the perspective of human protein complexes. To the best of our knowledge, we have presented here the most comprehensive and in-depth analysis of human-virus PPIs in the context of VTCs. Our findings confirm that human protein complexes are heavily involved in viral infection. The observed preferences of virally targeted subunits within complexes reflect the mechanisms used by viruses to manipulate host protein complexes. The identified periodic expression patterns of the VTCs and the corresponding candidates could increase our understanding of how viruses manipulate the host cell cycle. Finally, our proposed conceptual application framework of VTCs and the developed VTcomplex could provide new hints to develop antiviral drugs for the clinical treatment of viral infections. Computational analysis of human-virus protein-protein interaction (PPI) data is an effective way toward systems understanding the molecular mechanism of viral infection. Previous work has mainly focused on characterizing the global properties of viral targets within the entire human PPI network. In comparison, how viruses manipulate host local networks (e.g., human protein complexes) has been rarely addressed from a computational perspective. By mainly integrating information about human-virus PPIs, human protein complexes, and gene expression profiles, we performed a large-scale analysis of virally targeted complexes (VTCs) related to five common human-pathogenic viruses, including influenza A virus subtype H1N1, human immunodeficiency virus type 1, Epstein-Barr virus, human papillomavirus, and hepatitis C virus. We found that viral targets are enriched within human protein complexes. We observed in the context of VTCs that viral targets tended to have a high within-complex degree and to be scaffold and housekeeping proteins. Complexes that are essential for viral propagation were simultaneously targeted by multiple viruses. We characterized the periodic expression patterns of VTCs and provided the corresponding candidates that may be involved in the manipulation of the host cell cycle. As a potential application of the current analysis, we proposed a VTC-based antiviral drug target discovery strategy. Finally, we developed an online VTC-related platform known as VTcomplex (http://zzdlab.com/vtcomplex/index.php or http://systbio.cau.edu.cn/vtcomplex/index.php). We hope that the current analysis can provide new insights into the global landscape of human-virus PPIs at the VTC level and that the developed VTcomplex will become a vital resource for the community. IMPORTANCE Although human protein complexes have been reported to be directly related to viral infection, previous studies have not systematically investigated human-virus PPIs from the perspective of human protein complexes. To the best of our knowledge, we have presented here the most comprehensive and in-depth analysis of human-virus PPIs in the context of VTCs. Our findings confirm that human protein complexes are heavily involved in viral infection. The observed preferences of virally targeted subunits within complexes reflect the mechanisms used by viruses to manipulate host protein complexes. The identified periodic expression patterns of the VTCs and the corresponding candidates could increase our understanding of how viruses manipulate the host cell cycle. Finally, our proposed conceptual application framework of VTCs and the developed VTcomplex could provide new hints to develop antiviral drugs for the clinical treatment of viral infections.
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27
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de Anda‐Jáuregui G, McGregor BA, Guo K, Hur J. A Network Pharmacology Approach for the Identification of Common Mechanisms of Drug-Induced Peripheral Neuropathy. CPT Pharmacometrics Syst Pharmacol 2019; 8:211-219. [PMID: 30762308 PMCID: PMC6482281 DOI: 10.1002/psp4.12383] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2018] [Accepted: 12/27/2018] [Indexed: 01/06/2023] Open
Abstract
Drug-induced peripheral neuropathy is a side effect of a variety of therapeutic agents that can affect therapeutic adherence and lead to regimen modifications, impacting patient quality of life. The molecular mechanisms involved in the development of this condition have yet to be completely described in the literature. We used a computational network pharmacology approach to explore the Connectivity Map, a large collection of transcriptional profiles from drug perturbation experiments to identify common genes affected by peripheral neuropathy-inducing drugs. Consensus profiles for 98 of these drugs were used to construct a drug-gene perturbation network. We identified 27 genes significantly associated with neuropathy-inducing drugs. These genes may have a potential role in the action of neuropathy-inducing drugs. Our results suggest that molecular mechanisms, including alterations in mitochondrial function, microtubule and cytoskeleton function, ion channels, transcriptional regulation including epigenetic mechanisms, signal transduction, and wound healing, may play a critical role in drug-induced peripheral neuropathy.
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Affiliation(s)
- Guillermo de Anda‐Jáuregui
- Department of Biomedical SciencesSchool of Medicine & Health SciencesUniversity of North DakotaGrand ForksNorth DakotaUSA
- Present address:
Computational Genomics DivisionNational Institute of Genomic MedicineColonia Arenal TepepanDelegación TlalpanMéxico DFMexico
| | - Brett A. McGregor
- Department of Biomedical SciencesSchool of Medicine & Health SciencesUniversity of North DakotaGrand ForksNorth DakotaUSA
| | - Kai Guo
- Department of Biomedical SciencesSchool of Medicine & Health SciencesUniversity of North DakotaGrand ForksNorth DakotaUSA
| | - Junguk Hur
- Department of Biomedical SciencesSchool of Medicine & Health SciencesUniversity of North DakotaGrand ForksNorth DakotaUSA
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28
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Abstract
Network-aided in silico approaches have been widely used for prediction of drug-target interactions and evaluation of drug safety to increase the clinical efficiency and productivity during drug discovery and development. Here we review the advances and new progress in this field and summarize the translational applications of several new network-aided in silico approaches we developed recently. In addition, we describe the detailed protocols for a network-aided drug repositioning infrastructure for identification of new targets for old drugs, failed drugs in clinical trials, and new chemical entities. These state-of-the-art network-aided in silico approaches have been used for the discovery and development of broad-acting and targeted clinical therapies for various complex diseases, in particular for oncology drug repositioning. In this chapter, the described network-aided in silico protocols are appropriate for target-centric drug repositioning to various complex diseases, but expertise is still necessary to perform the specific oncology projects based on the cancer targets of interest.
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29
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Dhama K, Karthik K, Khandia R, Chakraborty S, Munjal A, Latheef SK, Kumar D, Ramakrishnan MA, Malik YS, Singh R, Malik SVS, Singh RK, Chaicumpa W. Advances in Designing and Developing Vaccines, Drugs, and Therapies to Counter Ebola Virus. Front Immunol 2018; 9:1803. [PMID: 30147687 PMCID: PMC6095993 DOI: 10.3389/fimmu.2018.01803] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2018] [Accepted: 07/23/2018] [Indexed: 01/10/2023] Open
Abstract
Ebola virus (EBOV), a member of the family Filoviridae, is responsible for causing Ebola virus disease (EVD) (formerly named Ebola hemorrhagic fever). This is a severe, often fatal illness with mortality rates varying from 50 to 90% in humans. Although the virus and associated disease has been recognized since 1976, it was only when the recent outbreak of EBOV in 2014-2016 highlighted the danger and global impact of this virus, necessitating the need for coming up with the effective vaccines and drugs to counter its pandemic threat. Albeit no commercial vaccine is available so far against EBOV, a few vaccine candidates are under evaluation and clinical trials to assess their prophylactic efficacy. These include recombinant viral vector (recombinant vesicular stomatitis virus vector, chimpanzee adenovirus type 3-vector, and modified vaccinia Ankara virus), Ebola virus-like particles, virus-like replicon particles, DNA, and plant-based vaccines. Due to improvement in the field of genomics and proteomics, epitope-targeted vaccines have gained top priority. Correspondingly, several therapies have also been developed, including immunoglobulins against specific viral structures small cell-penetrating antibody fragments that target intracellular EBOV proteins. Small interfering RNAs and oligomer-mediated inhibition have also been verified for EVD treatment. Other treatment options include viral entry inhibitors, transfusion of convalescent blood/serum, neutralizing antibodies, and gene expression inhibitors. Repurposed drugs, which have proven safety profiles, can be adapted after high-throughput screening for efficacy and potency for EVD treatment. Herbal and other natural products are also being explored for EVD treatment. Further studies to better understand the pathogenesis and antigenic structures of the virus can help in developing an effective vaccine and identifying appropriate antiviral targets. This review presents the recent advances in designing and developing vaccines, drugs, and therapies to counter the EBOV threat.
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Affiliation(s)
- Kuldeep Dhama
- Division of Pathology, ICAR-Indian Veterinary Research Institute, Bareilly, India
| | - Kumaragurubaran Karthik
- Central University Laboratory, Tamil Nadu Veterinary and Animal Sciences University, Chennai, India
| | - Rekha Khandia
- Department of Biochemistry and Genetics, Barkatullah University, Bhopal, India
| | - Sandip Chakraborty
- Department of Veterinary Microbiology, College of Veterinary Sciences and Animal Husbandry, Agartala, India
| | - Ashok Munjal
- Department of Biochemistry and Genetics, Barkatullah University, Bhopal, India
| | - Shyma K. Latheef
- Immunology Section, ICAR-Indian Veterinary Research Institute, Bareilly, India
| | - Deepak Kumar
- Division of Veterinary Biotechnology, ICAR-Indian Veterinary Research Institute, Bareilly, India
| | | | - Yashpal Singh Malik
- Division of Biological Standardization, ICAR-Indian Veterinary Research Institute, Bareilly, India
| | - Rajendra Singh
- Division of Pathology, ICAR-Indian Veterinary Research Institute, Bareilly, India
| | - Satya Veer Singh Malik
- Division of Veterinary Public Health, ICAR-Indian Veterinary Research Institute, Bareilly, India
| | - Raj Kumar Singh
- ICAR-Indian Veterinary Research Institute, Bareilly, Uttar Pradesh, India
| | - Wanpen Chaicumpa
- Center of Research Excellence on Therapeutic Proteins and Antibody Engineering, Department of Parasitology, Faculty of Medicine SIriraj Hospital, Mahidol University, Bangkok, Thailand
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30
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Proteomic composition of Nipah virus-like particles. J Proteomics 2018; 172:190-200. [DOI: 10.1016/j.jprot.2017.10.012] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2017] [Revised: 10/13/2017] [Accepted: 10/22/2017] [Indexed: 01/28/2023]
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31
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Khan FN, Qazi S, Tanveer K, Raza K. A review on the antagonist Ebola: A prophylactic approach. Biomed Pharmacother 2017; 96:1513-1526. [PMID: 29208326 PMCID: PMC7126370 DOI: 10.1016/j.biopha.2017.11.103] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2017] [Revised: 11/17/2017] [Accepted: 11/17/2017] [Indexed: 11/20/2022] Open
Abstract
Ebola virus (EBOV), a member of Filoviridae virus family under the genus Ebolavirus, has emerged as a dangerous and potential threat to human health globally. It causes a severe and deadly hemorrhagic fever in humans and other mammals, called Ebola Virus Disease (EVD). In recent outbreaks of EVD, there has been loss of large numbers of individual’s life. Therefore, EBOV has attracted researchers and increased interests in developing new models for virus evolution, and therapies. The EBOV interacts with the immune system of the host which led to understand how the virus functions and effects immune system behaviour. This article presents an exhaustive review on Ebola research which includes EVD illness, symptoms, transmission patterns, patho-physiology conditions, development of antiviral agents and vaccines, resilient health system, dynamics and mathematical model of EBOV, challenges and prospects for future studies.
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Affiliation(s)
- Fatima Nazish Khan
- Computational Intelligence and Bioinformatics Lab, Department of Computer Science, Jamia Millia Islamia, New Delhi, 110025, India
| | - Sahar Qazi
- Computational Intelligence and Bioinformatics Lab, Department of Computer Science, Jamia Millia Islamia, New Delhi, 110025, India
| | - Khushnuma Tanveer
- Computational Intelligence and Bioinformatics Lab, Department of Computer Science, Jamia Millia Islamia, New Delhi, 110025, India
| | - Khalid Raza
- Computational Intelligence and Bioinformatics Lab, Department of Computer Science, Jamia Millia Islamia, New Delhi, 110025, India.
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32
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Singh RK, Dhama K, Malik YS, Ramakrishnan MA, Karthik K, Khandia R, Tiwari R, Munjal A, Saminathan M, Sachan S, Desingu PA, Kattoor JJ, Iqbal HMN, Joshi SK. Ebola virus - epidemiology, diagnosis, and control: threat to humans, lessons learnt, and preparedness plans - an update on its 40 year's journey. Vet Q 2017; 37:98-135. [PMID: 28317453 DOI: 10.1080/01652176.2017.1309474] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
Ebola virus (EBOV) is an extremely contagious pathogen and causes lethal hemorrhagic fever disease in man and animals. The recently occurred Ebola virus disease (EVD) outbreaks in the West African countries have categorized it as an international health concern. For the virus maintenance and transmission, the non-human primates and reservoir hosts like fruit bats have played a vital role. For curbing the disease timely, we need effective therapeutics/prophylactics, however, in the absence of any approved vaccine, timely diagnosis and monitoring of EBOV remains of utmost importance. The technologically advanced vaccines like a viral-vectored vaccine, DNA vaccine and virus-like particles are underway for testing against EBOV. In the absence of any effective control measure, the adaptation of high standards of biosecurity measures, strict sanitary and hygienic practices, strengthening of surveillance and monitoring systems, imposing appropriate quarantine checks and vigilance on trade, transport, and movement of visitors from EVD endemic countries remains the answer of choice for tackling the EBOV spread. Herein, we converse with the current scenario of EBOV giving due emphasis on animal and veterinary perspectives along with advances in diagnosis and control strategies to be adopted, lessons learned from the recent outbreaks and the global preparedness plans. To retrieve the evolutionary information, we have analyzed a total of 56 genome sequences of various EBOV species submitted between 1976 and 2016 in public databases.
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Affiliation(s)
- Raj Kumar Singh
- a ICAR-Indian Veterinary Research Institute , Bareilly , India
| | - Kuldeep Dhama
- b Division of Pathology, ICAR-Indian Veterinary Research Institute , Bareilly , India
| | - Yashpal Singh Malik
- c Division of Biological Standardization, ICAR-Indian Veterinary Research Institute , Bareilly , India
| | | | - Kumaragurubaran Karthik
- e Divison of Bacteriology and Mycology, ICAR-Indian Veterinary Research Institute , Bareilly , India
| | - Rekha Khandia
- f Department of Biochemistry and Genetics , Barkatullah University , Bhopal , India
| | - Ruchi Tiwari
- g Department of Veterinary Microbiology and Immunology , College of Veterinary Sciences, Deen Dayal Upadhayay Pashu Chikitsa Vigyan Vishwavidyalay Evum Go-Anusandhan Sansthan (DUVASU) , Mathura , India
| | - Ashok Munjal
- f Department of Biochemistry and Genetics , Barkatullah University , Bhopal , India
| | - Mani Saminathan
- b Division of Pathology, ICAR-Indian Veterinary Research Institute , Bareilly , India
| | - Swati Sachan
- h Immunology Section, ICAR-Indian Veterinary Research Institute , Bareilly , India
| | | | - Jobin Jose Kattoor
- c Division of Biological Standardization, ICAR-Indian Veterinary Research Institute , Bareilly , India
| | - Hafiz M N Iqbal
- i School of Engineering and Science, Tecnologico de Monterrey , Monterrey , Mexico
| | - Sunil Kumar Joshi
- j Cellular Immunology Lab , Frank Reidy Research Center for Bioelectrics , School of Medical Diagnostics & Translational Sciences, Old Dominion University , Norfolk , VA , USA
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33
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Bonjardim CA. Viral exploitation of the MEK/ERK pathway - A tale of vaccinia virus and other viruses. Virology 2017; 507:267-275. [PMID: 28526201 DOI: 10.1016/j.virol.2016.12.011] [Citation(s) in RCA: 56] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2016] [Revised: 12/07/2016] [Accepted: 12/09/2016] [Indexed: 12/14/2022]
Abstract
The VACV replication cycle is remarkable in the sense that it is performed entirely in the cytoplasmic compartment of vertebrate cells, due to its capability to encode enzymes required either for regulating the macromolecular precursor pool or the biosynthetic processes. Although remarkable, this gene repertoire is not sufficient to confer the status of a free-living microorganism to the virus, and, consequently, the virus relies heavily on the host to successfully generate its progeny. During the complex virus-host interaction, viruses must deal not only with the host pathways to accomplish their temporal demands but also with pathways that counteract viral infection, including the inflammatory, innate and acquired immune responses. This review focuses on VACV and other DNA or RNA viruses that stimulate the MEK (MAPK - Mitogen Activated Protein Kinase)/ERK- Extracellular signal-Regulated Kinase) pathway as part of their replication cycle.
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Affiliation(s)
- Cláudio A Bonjardim
- Signal Transduction Group/Viruses Laboratory, Department of Microbiology, Institute of Biological Sciences, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6627, CEP: 31.270-901, Belo Horizonte, Minas Gerais, Brazil.
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34
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Fang J, Liu C, Wang Q, Lin P, Cheng F. In silico polypharmacology of natural products. Brief Bioinform 2017; 19:1153-1171. [DOI: 10.1093/bib/bbx045] [Citation(s) in RCA: 63] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2017] [Indexed: 12/16/2022] Open
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35
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Fang J, Cai C, Wang Q, Lin P, Zhao Z, Cheng F. Systems Pharmacology-Based Discovery of Natural Products for Precision Oncology Through Targeting Cancer Mutated Genes. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2017; 6:177-187. [PMID: 28294568 PMCID: PMC5356618 DOI: 10.1002/psp4.12172] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/11/2016] [Revised: 01/09/2017] [Accepted: 01/10/2017] [Indexed: 02/05/2023]
Abstract
Massive cancer genomics data have facilitated the rapid revolution of a novel oncology drug discovery paradigm through targeting clinically relevant driver genes or mutations for the development of precision oncology. Natural products with polypharmacological profiles have been demonstrated as promising agents for the development of novel cancer therapies. In this study, we developed an integrated systems pharmacology framework that facilitated identifying potential natural products that target mutated genes across 15 cancer types or subtypes in the realm of precision medicine. High performance was achieved for our systems pharmacology framework. In case studies, we computationally identified novel anticancer indications for several US Food and Drug Administration-approved or clinically investigational natural products (e.g., resveratrol, quercetin, genistein, and fisetin) through targeting significantly mutated genes in multiple cancer types. In summary, this study provides a powerful tool for the development of molecularly targeted cancer therapies through targeting the clinically actionable alterations by exploiting the systems pharmacology of natural products.
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Affiliation(s)
- J Fang
- Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine, Guangzhou, P.R. China
| | - C Cai
- Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine, Guangzhou, P.R. China
| | - Q Wang
- Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine, Guangzhou, P.R. China
| | - P Lin
- State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, and Collaborative Innovation Center for Biotherapy, Chengdu, Sichuan, P.R. China
| | - Z Zhao
- Center for Precision Health, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas, USA.,Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - F Cheng
- State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, and Collaborative Innovation Center for Biotherapy, Chengdu, Sichuan, P.R. China.,Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA.,Center for Complex Networks Research, Northeastern University, Boston, Massachusetts, USA
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36
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Mathematical and Computational Modeling in Complex Biological Systems. BIOMED RESEARCH INTERNATIONAL 2017; 2017:5958321. [PMID: 28386558 PMCID: PMC5366773 DOI: 10.1155/2017/5958321] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/16/2016] [Revised: 12/20/2016] [Accepted: 01/16/2017] [Indexed: 12/22/2022]
Abstract
The biological process and molecular functions involved in the cancer progression remain difficult to understand for biologists and clinical doctors. Recent developments in high-throughput technologies urge the systems biology to achieve more precise models for complex diseases. Computational and mathematical models are gradually being used to help us understand the omics data produced by high-throughput experimental techniques. The use of computational models in systems biology allows us to explore the pathogenesis of complex diseases, improve our understanding of the latent molecular mechanisms, and promote treatment strategy optimization and new drug discovery. Currently, it is urgent to bridge the gap between the developments of high-throughput technologies and systemic modeling of the biological process in cancer research. In this review, we firstly studied several typical mathematical modeling approaches of biological systems in different scales and deeply analyzed their characteristics, advantages, applications, and limitations. Next, three potential research directions in systems modeling were summarized. To conclude, this review provides an update of important solutions using computational modeling approaches in systems biology.
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Lu W, Yao X, Ouyang P, Dong N, Wu D, Jiang X, Wu Z, Zhang C, Xu Z, Tang Y, Zou S, Liu M, Li J, Zeng M, Lin P, Cheng F, Huang J. Drug Repurposing of Histone Deacetylase Inhibitors That Alleviate Neutrophilic Inflammation in Acute Lung Injury and Idiopathic Pulmonary Fibrosis via Inhibiting Leukotriene A4 Hydrolase and Blocking LTB4 Biosynthesis. J Med Chem 2017; 60:1817-1828. [PMID: 28218840 DOI: 10.1021/acs.jmedchem.6b01507] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Acute lung injury (ALI) and idiopathic pulmonary fibrosis (IPF) are both serious public health problems with high incidence and mortality rate in adults, and with few drugs available for the efficient treatment in clinic. In this study, we identified that two known histone deacetylase (HDAC) inhibitors, suberanilohydroxamic acid (SAHA, 1) and its analogue 4-(dimethylamino)-N-[7-(hydroxyamino)-7-oxoheptyl]benzamide (2), are effective inhibitors of Leukotriene A4 hydrolase (LTA4H), a key enzyme in the biosynthesis of leukotriene B4 (LTB4), across a panel of 18 HDAC inhibitors, using enzymatic assay, thermofluor assay, and X-ray crystallographic investigation. Importantly, both 1 and 2 markedly diminish early neutrophilic inflammation in mouse models of ALI and IPF under a clinical safety dose. Detailed mechanisms of down-regulation of proinflammatory cytokines by 1 or 2 were determined in vivo. Collectively, 1 and 2 would provide promising agents with well-known clinical safety for potential treatment in patients with ALI and IPF via pharmacologically inhibiting LAT4H and blocking LTB4 biosynthesis.
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Affiliation(s)
- Weiqiang Lu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology , Shanghai 200237, China.,Shanghai Key Laboratory of Regulatory Biology, The Institute of Biomedical Sciences and School of Life Sciences, East China Normal University , Shanghai 200241, China
| | - Xue Yao
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology , Shanghai 200237, China
| | - Ping Ouyang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology , Shanghai 200237, China
| | - Ningning Dong
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology , Shanghai 200237, China
| | - Dang Wu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology , Shanghai 200237, China
| | - Xingwu Jiang
- Shanghai Key Laboratory of Regulatory Biology, The Institute of Biomedical Sciences and School of Life Sciences, East China Normal University , Shanghai 200241, China
| | - Zengrui Wu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology , Shanghai 200237, China
| | - Chen Zhang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology , Shanghai 200237, China
| | - Zhongyu Xu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology , Shanghai 200237, China
| | - Yun Tang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology , Shanghai 200237, China
| | - Shien Zou
- Department of Gynecology, Obstetrics and Gynecology Hospital of Fudan University , Shanghai 200011, China
| | - Mingyao Liu
- Shanghai Key Laboratory of Regulatory Biology, The Institute of Biomedical Sciences and School of Life Sciences, East China Normal University , Shanghai 200241, China
| | - Jian Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology , Shanghai 200237, China
| | - Minghua Zeng
- Key Laboratory for the Chemistry and Molecular Engineering of Medicinal Resources (Ministry of Education), School of Chemistry & Chemical Engineering, Guangxi Normal University , Guilin 541004, China
| | - Ping Lin
- State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, and Collaborative Innovation Center for Biotherapy , Chengdu 610041, Sichuan, China
| | - Feixiong Cheng
- State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, and Collaborative Innovation Center for Biotherapy , Chengdu 610041, Sichuan, China.,Center for Complex Networks Research, Northeastern University , Boston, Massachusetts 02115, United States.,Center for Cancer Systems Biology and Department of Cancer Biology, Dana-Farber Cancer Institute, Harvard Medical School , Boston, Massachusetts 02215, United States
| | - Jin Huang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology , Shanghai 200237, China
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38
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Xin M, Fan J, Liu M, Jiang Z. Exploration and analysis of drug modes of action through feature integration. MOLECULAR BIOSYSTEMS 2017; 13:425-431. [DOI: 10.1039/c6mb00635c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Identifying drug modes of action (MoA) is of paramount importance for having a good grasp of drug indications in clinical tests.
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Affiliation(s)
- Mingyuan Xin
- Shanghai Key Laboratory of Regulatory Biology
- Institute of Biomedical Sciences and School of Life Sciences
- East China Normal University
- Shanghai 200241
- China
| | - Jun Fan
- Shanghai Key Laboratory of Multidimensional Information Processing
- Department of Computer Science and Technology
- East China Normal University
- Shanghai 200241
- China
| | - Mingyao Liu
- Shanghai Key Laboratory of Regulatory Biology
- Institute of Biomedical Sciences and School of Life Sciences
- East China Normal University
- Shanghai 200241
- China
| | - Zhenran Jiang
- Shanghai Key Laboratory of Multidimensional Information Processing
- Department of Computer Science and Technology
- East China Normal University
- Shanghai 200241
- China
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39
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Fedson DS. Treating the host response to emerging virus diseases: lessons learned from sepsis, pneumonia, influenza and Ebola. ANNALS OF TRANSLATIONAL MEDICINE 2016; 4:421. [PMID: 27942512 DOI: 10.21037/atm.2016.11.03] [Citation(s) in RCA: 92] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
There is an ongoing threat of epidemic or pandemic diseases that could be caused by influenza, Ebola or other emerging viruses. It will be difficult and costly to develop new drugs that target each of these viruses. Statins and angiotensin receptor blockers (ARBs) have been effective in treating patients with sepsis, pneumonia and influenza, and a statin/ARB combination appeared to dramatically reduce mortality during the recent Ebola outbreak. These drugs target (among other things) the endothelial dysfunction found in all of these diseases. Most scientists work on new drugs that target viruses, and few accept the idea of treating the host response with generic drugs. A great deal of research will be needed to show conclusively that these drugs work, and this will require the support of public agencies and foundations. Investigators in developing countries should take an active role in this research. If the next Public Health Emergency of International Concern is caused by an emerging virus, a "top down" approach to developing specific new drug treatments is unlikely to be effective. However, a "bottom up" approach to treatment that targets the host response to these viruses by using widely available and inexpensive generic drugs could reduce mortality in any country with a basic health care system. In doing so, it would make an immeasurable contribution to global equity and global security.
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Affiliation(s)
- David S Fedson
- Formerly, Department of Medicine, University of Virginia School of Medicine, Charlottesville, Virginia, USA
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40
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Cheng F, Zhao J, Hanker AB, Brewer MR, Arteaga CL, Zhao Z. Transcriptome- and proteome-oriented identification of dysregulated eIF4G, STAT3, and Hippo pathways altered by PIK3CA H1047R in HER2/ER-positive breast cancer. Breast Cancer Res Treat 2016; 160:457-474. [PMID: 27771839 PMCID: PMC10183099 DOI: 10.1007/s10549-016-4011-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2016] [Accepted: 10/05/2016] [Indexed: 01/25/2023]
Abstract
PURPOSE Phosphatidylinositol 3-kinase (PI3K)/AKT pathway aberrations are common in human breast cancer. Furthermore, PIK3CA mutations are commonly associated with resistance to anti-epidermal growth factor receptor 2 (HER2) or anti-estrogen receptor (ER) agents in HER2 or ER positive (HER2+/ER+) breast cancer. Hence, deciphering the underlying mechanisms of PIK3CA mutations in HER2+/ER+ breast cancer would provide novel insights into elucidating resistance to anti-HER2/ER therapies. METHODS In this study, we systematically investigated the biological consequences of PIK3CA H1047R in HER2+/ER+ breast cancer by uniquely incorporating mRNA transcriptomic data from The Cancer Genome Atlas and proteomic data from reverse-phase protein arrays. RESULTS Our integrative bioinformatics analyses revealed that several important pathways such as STAT3 and VEGF/hypoxia were selectively altered by PIK3CA H1047R in HER2+/ER+ breast cancer. Protein differential expression analysis indicated that an elevated eIF4G might promote tumor angiogenesis and growth via regulation of the hypoxia-activated switch in HER2+ PIK3CA H1047R breast cancer. We observed hypo-phosphorylation of EGFR in HER2+ PIK3CA H1047R breast cancer versus HER2+PIK3CAwild-type (PIK3CA WT). In addition, ER and PIK3CA H1047R might cooperate to activate STAT3, MAPK, AKT, and Hippo pathways in ER+ PIK3CA H1047R breast cancer. A higher YAPpS127 level was observed in ER+ PIK3CA H1047R patients than that in an ER+ PIK3CA WT subgroup. By examining breast cancer cell lines having both microarray gene expression and drug treatment data from the Genomics of Drug Sensitivity in Cancer and the Stand Up to Cancer datasets, we found that the elevated YAP1 mRNA expression was associated with the resistance of BCL-2 family inhibitors, but with the sensitivity to MEK/MAPK inhibitors in breast cancer cells. CONCLUSIONS In summary, these findings shed light on the functional consequences of PIK3CA H1047R-driven breast tumorigenesis and resistance to the existing therapeutic agents in HER2+/ER+ breast cancer.
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Affiliation(s)
- Feixiong Cheng
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, 37203, USA.,Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, 02215, USA.,Center for Complex Networks Research, Northeastern University, Boston, MA, 02115, USA
| | - Junfei Zhao
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, 37203, USA.,Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Ariella B Hanker
- Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Monica Red Brewer
- Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Carlos L Arteaga
- Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA. .,Department of Cancer Biology, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA. .,Breast Cancer Research Program, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.
| | - Zhongming Zhao
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, 37203, USA. .,Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA. .,Department of Cancer Biology, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA. .,Breast Cancer Research Program, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.
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41
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Cheng F, Murray JL, Rubin DH. Drug Repurposing: New Treatments for Zika Virus Infection? Trends Mol Med 2016; 22:919-921. [PMID: 27692879 DOI: 10.1016/j.molmed.2016.09.006] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2016] [Accepted: 09/13/2016] [Indexed: 11/25/2022]
Abstract
To date, no antiviral agents have been approved for treating Zika virus (ZIKV) infection. Two recent drug-repurposing studies published in Cell Host & Microbe and Nature Medicine demonstrated that screening FDA-approved drugs for antiviral activity is a promising strategy for identifying therapeutics with novel activity against ZIKV infection.
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Affiliation(s)
- Feixiong Cheng
- Center for Complex Networks Research, Northeastern University, Boston, MA 02115, USA; Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
| | | | - Donald H Rubin
- Division of Infectious Disease, Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN 37232, USA; Department of Pathology, Microbiology and Immunology, Vanderbilt University School of Medicine, Nashville, TN 37232, USA; Research Medicine, VA Tennessee Valley Healthcare System, Nashville, TN 37212, USA
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