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Dai Z, Hu T, Wei J, Wang X, Cai C, Gu Y, Hu Y, Wang W, Wu Q, Fang J. Network-based identification and mechanism exploration of active ingredients against Alzheimer's disease via targeting endoplasmic reticulum stress from traditional chinese medicine. Comput Struct Biotechnol J 2024; 23:506-519. [PMID: 38261917 PMCID: PMC10796977 DOI: 10.1016/j.csbj.2023.12.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 12/14/2023] [Accepted: 12/16/2023] [Indexed: 01/25/2024] Open
Abstract
Alzheimer's disease is a neurodegenerative disease that leads to dementia and poses a serious threat to the health of the elderly. Traditional Chinese medicine (TCM) presents as a promising novel therapeutic therapy for preventing and treating dementia. Studies have shown that natural products derived from kidney-tonifying herbs can effectively inhibit AD. Furthermore, endoplasmic reticulum (ER) stress is a critical factor in the pathology of AD. Regulation of ER stress is a crucial approach to prevent and treat AD. Thus, in this study, we first collected kidney-tonifying herbs, integrated chemical ingredients from multiple TCM databases, and constructed a comprehensive drug-target network. Subsequently, we employed the endophenotype network (network proximity) method to identify potential active ingredients in kidney-tonifying herbs that prevented AD via regulating ER stress. By combining the predicted outcomes, we discovered that 32 natural products could ameliorate AD pathology via regulating ER stress. After a comprehensive evaluation of the multi-network model and systematic pharmacological analyses, we further selected several promising compounds for in vitro testing in the APP-SH-SY5Y cell model. Experimental results showed that echinacoside and danthron were able to effectively reduce ER stress-mediated neuronal apoptosis by inhibiting the expression levels of BIP, p-PERK, ATF6, and CHOP in APP-SH-SY5Y cells. Overall, this study utilized the endophenotype network to preliminarily decipher the effective material basis and potential molecular mechanism of kidney-tonifying Chinese medicine for prevention and treatment of AD.
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Affiliation(s)
- Zhao Dai
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou 510405, China
| | - Tian Hu
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou 510405, China
| | - Junwen Wei
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou 510405, China
| | - Xue Wang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou 510405, China
| | - Chuipu Cai
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou 510405, China
| | - Yong Gu
- Clinical Research Center, Hainan Provincial Hospital of Traditional Chinese Medicine, Hainan Medical University, Haikou 570100, China
| | - Yunhui Hu
- Tasly Pharmaceutical Group Co., Ltd., Tianjin 300402, China
| | - Wenjia Wang
- Tasly Pharmaceutical Group Co., Ltd., Tianjin 300402, China
| | - Qihui Wu
- Clinical Research Center, Hainan Provincial Hospital of Traditional Chinese Medicine, Hainan Medical University, Haikou 570100, China
| | - Jiansong Fang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou 510405, China
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2
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Godos J, Micek A, Currenti W, Franchi C, Poli A, Battino M, Dolci A, Ricci C, Ungvari Z, Grosso G. Fish consumption, cognitive impairment and dementia: an updated dose-response meta-analysis of observational studies. Aging Clin Exp Res 2024; 36:171. [PMID: 39162889 PMCID: PMC11335789 DOI: 10.1007/s40520-024-02823-6] [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: 06/21/2024] [Accepted: 07/28/2024] [Indexed: 08/21/2024]
Abstract
BACKGROUND Cognitive impairment is projected to affect a preponderant proportion of the aging population. Lifelong dietary habits have been hypothesized to play a role in preventing cognitive decline. Among the most studied dietary components, fish consumptionhas been extensively studied for its potential effects on the human brain. AIMS To perform a meta-analysis of observational studies exploring the association between fish intake and cognitive impairment/decline and all types of dementia. METHODS A systematic search of electronic databases was performed to identify observational studies providing quantitative data on fish consumption and outcomes of interest. Random effects models for meta-analyses using only extreme exposure categories, subgroup analyses, and dose-response analyses were performed to estimate cumulative risk ratios (RRs) and 95% confidence intervals (CIs). RESULTS The meta-analysis comprised 35 studies. Individuals reporting the highest vs. the lowest fish consumption were associated with a lower likelihood of cognitive impairment/decline (RR = 0.82, 95% CI: 0.75, 0.90, I2 = 61.1%), dementia (RR = 0.82, 95% CI: 0.73, 0.93, I2 = 38.7%), and Alzheimer's disease (RR = 0.80, 95% CI: 0.67, 0.96, I2 = 20.3%). The dose-response relation revealed a significantly decreased risk of cognitive impairment/decline and all cognitive outcomes across higher levels of fish intake up to 30% for 150 g/d (RR = 0.70, 95% CI: 0.52, 0.95). The results of this relation based on APOE ε4 allele status was mixed based on the outcome investigated. CONCLUSIONS Current findings suggest fish consumption is associated with a lower risk of cognitive impairment/decline in a dose-response manner, while for dementia and Alzheimer's disease there is a need for further studies to improve the strength of evidence.
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Affiliation(s)
- Justyna Godos
- Department of Biomedical and Biotechnological Sciences, University of Catania, Catania, Italy
- Center for Human Nutrition and Mediterranean Foods (NUTREA), University of Catania, Catania, Italy
| | - Agnieszka Micek
- Statistical Laboratory, Faculty of Health Sciences, Jagiellonian University Medical College, Kraków, 31-501, Poland
| | - Walter Currenti
- Department of Biomedical and Biotechnological Sciences, University of Catania, Catania, Italy
| | - Carlotta Franchi
- Laboratory of Pharmacoepidemiology and Human Nutrition, Department of Health Policy, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, 20156, Italy
- Italian Institute for Planetary Health (IIPH), Milan, 20124, Italy
| | - Andrea Poli
- Nutrition Foundation of Italy (NFI), Milan, 20124, Italy
| | - Maurizio Battino
- Department of Clinical Sciences, Università Politecnica Delle Marche, Ancona, Italy
- Research Group on Food, Nutritional Biochemistry and Health, Universidad Europea del Atlántico, Isabel Torres 21, Santander, 39011, Spain
- International Joint Research Laboratory of Intelligent Agriculture and Agri-Products Processing, Jiangsu University, Zhenjiang, Jiangsu, China
| | - Alberto Dolci
- Sustainable Development Department, Bolton Food SpA, Milan, 20124, Italy
| | - Cristian Ricci
- Africa Unit for Transdisciplinary Health Research (AUTHeR), North-West University, Potchefstroom, 2531, South Africa
| | - Zoltan Ungvari
- Vascular Cognitive Impairment, Neurodegeneration and Healthy Brain Aging Program, Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- Oklahoma Center for Geroscience and Healthy Brain Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- International Training Program in Geroscience, Department of Public Health, Doctoral College, Semmelweis University, Budapest, Hungary
- Stephenson Cancer Center, University of Oklahoma, Oklahoma City, OK, USA
- Department of Health Promotion Sciences, College of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Giuseppe Grosso
- Department of Biomedical and Biotechnological Sciences, University of Catania, Catania, Italy.
- Center for Human Nutrition and Mediterranean Foods (NUTREA), University of Catania, Catania, Italy.
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3
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Ma M, Huang M, He Y, Fang J, Li J, Li X, Liu M, Zhou M, Cui G, Fan Q. Network Medicine: A Potential Approach for Virtual Drug Screening. Pharmaceuticals (Basel) 2024; 17:899. [PMID: 39065749 PMCID: PMC11280361 DOI: 10.3390/ph17070899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Revised: 06/27/2024] [Accepted: 07/04/2024] [Indexed: 07/28/2024] Open
Abstract
Traditional drug screening methods typically focus on a single protein target and exhibit limited efficiency due to the multifactorial nature of most diseases, which result from disturbances within complex networks of protein-protein interactions rather than single gene abnormalities. Addressing this limitation requires a comprehensive drug screening strategy. Network medicine is rooted in systems biology and provides a comprehensive framework for understanding disease mechanisms, prevention, and therapeutic innovations. This approach not only explores the associations between various diseases but also quantifies the relationships between disease genes and drug targets within interactome networks, thus facilitating the prediction of drug-disease relationships and enabling the screening of therapeutic drugs for specific complex diseases. An increasing body of research supports the efficiency and utility of network-based strategies in drug screening. This review highlights the transformative potential of network medicine in virtual therapeutic screening for complex diseases, offering novel insights and a robust foundation for future drug discovery endeavors.
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Affiliation(s)
- Mingxuan Ma
- School of Bioengineering, Zhuhai Campus of Zunyi Medical University, Zhuhai 519000, China; (M.M.); (M.H.); (Y.H.); (J.L.); (M.L.); (M.Z.)
| | - Mei Huang
- School of Bioengineering, Zhuhai Campus of Zunyi Medical University, Zhuhai 519000, China; (M.M.); (M.H.); (Y.H.); (J.L.); (M.L.); (M.Z.)
| | - Yinting He
- School of Bioengineering, Zhuhai Campus of Zunyi Medical University, Zhuhai 519000, China; (M.M.); (M.H.); (Y.H.); (J.L.); (M.L.); (M.Z.)
| | - Jiansong Fang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou 570000, China;
| | - Jiachao Li
- School of Bioengineering, Zhuhai Campus of Zunyi Medical University, Zhuhai 519000, China; (M.M.); (M.H.); (Y.H.); (J.L.); (M.L.); (M.Z.)
| | - Xiaohan Li
- School of Bioengineering, Zhuhai Campus of Zunyi Medical University, Zhuhai 519000, China; (M.M.); (M.H.); (Y.H.); (J.L.); (M.L.); (M.Z.)
| | - Mengchen Liu
- School of Bioengineering, Zhuhai Campus of Zunyi Medical University, Zhuhai 519000, China; (M.M.); (M.H.); (Y.H.); (J.L.); (M.L.); (M.Z.)
| | - Mei Zhou
- School of Bioengineering, Zhuhai Campus of Zunyi Medical University, Zhuhai 519000, China; (M.M.); (M.H.); (Y.H.); (J.L.); (M.L.); (M.Z.)
| | - Guozhen Cui
- School of Bioengineering, Zhuhai Campus of Zunyi Medical University, Zhuhai 519000, China; (M.M.); (M.H.); (Y.H.); (J.L.); (M.L.); (M.Z.)
| | - Qing Fan
- Basic Medical Science Department, Zhuhai Campus of Zunyi Medical University, Zhuhai 519041, China
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Qiu Y, Hou Y, Gohel D, Zhou Y, Xu J, Bykova M, Yang Y, Leverenz JB, Pieper AA, Nussinov R, Caldwell JZK, Brown JM, Cheng F. Systematic characterization of multi-omics landscape between gut microbial metabolites and GPCRome in Alzheimer's disease. Cell Rep 2024; 43:114128. [PMID: 38652661 DOI: 10.1016/j.celrep.2024.114128] [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: 12/22/2023] [Revised: 03/06/2024] [Accepted: 04/03/2024] [Indexed: 04/25/2024] Open
Abstract
Shifts in the magnitude and nature of gut microbial metabolites have been implicated in Alzheimer's disease (AD), but the host receptors that sense and respond to these metabolites are largely unknown. Here, we develop a systems biology framework that integrates machine learning and multi-omics to identify molecular relationships of gut microbial metabolites with non-olfactory G-protein-coupled receptors (termed the "GPCRome"). We evaluate 1.09 million metabolite-protein pairs connecting 408 human GPCRs and 335 gut microbial metabolites. Using genetics-derived Mendelian randomization and integrative analyses of human brain transcriptomic and proteomic profiles, we identify orphan GPCRs (i.e., GPR84) as potential drug targets in AD and that triacanthine experimentally activates GPR84. We demonstrate that phenethylamine and agmatine significantly reduce tau hyperphosphorylation (p-tau181 and p-tau205) in AD patient induced pluripotent stem cell-derived neurons. This study demonstrates a systems biology framework to uncover the GPCR targets of human gut microbiota in AD and other complex diseases if broadly applied.
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Affiliation(s)
- Yunguang Qiu
- Cleveland Clinic Genome Center, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA; Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Yuan Hou
- Cleveland Clinic Genome Center, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA; Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Dhruv Gohel
- Cleveland Clinic Genome Center, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA; Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Yadi Zhou
- Cleveland Clinic Genome Center, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA; Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Jielin Xu
- Cleveland Clinic Genome Center, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA; Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Marina Bykova
- Cleveland Clinic Genome Center, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA; Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Yuxin Yang
- Cleveland Clinic Genome Center, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA; Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - James B Leverenz
- Lou Ruvo Center for Brain Health, Neurological 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
| | - Andrew A Pieper
- Brain Health Medicines Center, Harrington Discovery Institute, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA; Department of Psychiatry, Case Western Reserve University, Cleveland, OH 44106, USA; Geriatric Psychiatry, GRECC, Louis Stokes Cleveland VA Medical Center, Cleveland, OH 44106, USA; Institute for Transformative Molecular Medicine, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA; Department of Neurosciences, Case Western Reserve University, School of Medicine, Cleveland, OH 44106, USA; Department of Pathology, Case Western Reserve University, School of Medicine, Cleveland, OH 44106, USA
| | - Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Cancer Innovation Laboratory, National Cancer Institute, Frederick, MD 21702, USA; Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
| | - Jessica Z K Caldwell
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA; Lou Ruvo Center for Brain Health, Neurological Institute, Cleveland Clinic, Las Vegas, NV 89106, USA
| | - J Mark Brown
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA; Department of Cancer Biology, Lerner Research Institute Cleveland Clinic, Cleveland, OH 44195, USA; Center for Microbiome and Human Health, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Feixiong Cheng
- Cleveland Clinic Genome Center, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA; 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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5
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Qiu Y, Cheng F. Artificial intelligence for drug discovery and development in Alzheimer's disease. Curr Opin Struct Biol 2024; 85:102776. [PMID: 38335558 DOI: 10.1016/j.sbi.2024.102776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 12/29/2023] [Accepted: 01/15/2024] [Indexed: 02/12/2024]
Abstract
The complex molecular mechanism and pathophysiology of Alzheimer's disease (AD) limits the development of effective therapeutics or prevention strategies. Artificial Intelligence (AI)-guided drug discovery combined with genetics/multi-omics (genomics, epigenomics, transcriptomics, proteomics, and metabolomics) analysis contributes to the understanding of the pathophysiology and precision medicine of the disease, including AD and AD-related dementia. In this review, we summarize the AI-driven methodologies for AD-agnostic drug discovery and development, including de novo drug design, virtual screening, and prediction of drug-target interactions, all of which have shown potentials. In particular, AI-based drug repurposing emerges as a compelling strategy to identify new indications for existing drugs for AD. We provide several emerging AD targets from human genetics and multi-omics findings and highlight recent AI-based technologies and their applications in drug discovery using AD as a prototypical example. In closing, we discuss future challenges and directions in AI-based drug discovery for AD and other neurodegenerative diseases.
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Affiliation(s)
- Yunguang Qiu
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA. https://twitter.com/YunguangQiu
| | - 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; Cleveland Clinic Genome Center, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA.
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6
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He D, Liu Q, Mi Y, Meng Q, Xu L, Hou C, Wang J, Li N, Liu Y, Chai H, Yang Y, Liu J, Wang L, Hou Y. De Novo Generation and Identification of Novel Compounds with Drug Efficacy Based on Machine Learning. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2307245. [PMID: 38204214 PMCID: PMC10962488 DOI: 10.1002/advs.202307245] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 12/05/2023] [Indexed: 01/12/2024]
Abstract
One of the main challenges in small molecule drug discovery is finding novel chemical compounds with desirable activity. Traditional drug development typically begins with target selection, but the correlation between targets and disease remains to be further investigated, and drugs designed based on targets may not always have the desired drug efficacy. The emergence of machine learning provides a powerful tool to overcome the challenge. Herein, a machine learning-based strategy is developed for de novo generation of novel compounds with drug efficacy termed DTLS (Deep Transfer Learning-based Strategy) by using dataset of disease-direct-related activity as input. DTLS is applied in two kinds of disease: colorectal cancer (CRC) and Alzheimer's disease (AD). In each case, novel compound is discovered and identified in in vitro and in vivo disease models. Their mechanism of actionis further explored. The experimental results reveal that DTLS can not only realize the generation and identification of novel compounds with drug efficacy but also has the advantage of identifying compounds by focusing on protein targets to facilitate the mechanism study. This work highlights the significant impact of machine learning on the design of novel compounds with drug efficacy, which provides a powerful new approach to drug discovery.
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Affiliation(s)
- Dakuo He
- College of Information Science and EngineeringState Key Laboratory of Synthetical Automation for Process IndustriesNortheastern UniversityShenyang110819China
| | - Qing Liu
- College of Information Science and EngineeringState Key Laboratory of Synthetical Automation for Process IndustriesNortheastern UniversityShenyang110819China
| | - Yan Mi
- Key Laboratory of Bioresource Research and Development of Liaoning ProvinceCollege of Life and Health SciencesNational Frontiers Science Center for Industrial Intelligence and Systems OptimizationNortheastern UniversityShenyang110169China
- Key Laboratory of Data Analytics and Optimization for Smart IndustryMinistry of EducationNortheastern UniversityShenyang110169China
| | - Qingqi Meng
- Key Laboratory of Bioresource Research and Development of Liaoning ProvinceCollege of Life and Health SciencesNational Frontiers Science Center for Industrial Intelligence and Systems OptimizationNortheastern UniversityShenyang110169China
- Key Laboratory of Data Analytics and Optimization for Smart IndustryMinistry of EducationNortheastern UniversityShenyang110169China
| | - Libin Xu
- Key Laboratory of Bioresource Research and Development of Liaoning ProvinceCollege of Life and Health SciencesNational Frontiers Science Center for Industrial Intelligence and Systems OptimizationNortheastern UniversityShenyang110169China
- Key Laboratory of Data Analytics and Optimization for Smart IndustryMinistry of EducationNortheastern UniversityShenyang110169China
| | - Chunyu Hou
- College of Information Science and EngineeringState Key Laboratory of Synthetical Automation for Process IndustriesNortheastern UniversityShenyang110819China
| | - Jinpeng Wang
- College of Information Science and EngineeringState Key Laboratory of Synthetical Automation for Process IndustriesNortheastern UniversityShenyang110819China
| | - Ning Li
- School of Traditional Chinese Materia MedicaKey Laboratory for TCM Material Basis Study and Innovative Drug Development of Shenyang CityShenyang Pharmaceutical UniversityShenyang110016China
| | - Yang Liu
- Key Laboratory of Structure‐Based Drug Design & Discovery of Ministry of EducationShenyang Pharmaceutical UniversityShenyang110016China
| | - Huifang Chai
- School of PharmacyGuizhou University of Traditional Chinese MedicineGuiyang550025China
| | - Yanqiu Yang
- Key Laboratory of Bioresource Research and Development of Liaoning ProvinceCollege of Life and Health SciencesNational Frontiers Science Center for Industrial Intelligence and Systems OptimizationNortheastern UniversityShenyang110169China
- Key Laboratory of Data Analytics and Optimization for Smart IndustryMinistry of EducationNortheastern UniversityShenyang110169China
| | - Jingyu Liu
- Key Laboratory of Bioresource Research and Development of Liaoning ProvinceCollege of Life and Health SciencesNational Frontiers Science Center for Industrial Intelligence and Systems OptimizationNortheastern UniversityShenyang110169China
- Key Laboratory of Data Analytics and Optimization for Smart IndustryMinistry of EducationNortheastern UniversityShenyang110169China
| | - Lihui Wang
- Department of PharmacologyShenyang Pharmaceutical UniversityShenyang110016China
| | - Yue Hou
- Key Laboratory of Bioresource Research and Development of Liaoning ProvinceCollege of Life and Health SciencesNational Frontiers Science Center for Industrial Intelligence and Systems OptimizationNortheastern UniversityShenyang110169China
- Key Laboratory of Data Analytics and Optimization for Smart IndustryMinistry of EducationNortheastern UniversityShenyang110169China
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Cheng F, Wang F, Tang J, Zhou Y, Fu Z, Zhang P, Haines JL, Leverenz JB, Gan L, Hu J, Rosen-Zvi M, Pieper AA, Cummings J. Artificial intelligence and open science in discovery of disease-modifying medicines for Alzheimer's disease. Cell Rep Med 2024; 5:101379. [PMID: 38382465 PMCID: PMC10897520 DOI: 10.1016/j.xcrm.2023.101379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 08/15/2023] [Accepted: 12/19/2023] [Indexed: 02/23/2024]
Abstract
The high failure rate of clinical trials in Alzheimer's disease (AD) and AD-related dementia (ADRD) is due to a lack of understanding of the pathophysiology of disease, and this deficit may be addressed by applying artificial intelligence (AI) to "big data" to rapidly and effectively expand therapeutic development efforts. Recent accelerations in computing power and availability of big data, including electronic health records and multi-omics profiles, have converged to provide opportunities for scientific discovery and treatment development. Here, we review the potential utility of applying AI approaches to big data for discovery of disease-modifying medicines for AD/ADRD. We illustrate how AI tools can be applied to the AD/ADRD drug development pipeline through collaborative efforts among neurologists, gerontologists, geneticists, pharmacologists, medicinal chemists, and computational scientists. AI and open data science expedite drug discovery and development of disease-modifying therapeutics for AD/ADRD and other neurodegenerative diseases.
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Affiliation(s)
- Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA; Cleveland Clinic Genome Center, 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.
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medical College, Cornell University, New York, NY 10065, USA
| | - Jian Tang
- Mila-Quebec Institute for Learning Algorithms and CIFAR AI Research Chair, HEC Montreal, Montréal, QC H3T 2A7, Canada
| | - Yadi Zhou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Zhimin Fu
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA; College of Pharmacy, Northeast Ohio Medical University, Rootstown, OH 44272, USA
| | - Pengyue Zhang
- Department of Biostatistics and Health Data Science, Indiana University, Indianapolis, IN 46037, USA
| | - Jonathan L Haines
- Cleveland Institute for Computational Biology, and Department of Population & Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH 44106, USA
| | - James B Leverenz
- Lou Ruvo Center for Brain Health, Neurological Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Li Gan
- Helen and Robert Appel Alzheimer's Disease Research Institute, Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10021, USA
| | - Jianying Hu
- IBM Research, Yorktown Heights, New York, NY 10598, USA
| | - Michal Rosen-Zvi
- AI for Accelerated Healthcare and Life Sciences Discovery, IBM Research Labs, Haifa 3498825, Israel; Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem 9190500, Israel
| | - Andrew A Pieper
- Brain Health Medicines Center, Harrington Discovery Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, 44106, USA; Department of Psychiatry, Case Western Reserve University, Cleveland, OH 44106, USA; Geriatric Psychiatry, GRECC, Louis Stokes Cleveland VA Medical Center, Cleveland, OH 44106, USA; Institute for Transformative Molecular Medicine, School of Medicine, Case Western Reserve University, Cleveland OH 44106, USA; Department of Pathology, Case Western Reserve University, School of Medicine, Cleveland, OH, 44106, USA; Department of Neurosciences, Case Western Reserve University, School of Medicine, Cleveland, OH 44106, USA
| | - Jeffrey Cummings
- Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Sciences, UNLV, Las Vegas, NV 89154, USA
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8
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Xiang S, Lawrence PJ, Peng B, Chiang C, Kim D, Shen L, Ning X. Modeling Path Importance for Effective Alzheimer's Disease Drug Repurposing. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2024; 29:306-321. [PMID: 38160288 PMCID: PMC11056095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Abstract
Recently, drug repurposing has emerged as an effective and resource-efficient paradigm for AD drug discovery. Among various methods for drug repurposing, network-based methods have shown promising results as they are capable of leveraging complex networks that integrate multiple interaction types, such as protein-protein interactions, to more effectively identify candidate drugs. However, existing approaches typically assume paths of the same length in the network have equal importance in identifying the therapeutic effect of drugs. Other domains have found that same length paths do not necessarily have the same importance. Thus, relying on this assumption may be deleterious to drug repurposing attempts. In this work, we propose MPI (Modeling Path Importance), a novel network-based method for AD drug repurposing. MPI is unique in that it prioritizes important paths via learned node embeddings, which can effectively capture a network's rich structural information. Thus, leveraging learned embeddings allows MPI to effectively differentiate the importance among paths. We evaluate MPI against a commonly used baseline method that identifies anti-AD drug candidates primarily based on the shortest paths between drugs and AD in the network. We observe that among the top-50 ranked drugs, MPI prioritizes 20.0% more drugs with anti-AD evidence compared to the baseline. Finally, Cox proportional-hazard models produced from insurance claims data aid us in identifying the use of etodolac, nicotine, and BBB-crossing ACE-INHs as having a reduced risk of AD, suggesting such drugs may be viable candidates for repurposing and should be explored further in future studies.
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Affiliation(s)
- Shunian Xiang
- Biomedical Informatics Department, The Ohio State University, Columbus, OH 43210, USA
| | - Patrick J. Lawrence
- Biomedical Informatics Department, The Ohio State University, Columbus, OH 43210, USA
| | - Bo Peng
- Computer Science and Engineering Department, The Ohio State University, Columbus, OH 43210, USA
| | - ChienWei Chiang
- Biomedical Informatics Department, The Ohio State University, Columbus, OH 43210, USA
| | - Dokyoon Kim
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Li Shen
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Xia Ning
- Biomedical Informatics Department, The Ohio State University, Columbus, OH 43210, USA
- Computer Science and Engineering Department, The Ohio State University, Columbus, OH 43210, USA
- Translational Data Analytics Institute, The Ohio State University, Columbus, OH 43210, USA
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9
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Wang X, Fu X, Luo X, Lai Y, Cai C, Liao Y, Dai Z, Fang S, Fang J. Network Proximity Analysis Deciphers the Pharmacological Mechanism of Osthole against D-Galactose Induced Cognitive Disorder in Rats. Molecules 2023; 29:21. [PMID: 38202603 PMCID: PMC10779601 DOI: 10.3390/molecules29010021] [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: 11/11/2023] [Revised: 12/07/2023] [Accepted: 12/12/2023] [Indexed: 01/12/2024] Open
Abstract
Osthole, a natural coumarin found in various medicinal plants, has been previously reported to have neuroprotective effects. However, the specific mechanism by which Osthole alleviates dysmnesia associated with Alzheimer's disease (AD) remains unclear. This study aimed to investigate the neuroprotective properties of Osthole against cognitive impairment in rats induced by D-galactose and elucidate its pharmacological mechanism. The rat model was established by subcutaneously injecting D-galactose at a dose of 150 mg/kg/day for 56 days. The effect of Osthole on cognitive impairment was evaluated by behavior and biochemical analysis. Subsequently, a combination of in silico prediction and experimental validation was performed to verify the network-based predictions, using western blot, Nissl staining, and immunofluorescence. The results demonstrate that Osthole could improve memory dysfunction induced by D-galactose in Sprague Dawley male rats. A network proximity-based approach and integrated pathways analysis highlight two key AD-related pathological processes that may be regulated by Osthole, including neuronal apoptosis, i.e., neuroinflammation. Among them, the pro-apoptotic markers (Bax), anti-apoptotic protein (Bcl-2), the microgliosis (Iba-1), Astro-cytosis (GFAP), and inflammatory cytokines (TNF-R1) were evaluated in both hippocampus and cortex. The results indicated that Osthole significantly ameliorated neuronal apoptosis and neuroinflammation in D-galactose-induced cognitive impairment rats. In conclusion, this study sheds light on the pharmacological mechanism of Osthole in mitigating D-galactose-induced memory impairment and identifies Osthole as a potential drug candidate for AD treatment, targeting multiple signaling pathways through network proximity and integrated pathways analysis.
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Affiliation(s)
- Xue Wang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou 510405, China; (X.W.); (X.F.); (X.L.); (Y.L.); (Y.L.); (Z.D.)
| | - Xiaomei Fu
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou 510405, China; (X.W.); (X.F.); (X.L.); (Y.L.); (Y.L.); (Z.D.)
| | - Xiurong Luo
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou 510405, China; (X.W.); (X.F.); (X.L.); (Y.L.); (Y.L.); (Z.D.)
| | - Yiyi Lai
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou 510405, China; (X.W.); (X.F.); (X.L.); (Y.L.); (Y.L.); (Z.D.)
| | - Chuipu Cai
- Division of Data Intelligence, Department of Computer Science, Key Laboratory of Intelligent Manufacturing Technology of Ministry of Education, College of Engineering, Shantou University, Shantou 515063, China;
| | - Yanfang Liao
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou 510405, China; (X.W.); (X.F.); (X.L.); (Y.L.); (Y.L.); (Z.D.)
| | - Zhao Dai
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou 510405, China; (X.W.); (X.F.); (X.L.); (Y.L.); (Y.L.); (Z.D.)
| | - Shuhuan Fang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou 510405, China; (X.W.); (X.F.); (X.L.); (Y.L.); (Y.L.); (Z.D.)
| | - Jiansong Fang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou 510405, China; (X.W.); (X.F.); (X.L.); (Y.L.); (Y.L.); (Z.D.)
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10
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Xiang S, Lawrence PJ, Peng B, Chiang C, Kim D, Shen L, Ning X. Modeling Path Importance for Effective Alzheimer's Disease Drug Repurposing. ARXIV 2023:arXiv:2310.15211v2. [PMID: 37961739 PMCID: PMC10635281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Recently, drug repurposing has emerged as an effective and resource-efficient paradigm for AD drug discovery. Among various methods for drug repurposing, network-based methods have shown promising results as they are capable of leveraging complex networks that integrate multiple interaction types, such as protein-protein interactions, to more effectively identify candidate drugs. However, existing approaches typically assume paths of the same length in the network have equal importance in identifying the therapeutic effect of drugs. Other domains have found that same length paths do not necessarily have the same importance. Thus, relying on this assumption may be deleterious to drug repurposing attempts. In this work, we propose MPI (Modeling Path Importance), a novel network-based method for AD drug repurposing. MPI is unique in that it prioritizes important paths via learned node embeddings, which can effectively capture a network's rich structural information. Thus, leveraging learned embeddings allows MPI to effectively differentiate the importance among paths. We evaluate MPI against a commonly used baseline method that identifies anti-AD drug candidates primarily based on the shortest paths between drugs and AD in the network. We observe that among the top-50 ranked drugs, MPI prioritizes 20.0% more drugs with anti-AD evidence compared to the baseline. Finally, Cox proportional-hazard models produced from insurance claims data aid us in identifying the use of etodolac, nicotine, and BBB-crossing ACE-INHs as having a reduced risk of AD, suggesting such drugs may be viable candidates for repurposing and should be explored further in future studies.
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Affiliation(s)
- Shunian Xiang
- Biomedical Informatics Department, The Ohio State University,
Columbus, OH 43210, USA
| | - Patrick J. Lawrence
- Biomedical Informatics Department, The Ohio State University,
Columbus, OH 43210, USA
| | - Bo Peng
- Computer Science and Engineering Department, The Ohio State
University, Columbus, OH 43210, USA
| | - ChienWei Chiang
- Biomedical Informatics Department, The Ohio State University,
Columbus, OH 43210, USA
| | - Dokyoon Kim
- Department of Biostatistics, Epidemiology, and Informatics,
University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Li Shen
- Department of Biostatistics, Epidemiology, and Informatics,
University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Xia Ning
- Biomedical Informatics Department, The Ohio State University,
Columbus, OH 43210, USA
- Computer Science and Engineering Department, The Ohio State
University, Columbus, OH 43210, USA
- Translational Data Analytics Institute, The Ohio State
University, Columbus, OH 43210, USA
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11
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Chakkittukandiyil A, Chakraborty S, Kothandan R, Rymbai E, Muthu SK, Vasu S, Sajini DV, Sugumar D, Mohammad ZB, Jayaram S, Rajagopal K, Ramachandran V, Selvaraj D. Side effects based network construction and drug repositioning of ropinirole as a potential molecule for Alzheimer's disease: an in-silico, in-vitro, and in-vivo study. J Biomol Struct Dyn 2023:1-15. [PMID: 37723871 DOI: 10.1080/07391102.2023.2258968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 09/08/2023] [Indexed: 09/20/2023]
Abstract
Alzheimer's disease (AD) is the leading cause of dementia in older adults. Drug repositioning is a process of finding new therapeutic applications for existing drugs. One of the methods in drug repositioning is to use the side-effect profile of a drug to identify a new therapeutic indication. The drugs with similar side-effects may act on similar biological targets and could affect the same biochemical process. In this study, we explored the Food and Drug Administration-approved drugs using PROMISCUOUS database to find those that have adverse effects profile comparable with the ligands being studied or used to treat AD. Here, we found that the ropinirole, a dopamine receptor agonist, shared a maximum number of side-effects with the drugs proven beneficial for treating AD. Furthermore, molecular modelling demonstrated that ropinirole exhibited strong binding affinity (-9.313 kcal/mol) and best ligand efficiency (0.49) with sigma-1 receptor. Here, we observed that the quaternary amino group of ropinirole is essential for binding with sigma-1 receptor. Molecular dynamic simulation indicated that the movement of the carboxy-terminal helices (α4/α5) could play a major role in the receptor's physiological functions. The neurotoxicity induced by Aβ25-35 in SH-SY5Y cells was reduced by ropinirole at concentrations 10, 30, and 50 µM. The effect on spatial learning and memory was examined in mice with Aβ25-35 induced memory deficit using the radial arm maze. Ropinirole (10 and 20 mg/kg) significantly improved the short and long-term memories in the radial arm maze test. Our results suggest that ropinirole has the potential to be repositioned for AD treatment.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Amritha Chakkittukandiyil
- Department of Pharmacology, JSS College of Pharmacy, JSS Academy of Higher Education & Research, Ooty, Nilgiris, Tamil Nadu, India
| | - Saurav Chakraborty
- Department of Pharmacology, JSS College of Pharmacy, JSS Academy of Higher Education & Research, Ooty, Nilgiris, Tamil Nadu, India
| | - Ram Kothandan
- Bioinformatics Laboratory, Department of Biotechnology, Kumaraguru College of Technology, Coimbatore, Tamil Nadu, India
| | - Emdormi Rymbai
- Department of Pharmacology, JSS College of Pharmacy, JSS Academy of Higher Education & Research, Ooty, Nilgiris, Tamil Nadu, India
| | - Santhosh Kumar Muthu
- Department of Biochemistry, Kongunadu Arts and Science College, GN Mills, Coimbatore, Tamil Nadu, India
| | - Soumya Vasu
- Department of Pharmaceutical Chemistry, Sri Ramachandra Institute of Higher Education & Research, Porur, Chennai, Tamil Nadu, India
| | - Deepak Vasudevan Sajini
- Department of Pharmacology, JSS College of Pharmacy, JSS Academy of Higher Education & Research, Ooty, Nilgiris, Tamil Nadu, India
| | - Deepa Sugumar
- Department of Pharmacology, JSS College of Pharmacy, JSS Academy of Higher Education & Research, Ooty, Nilgiris, Tamil Nadu, India
| | - Zubair Baba Mohammad
- Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education & Research, Ooty, Nilgiris, Tamil Nadu, India
| | - Saravanan Jayaram
- Department of Pharmacology, JSS College of Pharmacy, JSS Academy of Higher Education & Research, Ooty, Nilgiris, Tamil Nadu, India
| | - Kalirajan Rajagopal
- Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education & Research, Ooty, Nilgiris, Tamil Nadu, India
| | - Vadivelan Ramachandran
- Department of Pharmacology, JSS College of Pharmacy, JSS Academy of Higher Education & Research, Ooty, Nilgiris, Tamil Nadu, India
| | - Divakar Selvaraj
- Department of Pharmacology, JSS College of Pharmacy, JSS Academy of Higher Education & Research, Ooty, Nilgiris, Tamil Nadu, India
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12
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Hainsworth AH, Arancio O, Elahi FM, Isaacs JD, Cheng F. PDE5 inhibitor drugs for use in dementia. ALZHEIMER'S & DEMENTIA (NEW YORK, N. Y.) 2023; 9:e12412. [PMID: 37766832 PMCID: PMC10520293 DOI: 10.1002/trc2.12412] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 07/07/2023] [Accepted: 07/08/2023] [Indexed: 09/29/2023]
Abstract
Alzheimer's disease and related dementias (ADRD) remain a major health-care challenge with few licensed medications. Repurposing existing drugs may afford prevention and treatment. Phosphodiesterase-5 (PDE5) is widely expressed in vascular myocytes, neurons, and glia. Potent, selective, Food and Drug Administration-approved PDE5 inhibitors are already in clinical use (sildenafil, vardenafil, tadalafil) as vasodilators in erectile dysfunction and pulmonary arterial hypertension. Animal data indicate cognitive benefits of PDE5 inhibitors. In humans, real-world patient data suggest that sildenafil and vardenafil are associated with reduced dementia risk. While a recent clinical trial of acute tadalafil on cerebral blood flow was neutral, there may be chronic actions of PDE5 inhibition on cerebrovascular or synaptic function. We provide a perspective on the potential utility of PDE5 inhibitors for ADRD. We conclude that further prospective clinical trials with PDE5 inhibitors are warranted. The choice of drug will depend on brain penetration, tolerability in older people, half-life, and off-target effects. HIGHLIGHTS Potent phosphodiesterase-5 (PDE5) inhibitors are in clinical use as vasodilators.In animals PDE5 inhibitors enhance synaptic function and cognitive ability.In humans the PDE5 inhibitor sildenafil is associated with reduced risk of Alzheimer's disease.Licensed PDE5 inhibitors have potential for repurposing in dementia.Prospective clinical trials of PDE5 inhibitors are warranted.
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Affiliation(s)
- Atticus H. Hainsworth
- Molecular & Clinical Sciences Research InstituteSt George's University of LondonLondonUK
- Department of NeurologySt George's University Hospitals NHS Foundation TrustLondonUK
| | - Ottavio Arancio
- Department of Pathology and Cell BiologyTaub Institute for Research on Alzheimer's Disease and the Aging BrainDepartment of MedicineColumbia UniversityNew YorkNew YorkUSA
| | - Fanny M. Elahi
- Departments of Neurology and NeuroscienceRonald M. Loeb Center for Alzheimer's DiseaseFriedman Brain InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Jeremy D. Isaacs
- Molecular & Clinical Sciences Research InstituteSt George's University of LondonLondonUK
- Department of NeurologySt George's University Hospitals NHS Foundation TrustLondonUK
| | - Feixiong Cheng
- Genomic Medicine InstituteLerner Research InstituteCleveland ClinicClevelandOhioUSA
- Department of Molecular MedicineCleveland Clinic Lerner College of MedicineCase Western Reserve UniversityClevelandOhioUSA
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13
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Arora S, Santiago JA, Bernstein M, Potashkin JA. Diet and lifestyle impact the development and progression of Alzheimer's dementia. Front Nutr 2023; 10:1213223. [PMID: 37457976 PMCID: PMC10344607 DOI: 10.3389/fnut.2023.1213223] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 06/08/2023] [Indexed: 07/18/2023] Open
Abstract
Dementia is a growing public health concern, with an estimated prevalence of 57 million adults worldwide. Alzheimer's disease (AD) accounts for 60-80% of the cases. Clinical trials testing potential drugs and neuroprotective agents have proven futile, and currently approved drugs only provide symptomatic benefits. Emerging epidemiological and clinical studies suggest that lifestyle changes, including diet and physical activity, offer an alternative therapeutic route for slowing and preventing cognitive decline and dementia. Age is the single most common risk factor for dementia, and it is associated with slowing cellular bioenergetics and metabolic processes. Therefore, a nutrient-rich diet is critical for optimal brain health. Furthermore, type 2 diabetes (T2D) is a risk factor for AD, and diets that reduce the risk of T2D may confer neuroprotection. Foods predominant in Mediterranean, MIND, and DASH diets, including fruits, leafy green vegetables, fish, nuts, and olive oil, may prevent or slow cognitive decline. The mechanisms by which these nutrients promote brain health, however, are not yet completely understood. Other dietary approaches and eating regimes, including ketogenic and intermittent fasting, are also emerging as beneficial for brain health. This review summarizes the pathophysiology, associated risk factors, and the potential neuroprotective pathways activated by several diets and eating regimes that have shown promising results in promoting brain health and preventing dementia.
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Affiliation(s)
- Sarah Arora
- Center for Neurodegenerative Diseases and Therapeutics, Cellular and Molecular Pharmacology Discipline, The Chicago Medical School, Rosalind Franklin University of Medicine and Science, North Chicago, IL, United States
| | | | - Melissa Bernstein
- Department of Nutrition, College of Health Professions, The Chicago Medical School, Rosalind Franklin University of Medicine and Science, North Chicago, IL, United States
| | - Judith A. Potashkin
- Center for Neurodegenerative Diseases and Therapeutics, Cellular and Molecular Pharmacology Discipline, The Chicago Medical School, Rosalind Franklin University of Medicine and Science, North Chicago, IL, United States
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14
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Wang Y, Li M, Kazis LE, Xia W. The Comparative Effectiveness of Monotherapy and Combination Therapies: Impact of Angiotensin Receptor Blockers on the Onset of Alzheimer's Disease. JAR LIFE 2023; 12:35-45. [PMID: 37441415 PMCID: PMC10333644 DOI: 10.14283/jarlife.2023.8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 04/28/2023] [Indexed: 07/15/2023]
Abstract
Background The criteria for use of Alzheimer's disease (AD) drug Leqembi recommended by the Department of Veterans Affairs (VA) include patients aged 65 years or older with mild cognitive impairment (MCI) or mild AD. Comorbidities that include hypertension, hyperlipidemia, and diabetes are common among these patients. Objectives Our objective is to investigate the comparative effectiveness of the administration of one, two, or three medications belonging to the categories of angiotensin receptor blockers (ARBs), angiotensin-converting enzyme inhibitors (ACEIs), Beta Blockers, Statins, and Metformin, for their potential to delay the clinical onset of AD and provide a window of opportunity for therapeutic intervention. Design Retrospective matched case-control study. Setting Data from the Department of Veterans Affairs national corporate data warehouse. Participants We conducted an analysis of 122,351 participants (13,611 with AD and 108,740 without AD), aged 65-89, who began at least one of the prescribed medication classes under investigation between October 1998 and April 2018. Measurements We utilized Cox proportional hazard regressions, both with and without propensity score weighting, to estimate hazard ratios (HR) associated with the use of different medication combinations for the pre-symptomatic survival time of AD onset. Additionally, we employed a supervised machine learning algorithm (random forest) to assess the relative importance of various therapies in predicting the occurrence of AD. Result Adding Metformin to the combination of ACEI+Beta Blocker (HR = 0.56, 95% CI (0.41, 0.77)) reduced the risk of AD onset compared to ACEI monotherapy alone (HR = 0.91, (0.85, 0.98)), Beta Blocker monotherapy (HR = 0.86, 95% CI (0.80, 0.92)), or combined ACEI+Beta Blocker (HR=0.85, 95%CI (0.77, 0.94)), when statin prescribers were used as a reference. Prescriptions of ARB alone or the combination of ARB with Beta Blocker showed an association with a lower risk of AD onset. Conclusion Selected medications for the treatment of multiple chronic conditions among elderly individuals with hypertension, hyperlipidemia, and diabetes as monotherapy or combination therapies lengthen the pre-symptomatic period before the onset of AD.
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Affiliation(s)
- Y Wang
- Geriatric Research Education and Clinical Center, Bedford VA Healthcare System, Bedford, MA, USA
- Department of Mathematical Sciences, Bentley University, Waltham, MA, USA
| | - M Li
- Department of Mathematical Sciences, Bentley University, Waltham, MA, USA
- Center for Healthcare Organization and Implementation Research, Bedford VA Healthcare System, Bedford, MA, USA
| | - L E Kazis
- Department of Health Law, Policy and Management, Boston University School of Public Health, Boston, MA, USA
- Harvard Medical School and Rehabilitation Outcomes Center (ROC), Spaulding Rehabilitation Hospital, Boston, MA, USA
| | - W Xia
- Geriatric Research Education and Clinical Center, Bedford VA Healthcare System, Bedford, MA, USA
- Department of Pharmacology, Physiology & Biophysics, Boston University School of Medicine, Boston, MA, USA
- Department of Biological Sciences, University of Massachusetts, Lowell, MA, USA
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15
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Walgrave H, Penning A, Tosoni G, Snoeck S, Davie K, Davis E, Wolfs L, Sierksma A, Mars M, Bu T, Thrupp N, Zhou L, Moechars D, Mancuso R, Fiers M, Howden AJ, De Strooper B, Salta E. microRNA-132 regulates gene expression programs involved in microglial homeostasis. iScience 2023; 26:106829. [PMID: 37250784 PMCID: PMC10213004 DOI: 10.1016/j.isci.2023.106829] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 03/13/2023] [Accepted: 05/03/2023] [Indexed: 05/31/2023] Open
Abstract
microRNA-132 (miR-132), a known neuronal regulator, is one of the most robustly downregulated microRNAs (miRNAs) in the brain of Alzheimer's disease (AD) patients. Increasing miR-132 in AD mouse brain ameliorates amyloid and Tau pathologies, and also restores adult hippocampal neurogenesis and memory deficits. However, the functional pleiotropy of miRNAs requires in-depth analysis of the effects of miR-132 supplementation before it can be moved forward for AD therapy. We employ here miR-132 loss- and gain-of-function approaches using single-cell transcriptomics, proteomics, and in silico AGO-CLIP datasets to identify molecular pathways targeted by miR-132 in mouse hippocampus. We find that miR-132 modulation significantly affects the transition of microglia from a disease-associated to a homeostatic cell state. We confirm the regulatory role of miR-132 in shifting microglial cell states using human microglial cultures derived from induced pluripotent stem cells.
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Affiliation(s)
- Hannah Walgrave
- VIB-KU Leuven Center for Brain & Disease Research, 3000 Leuven, Belgium
- KU Leuven, Department of Neurosciences, Leuven Brain Institute (LBI), 3000 Leuven, Belgium
| | - Amber Penning
- Netherlands Institute for Neuroscience, 1105 BA Amsterdam, the Netherlands
| | - Giorgia Tosoni
- Netherlands Institute for Neuroscience, 1105 BA Amsterdam, the Netherlands
| | - Sarah Snoeck
- Netherlands Institute for Neuroscience, 1105 BA Amsterdam, the Netherlands
| | - Kristofer Davie
- VIB-KU Leuven Center for Brain & Disease Research, 3000 Leuven, Belgium
- VIB-KU Leuven Center for Brain & Disease Research, Bioinformatics Core Facility, 3000 Leuven, Belgium
| | - Emma Davis
- UK Dementia Research Institute at UCL, London WC1E 6BT, UK
| | - Leen Wolfs
- VIB-KU Leuven Center for Brain & Disease Research, 3000 Leuven, Belgium
- KU Leuven, Department of Neurosciences, Leuven Brain Institute (LBI), 3000 Leuven, Belgium
| | - Annerieke Sierksma
- VIB-KU Leuven Center for Brain & Disease Research, 3000 Leuven, Belgium
- KU Leuven, Department of Neurosciences, Leuven Brain Institute (LBI), 3000 Leuven, Belgium
| | - Mayte Mars
- Netherlands Institute for Neuroscience, 1105 BA Amsterdam, the Netherlands
| | - Taofeng Bu
- VIB-KU Leuven Center for Brain & Disease Research, 3000 Leuven, Belgium
- KU Leuven, Department of Neurosciences, Leuven Brain Institute (LBI), 3000 Leuven, Belgium
| | - Nicola Thrupp
- VIB-KU Leuven Center for Brain & Disease Research, 3000 Leuven, Belgium
- KU Leuven, Department of Neurosciences, Leuven Brain Institute (LBI), 3000 Leuven, Belgium
| | - Lujia Zhou
- Discovery Neuroscience, Janssen Research and Development, Division of Janssen Pharmaceutica NV, 2340 Beerse, Belgium
| | - Diederik Moechars
- Discovery Neuroscience, Janssen Research and Development, Division of Janssen Pharmaceutica NV, 2340 Beerse, Belgium
| | - Renzo Mancuso
- Microglia and Inflammation in Neurological Disorders (MIND) Lab, VIB Center for Molecular Neurology, VIB, 2610 Antwerp, Belgium
- Department of Biomedical Sciences, University of Antwerp, 2610 Antwerp, Belgium
| | - Mark Fiers
- VIB-KU Leuven Center for Brain & Disease Research, 3000 Leuven, Belgium
- KU Leuven, Department of Neurosciences, Leuven Brain Institute (LBI), 3000 Leuven, Belgium
| | - Andrew J.M. Howden
- UK Dementia Research Institute, University of Dundee, Dundee DD1 4HN, UK
| | - Bart De Strooper
- VIB-KU Leuven Center for Brain & Disease Research, 3000 Leuven, Belgium
- KU Leuven, Department of Neurosciences, Leuven Brain Institute (LBI), 3000 Leuven, Belgium
- UK Dementia Research Institute at UCL, London WC1E 6BT, UK
| | - Evgenia Salta
- Netherlands Institute for Neuroscience, 1105 BA Amsterdam, the Netherlands
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16
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Zhang P, Hou Y, Tu W, Campbell N, Pieper AA, Leverenz JB, Gao S, Cummings J, Cheng F. Population-based discovery and Mendelian randomization analysis identify telmisartan as a candidate medicine for Alzheimer's disease in African Americans. Alzheimers Dement 2023; 19:1876-1887. [PMID: 36331056 PMCID: PMC10156891 DOI: 10.1002/alz.12819] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 08/11/2022] [Accepted: 09/02/2022] [Indexed: 11/06/2022]
Abstract
INTRODUCTION African Americans (AAs) and European Americans (EAs) differ in Alzheimer's disease (AD) prevalence, risk factors, and symptomatic presentation and AAs are less likely to enroll in AD clinical trials. METHODS We conducted race-conscious pharmacoepidemiologic studies of 5.62 million older individuals (age ≥60) to investigate the association of telmisartan exposure and AD outcome using Cox analysis, Kaplan-Meier analysis, and log-rank test. We performed Mendelian randomization (MR) analysis of large ethnically diverse genetic data to test likely causal relationships between telmisartan's target and AD. RESULTS We identified that moderate/high telmisartan exposure was significantly associated with a reduced incidence of AD in the AAs compared to low/no telmisartan exposure (hazard ratio [HR] = 0.77, 95% CI: 0.65-0.91, p-value = 0.0022), but not in the non-Hispanic EAs (HR = 0.97, 95% CI: 0.89-1.05, p-value = 0.4110). Sensitivity and sex-/age-stratified patient subgroup analyses identified that telmisartan's medication possession ratio (MPR) and average hypertension daily dosage were significantly associated with a stronger reduction in the incidence of both AD and dementia in AAs. Using MR analysis from large genome-wide association studies (GWAS) (over 2 million individuals) across AD, hypertension, and diabetes, we further identified AA-specific beneficial effects of telmisartan for AD. DISCUSSION Randomized controlled trials with ethnically diverse patient cohorts are warranted to establish causality and therapeutic outcomes of telmisartan and AD. HIGHLIGHTS Telmisartan is associated with lower risk of Alzheimer's disease (AD) in African Americans (AAs). Telmisartan is the only angiotensin II receptor blockers having PPAR-γ agonistic properties with beneficial anti-diabetic and renal function effects, which mitigate AD risk in AAs. Mendelian randomization (MR) analysis demonstrates the specificity of telmisartan's protective mechanism to AAs.
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Affiliation(s)
- Pengyue Zhang
- Department of Biostatistics and Health Data Science, Indiana University, Indianapolis, Indiana, USA
| | - Yuan Hou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | - Wanzhu Tu
- Department of Biostatistics and Health Data Science, Indiana University, Indianapolis, Indiana, USA
| | - Noll Campbell
- Department of Pharmacy Practice, Purdue University, West Lafayette, Indiana, USA
| | - Andrew A. Pieper
- Harrington Discovery Institute, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA
- Department of Psychiatry, Case Western Reserve University, Cleveland, Ohio, USA
- Geriatric Psychiatry, GRECC, Louis Stokes Cleveland VA Medical Center, Cleveland, Ohio, USA
- Institute for Transformative Molecular Medicine, School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA
- Department of Neuroscience, Case Western Reserve University, School of Medicine, Cleveland, Ohio, USA
| | - James B. Leverenz
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio, USA
- Lou Ruvo Center for Brain Health, Neurological Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | - Sujuan Gao
- Department of Biostatistics and Health Data Science, Indiana University, Indianapolis, Indiana, USA
| | - Jeffrey Cummings
- Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Sciences, University of Nevada Las Vegas, Las Vegas, Nevada, USA
| | - 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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17
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Pérez-Cano L, Azidane Chenlo S, Sabido-Vera R, Sirci F, Durham L, Guney E. Translating precision medicine for autism spectrum disorder: A pressing need. Drug Discov Today 2023; 28:103486. [PMID: 36623795 DOI: 10.1016/j.drudis.2023.103486] [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: 08/18/2022] [Revised: 12/01/2022] [Accepted: 01/03/2023] [Indexed: 01/09/2023]
Abstract
Autism spectrum disorder (ASD) is a heterogenous group of neurodevelopmental disorders (NDDs) with a high unmet medical need. Currently, ASD is diagnosed according to behavior-based criteria that overlook clinical and genomic heterogeneity, thus repeatedly resulting in failed clinical trials. Here, we summarize the scientific evidence pointing to the pressing need to create a precision medicine framework for ASD and other NDDs. We discuss the role of omics and systems biology to characterize more homogeneous disease subtypes with different underlying pathophysiological mechanisms and to determine corresponding tailored treatments. Finally, we provide recent initiatives towards tackling the complexity in NDDs for precision medicine and cost-effective drug discovery.
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Affiliation(s)
- Laura Pérez-Cano
- Discovery and Data Science (DDS) Unit, STALICLA SL, Moll de Barcelona, s/n, Edif Este, 08039 Barcelona, Spain
| | - Sara Azidane Chenlo
- Discovery and Data Science (DDS) Unit, STALICLA SL, Moll de Barcelona, s/n, Edif Este, 08039 Barcelona, Spain
| | - Rubén Sabido-Vera
- Discovery and Data Science (DDS) Unit, STALICLA SL, Moll de Barcelona, s/n, Edif Este, 08039 Barcelona, Spain
| | - Francesco Sirci
- Discovery and Data Science (DDS) Unit, STALICLA SL, Moll de Barcelona, s/n, Edif Este, 08039 Barcelona, Spain
| | - Lynn Durham
- Discovery and Data Science (DDS) Unit, STALICLA SL, Moll de Barcelona, s/n, Edif Este, 08039 Barcelona, Spain; Drug Development Unit (DDU), STALICLA SA, Avenue de Sécheron 15, 1202 Geneva, Switzerland.
| | - Emre Guney
- Discovery and Data Science (DDS) Unit, STALICLA SL, Moll de Barcelona, s/n, Edif Este, 08039 Barcelona, Spain.
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18
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Qin S, Li W, Yu H, Xu M, Li C, Fu L, Sun S, He Y, Lv J, He W, Chen L. Guiding Drug Repositioning for Cancers Based on Drug Similarity Networks. Int J Mol Sci 2023; 24:ijms24032244. [PMID: 36768566 PMCID: PMC9917231 DOI: 10.3390/ijms24032244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Revised: 01/05/2023] [Accepted: 01/16/2023] [Indexed: 01/24/2023] Open
Abstract
Drug repositioning aims to discover novel clinical benefits of existing drugs, is an effective way to develop drugs for complex diseases such as cancer and may facilitate the process of traditional drug development. Meanwhile, network-based computational biology approaches, which allow the integration of information from different aspects to understand the relationships between biomolecules, has been successfully applied to drug repurposing. In this work, we developed a new strategy for network-based drug repositioning against cancer. Combining the mechanism of action and clinical efficacy of the drugs, a cancer-related drug similarity network was constructed, and the correlation score of each drug with a specific cancer was quantified. The top 5% of scoring drugs were reviewed for stability and druggable potential to identify potential repositionable drugs. Of the 11 potentially repurposable drugs for non-small cell lung cancer (NSCLC), 10 were confirmed by clinical trial articles and databases. The targets of these drugs were significantly enriched in cancer-related pathways and significantly associated with the prognosis of NSCLC. In light of the successful application of our approach to colorectal cancer as well, it provides an effective clue and valuable perspective for drug repurposing in cancer.
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Affiliation(s)
- Shimei Qin
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Wan Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Hongzheng Yu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Manyi Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Chao Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Lei Fu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Shibin Sun
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Yuehan He
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Junjie Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Weiming He
- Institute of Opto-Electronics, Harbin Institute of Technology, Harbin 150001, China
| | - Lina Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
- Correspondence: ; Tel.: +86-451-8667-4768
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19
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Liao Y, Wang X, Huang L, Qian H, Liu W. Mechanism of pyroptosis in neurodegenerative diseases and its therapeutic potential by traditional Chinese medicine. Front Pharmacol 2023; 14:1122104. [PMID: 36713841 PMCID: PMC9880437 DOI: 10.3389/fphar.2023.1122104] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 01/03/2023] [Indexed: 01/15/2023] Open
Abstract
Neurodegenerative diseases (NDs) are disorders characterized by degenerative degeneration of neurons and loss of their function. NDs have a complicated pathophysiology, of which neuroinflammation and neuronal death are significant factors. The inflammatory process known as pyroptosis ("fiery death") is caused by a family of pore-forming proteins called Gasdermins (GSDMs), which appears downstream from the activation of the inflammasome. Clear evidence of enhanced pyroptosis-related proteins activity in common NDs has coincided with abnormal aggregation of pathological proteins (such as Aβ, tau, α-synuclein et al.), making pyroptosis an attractive direction for the recent study of NDs. The purpose of this review is to provide an overview of the molecular mechanisms driving pyroptosis, the mechanistic links between pyroptosis and NDs, and emerging therapeutic strategies in Traditional Chinese Medicine (TCM) to inhibit pyroptosis for the treatment of NDs.
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Affiliation(s)
- Yanfang Liao
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Xue Wang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Liting Huang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Hu Qian
- Department of Breast Cancer Oncology, Foshan No 1 Hospital, Foshan, China,*Correspondence: Hu Qian, ; Wei Liu,
| | - Wei Liu
- The First Clinical Medicine College of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China,Integrative Cancer Centre, The First Affiliated Hospital of Guangzhou, University of Chinese Medicine, Guangzhou, China,*Correspondence: Hu Qian, ; Wei Liu,
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20
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Zhu J, Liang Q, He S, Wang C, Lin X, Wu D, Lin G, Wang Z. Research trends and hotspots of neurodegenerative diseases employing network pharmacology: A bibliometric analysis. Front Pharmacol 2023; 13:1109400. [PMID: 36712694 PMCID: PMC9878685 DOI: 10.3389/fphar.2022.1109400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Accepted: 12/28/2022] [Indexed: 01/15/2023] Open
Abstract
Background: Employing network pharmacology in neurodegenerative diseases (NDs) has been extensively studied recently. However, no comprehensive study has conducted on this subject employing bibliometrics so far. The purpose of this study was to find out the developmental trends and hotspots, and to predict potential research directions in this filed. Methods: Relevant research were collected from the Web of Science Core Collection Bibliometrics and visual analysis were executed using CiteSpace, VOSviewer, Histcite and R-bibliometrix. Results: A total of 420 English articles on network pharmacology in NDs published in 2008-2022 were obtained from the WOSCC database. From 2008 to 2022, annual publications showed a steady growing trend, especially in 2014-2022. China, Beijing Univ Chinese Med, Frontiers in Pharmacology, and Geerts H are the most prolific country, institution, journal, and author, respectively. China, Nucleic Acids Research, and Hopkins AL are the most highly cited country, journal, and author, respectively. Moreover, network pharmacology and Alzheimer's disease are the focal areas of current researches according to analysis of co-cited references and keywords. Finally, in the detection of burst keywords, systems pharmacology and database are new approaches to disease and drug research, while traditional Chinese medicine (TCM) and Alzheimer's disease are hot research directions. The above keywords are speculated to be the research frontiers. Conclusion: Network pharmacology and Alzheimers' disease are the main topics of researches on network pharmacology in NDs. Network pharmacology and the TCM treatment of Alzheimer's disease have been the recent research hotspots. To sum up, the potential for exploring TCM treatment of AD with network pharmacology is huge.
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Affiliation(s)
- Jie Zhu
- Department of Anesthesiology, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, Hainan, China
| | - Qingchun Liang
- Department of Anesthesiology, The Third Affiliated Hospital, Southern Medical University, Guangzhou, China
| | - Siyi He
- Department of Anesthesiology, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, Hainan, China
| | - Chen Wang
- Department of Anesthesiology, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, Hainan, China
| | - Xiafei Lin
- Department of Anesthesiology, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, Hainan, China
| | - Duozhi Wu
- Department of Anesthesiology, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, Hainan, China
| | - Guanwen Lin
- Department of Anesthesiology, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, Hainan, China,*Correspondence: Guanwen Lin, ; Zhihua Wang,
| | - Zhihua Wang
- Department of Anesthesiology, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, Hainan, China,*Correspondence: Guanwen Lin, ; Zhihua Wang,
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21
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Wu Q, Su S, Cai C, Xu L, Fan X, Ke H, Dai Z, Fang S, Zhuo Y, Wang Q, Pan H, Gu Y, Fang J. Network Proximity-based computational pipeline identifies drug candidates for different pathological stages of Alzheimer's disease. Comput Struct Biotechnol J 2023; 21:1907-1920. [PMID: 36936813 PMCID: PMC10015208 DOI: 10.1016/j.csbj.2023.02.041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 02/22/2023] [Accepted: 02/22/2023] [Indexed: 02/26/2023] Open
Abstract
Despite the massive investment in Alzheimer's disease (AD), there are still no disease-modifying treatments (DMTs) for AD. One major reason is attributed to the limitation of clinical "one-size-fits-all" approach, since the same AD treatment solely based on clinical diagnosis was unlikely to achieve good clinical efficacy. In recent years, computational approaches based on multiomics data have provided an unprecedented opportunity for drug discovery since they can substantially lower the costs and boost the efficiency. In this study, we intended to identify potential drug candidates for different pathological stages of AD by computationally repurposing Food and Drug Administration (FDA) approved drugs. First, we assembled gene expression data from three different AD pathological stages, which include mild cognitive impairment (MCI) and early and late stages of AD (EAD, LAD). We next quantified the network distances between drug target networks and AD modules by utilizing a network proximity approach, and identified 193 candidates that possessed significant associations with AD. After searching for previous literature evidence, 63 out of 193 (32.6%) predicted drugs were demonstrated to exert therapeutic effects on AD. We further explored the novel mechanism of action (MOA) for these drug candidates by determining the specific brain cells they might function on based on AD patient single cell transcriptomic data. Additionally, we selected several promising candidates that could cross the blood brain barrier together with confirmed neuroprotective effects, and subsequently determined the antioxidative activity of these compounds. Experimental results showed that azathioprine decreased the reactive oxygen species (ROS) and malondialdehyde (MDA) levels and improved the superoxide dismutase (SOD) activity in APP-SH-SY5Y cells. Finally, we deciphered the potential MOA of azathioprine against AD via network analysis and validated several apoptosis-related proteins (Caspase 3, Cleaved Caspase 3, Bax, Bcl2) through western blotting. In summary, this study presented an effective computational strategy utilizing omics data for AD drug repurposing, which provides a new perspective for drug discovery and development.
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Affiliation(s)
- Qihui Wu
- Clinical Research Center, Hainan Provincial Hospital of Traditional Chinese Medicine, Guangzhou University of Chinese Medicine, Haikou, China
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China
- Hainan Clinical Center for Encephalopathy of Chinese Medicine, Haikou, China
- Hainan Clinical Research Center for Preventive Treatment of Diseases, Haikou, China
| | - Shijie Su
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Chuipu Cai
- Department of Computer Science, Key Laboratory of Intelligent Manufacturing Technology of Ministry of Education, Shantou University, Shantou, China
| | - Lina Xu
- Department of Cardiac Surgery, Qingdao Fuwai Cardiovascular Hospital, Qingdao, China
| | - Xiude Fan
- Department of Infectious Diseases, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Hanzhong Ke
- Department of Cancer Immunology and Virology, Dana-Farber Cancer Institute, Boston, MA, United States
| | - Zhao Dai
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Shuhuan Fang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yue Zhuo
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Qi Wang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Huafeng Pan
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yong Gu
- Clinical Research Center, Hainan Provincial Hospital of Traditional Chinese Medicine, Guangzhou University of Chinese Medicine, Haikou, China
- Hainan Clinical Center for Encephalopathy of Chinese Medicine, Haikou, China
- Hainan Clinical Research Center for Preventive Treatment of Diseases, Haikou, China
- Corresponding author at: Clinical Research Center, Hainan Provincial Hospital of Traditional Chinese Medicine, Guangzhou University of Chinese Medicine, Haikou, China.
| | - Jiansong Fang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China
- Corresponding author.
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22
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Dai Z, Pang X, Fang J. Editorial: Data mining and network-driven analysis: Drug discovery and mechanism exploration by targeting vascular dysfunction. Front Pharmacol 2022; 13:1099854. [PMID: 36582522 PMCID: PMC9794180 DOI: 10.3389/fphar.2022.1099854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 12/05/2022] [Indexed: 12/15/2022] Open
Affiliation(s)
- Zhao Dai
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China,Department of Rheumatology, First Affiliated Hospital of Guangzhou University of Chinese Medicine, Beijing, China
| | - Xiaocong Pang
- Department of Pharmacy, Peking University First Hospital, Beijing, China
| | - Jiansong Fang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China,*Correspondence: Jiansong Fang,
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23
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Clark C, Rabl M, Dayon L, Popp J. The promise of multi-omics approaches to discover biological alterations with clinical relevance in Alzheimer's disease. Front Aging Neurosci 2022; 14:1065904. [PMID: 36570537 PMCID: PMC9768448 DOI: 10.3389/fnagi.2022.1065904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 11/21/2022] [Indexed: 12/12/2022] Open
Abstract
Beyond the core features of Alzheimer's disease (AD) pathology, i.e. amyloid pathology, tau-related neurodegeneration and microglia response, multiple other molecular alterations and pathway dysregulations have been observed in AD. Their inter-individual variations, complex interactions and relevance for clinical manifestation and disease progression remain poorly understood, however. Heterogeneity at both pathophysiological and clinical levels complicates diagnosis, prognosis, treatment and drug design and testing. High-throughput "omics" comprise unbiased and untargeted data-driven methods which allow the exploration of a wide spectrum of disease-related changes at different endophenotype levels without focussing a priori on specific molecular pathways or molecules. Crucially, new methodological and statistical advances now allow for the integrative analysis of data resulting from multiple and different omics methods. These multi-omics approaches offer the unique advantage of providing a more comprehensive characterisation of the AD endophenotype and to capture molecular signatures and interactions spanning various biological levels. These new insights can then help decipher disease mechanisms more deeply. In this review, we describe the different multi-omics tools and approaches currently available and how they have been applied in AD research so far. We discuss how multi-omics can be used to explore molecular alterations related to core features of the AD pathologies and how they interact with comorbid pathological alterations. We further discuss whether the identified pathophysiological changes are relevant for the clinical manifestation of AD, in terms of both cognitive impairment and neuropsychiatric symptoms, and for clinical disease progression over time. Finally, we address the opportunities for multi-omics approaches to help discover novel biomarkers for diagnosis and monitoring of relevant pathophysiological processes, along with personalised intervention strategies in AD.
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Affiliation(s)
- Christopher Clark
- Department of Psychiatry, Psychotherapy and Psychosomatics, University of Zürich, Zürich, Switzerland,Geriatric Psychiatry, University Hospital of Psychiatry Zürich, Zürich, Switzerland,*Correspondence: Christopher Clark,
| | - Miriam Rabl
- Geriatric Psychiatry, University Hospital of Psychiatry Zürich, Zürich, Switzerland,University of Lausanne, Lausanne, Switzerland
| | - Loïc Dayon
- Nestlé Institute of Food Safety and Analytical Sciences, Nestlé Research, Lausanne, Switzerland,Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Julius Popp
- Department of Psychiatry, Psychotherapy and Psychosomatics, University of Zürich, Zürich, Switzerland,Geriatric Psychiatry, University Hospital of Psychiatry Zürich, Zürich, Switzerland,Old Age Psychiatry, Department of Psychiatry, Lausanne University Hospital, Lausanne, Switzerland
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24
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Xu J, Mao C, Hou Y, Luo Y, Binder JL, Zhou Y, Bekris LM, Shin J, Hu M, Wang F, Eng C, Oprea TI, Flanagan ME, Pieper AA, Cummings J, Leverenz JB, Cheng F. Interpretable deep learning translation of GWAS and multi-omics findings to identify pathobiology and drug repurposing in Alzheimer's disease. Cell Rep 2022; 41:111717. [PMID: 36450252 PMCID: PMC9837836 DOI: 10.1016/j.celrep.2022.111717] [Citation(s) in RCA: 22] [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] [Received: 04/29/2022] [Revised: 09/01/2022] [Accepted: 11/02/2022] [Indexed: 12/03/2022] Open
Abstract
Translating human genetic findings (genome-wide association studies [GWAS]) to pathobiology and therapeutic discovery remains a major challenge for Alzheimer's disease (AD). We present a network topology-based deep learning framework to identify disease-associated genes (NETTAG). We leverage non-coding GWAS loci effects on quantitative trait loci, enhancers and CpG islands, promoter regions, open chromatin, and promoter flanking regions under the protein-protein interactome. Via NETTAG, we identified 156 AD-risk genes enriched in druggable targets. Combining network-based prediction and retrospective case-control observations with 10 million individuals, we identified that usage of four drugs (ibuprofen, gemfibrozil, cholecalciferol, and ceftriaxone) is associated with reduced likelihood of AD incidence. Gemfibrozil (an approved lipid regulator) is significantly associated with 43% reduced risk of AD compared with simvastatin using an active-comparator design (95% confidence interval 0.51-0.63, p < 0.0001). In summary, NETTAG offers a deep learning methodology that utilizes GWAS and multi-genomic findings to identify pathobiology and drug repurposing in AD.
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Affiliation(s)
- Jielin Xu
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Chengsheng Mao
- Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Yuan Hou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Yuan Luo
- Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Jessica L Binder
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM 87131, USA
| | - Yadi Zhou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Lynn M Bekris
- 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
| | - Jiyoung Shin
- Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Ming Hu
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44106, USA
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medical College, Cornell University, New York, NY 10065, 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
| | - Tudor I Oprea
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM 87131, USA
| | - Margaret E Flanagan
- Department of Pathology and Mesulam Center for Cognitive Neurology and Alzheimer's Disease, Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Andrew A Pieper
- Harrington Discovery Institute, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA; Department of Psychiatry, Case Western Reserve University, Cleveland, OH 44106, USA; Geriatric Psychiatry, GRECC, Louis Stokes Cleveland VA Medical Center, Cleveland, OH 44106, USA; Institute for Transformative Molecular Medicine, School of Medicine, Case Western Reserve University, Cleveland 44106, OH, USA; Department of Neuroscience, Case Western Reserve University, School of Medicine, Cleveland, OH 44106, USA
| | - Jeffrey Cummings
- Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Sciences, University of Nevada Las Vegas, Las Vegas, NV 89154, USA
| | - James B Leverenz
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA; Lou Ruvo Center for Brain Health, Neurological 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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Zhang L, Fan S, Vera J, Lai X. A network medicine approach for identifying diagnostic and prognostic biomarkers and exploring drug repurposing in human cancer. Comput Struct Biotechnol J 2022; 21:34-45. [PMID: 36514340 PMCID: PMC9732137 DOI: 10.1016/j.csbj.2022.11.037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Revised: 11/18/2022] [Accepted: 11/18/2022] [Indexed: 12/03/2022] Open
Abstract
Cancer is a heterogeneous disease mainly driven by abnormal gene perturbations in regulatory networks. Therefore, it is appealing to identify the common and specific perturbed genes from multiple cancer networks. We developed an integrative network medicine approach to identify novel biomarkers and investigate drug repurposing across cancer types. We used a network-based method to prioritize genes in cancer-specific networks reconstructed using human transcriptome and interactome data. The prioritized genes show extensive perturbation and strong regulatory interaction with other highly perturbed genes, suggesting their vital contribution to tumorigenesis and tumor progression, and are therefore regarded as cancer genes. The cancer genes detected show remarkable performances in discriminating tumors from normal tissues and predicting survival times of cancer patients. Finally, we developed a network proximity approach to systematically screen drugs and identified dozens of candidates with repurposable potential in several cancer types. Taken together, we demonstrated the power of the network medicine approach to identify novel biomarkers and repurposable drugs in multiple cancer types. We have also made the data and code freely accessible to ensure reproducibility and reusability of the developed computational workflow.
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Affiliation(s)
- Le Zhang
- College of Computer Science, Sichuan University, Chengdu, China
| | - Shiwei Fan
- College of Computer Science, Sichuan University, Chengdu, China
| | - Julio Vera
- Laboratory of Systems Tumor Immunology, Department of Dermatology, Universitätsklinikum Erlangen and Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany,Deutsches Zentrum Immuntherapie, Erlangen, Germany,Comprehensive Cancer Center Erlangen, Erlangen, Germany
| | - Xin Lai
- Laboratory of Systems Tumor Immunology, Department of Dermatology, Universitätsklinikum Erlangen and Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany,Deutsches Zentrum Immuntherapie, Erlangen, Germany,Comprehensive Cancer Center Erlangen, Erlangen, Germany,BioMediTech, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland,Corresponding author at: Universitätsklinikum Erlangen, Erlangen, Germany; Tampere University, Tampere, Finland.
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26
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Atlante A, Amadoro G, Latina V, Valenti D. Therapeutic Potential of Targeting Mitochondria for Alzheimer's Disease Treatment. J Clin Med 2022; 11:jcm11226742. [PMID: 36431219 PMCID: PMC9697019 DOI: 10.3390/jcm11226742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 11/11/2022] [Accepted: 11/11/2022] [Indexed: 11/16/2022] Open
Abstract
Alzheimer's disease (AD), a chronic and progressive neurodegenerative disease, is characterized by memory and cognitive impairment and by the accumulation in the brain of abnormal proteins, more precisely beta-amyloid (β-amyloid or Aβ) and Tau proteins. Studies aimed at researching pharmacological treatments against AD have focused precisely on molecules capable, in one way or another, of preventing/eliminating the accumulations of the aforementioned proteins. Unfortunately, more than 100 years after the discovery of the disease, there is still no effective therapy in modifying the biology behind AD and nipping the disease in the bud. This state of affairs has made neuroscientists suspicious, so much so that for several years the idea has gained ground that AD is not a direct neuropathological consequence taking place downstream of the deposition of the two toxic proteins, but rather a multifactorial disease, including mitochondrial dysfunction as an early event in the pathogenesis of AD, occurring even before clinical symptoms. This is the reason why the search for pharmacological agents capable of normalizing the functioning of these subcellular organelles of vital importance for nerve cells is certainly to be considered a promising approach to the design of effective neuroprotective drugs aimed at preserving this organelle to arrest or delay the progression of the disease. Here, our intent is to provide an updated overview of the mitochondrial alterations related to this disorder and of the therapeutic strategies (both natural and synthetic) targeting mitochondrial dysfunction.
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Affiliation(s)
- Anna Atlante
- Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies (IBIOM)-CNR, Via G. Amendola122/O, 70126 Bari, Italy
- Correspondence: (A.A.); (D.V.); Tel.: +39-080-5929804 (A.A.); +39-080-5929805 (D.V.)
| | - Giuseppina Amadoro
- Institute of Translational Pharmacology (IFT)-CNR, Via Fosso del Cavaliere 100, 00133 Rome, Italy
| | - Valentina Latina
- European Brain Research Institute (EBRI), Viale Regina Elena 295, 00161 Rome, Italy
| | - Daniela Valenti
- Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies (IBIOM)-CNR, Via G. Amendola122/O, 70126 Bari, Italy
- Correspondence: (A.A.); (D.V.); Tel.: +39-080-5929804 (A.A.); +39-080-5929805 (D.V.)
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Zhou Y, Xu J, Hou Y, Bekris L, Leverenz JB, Pieper AA, Cummings J, Cheng F. The Alzheimer's Cell Atlas (TACA): A single-cell molecular map for translational therapeutics accelerator in Alzheimer's disease. ALZHEIMER'S & DEMENTIA (NEW YORK, N. Y.) 2022; 8:e12350. [PMID: 36254161 PMCID: PMC9558163 DOI: 10.1002/trc2.12350] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 08/01/2022] [Accepted: 08/15/2022] [Indexed: 12/30/2022]
Abstract
Introduction Recent advances in generating massive single-cell/nucleus transcriptomic data have shown great potential for facilitating the identification of cell type-specific Alzheimer's disease (AD) pathobiology and drug-target discovery for therapeutic development. Methods We developed The Alzheimer's Cell Atlas (TACA) by compiling an AD brain cell atlas consisting of over 1.1 million cells/nuclei across 26 data sets, covering major brain regions (hippocampus, cerebellum, prefrontal cortex, and so on) and cell types (astrocyte, microglia, neuron, oligodendrocytes, and so on). We conducted nearly 1400 differential expression comparisons to identify cell type-specific molecular alterations (e.g., case vs healthy control, sex-specific, apolipoprotein E (APOE) ε4/ε4, and TREM2 mutations). Each comparison was followed by protein-protein interaction module detection, functional enrichment analysis, and omics-informed target and drug (over 700,000 perturbation profiles) screening. Over 400 cell-cell interaction analyses using 6000 ligand-receptor interactions were conducted to identify the cell-cell communication networks in AD. Results All results are integrated into TACA (https://taca.lerner.ccf.org/), a new web portal with cell type-specific, abundant transcriptomic information, and 12 interactive visualization tools for AD. Discussion We envision that TACA will be a highly valuable resource for both basic and translational research in AD, as it provides abundant information for AD pathobiology and actionable systems biology tools for drug discovery. Highlights We compiled an Alzheimer's disease (AD) brain cell atlas consisting of more than 1.1 million cells/nuclei transcriptomes from 26 data sets, covering major brain regions (cortex, hippocampus, cerebellum) and cell types (e.g., neuron, oligodendrocyte, astrocyte, and microglia).We conducted over 1400 differential expression (DE) comparisons to identify cell type-specific gene expression alterations. Major comparison types are (1) AD versus healthy control; (2) sex-specific DE, (3) genotype-driven DE (i.e., apolipoprotein E [APOE] ε4/ε4 vs APOE ε3/ε3; TREM2R47H vs common variants) analysis; and (4) others. Each comparison was further followed by (1) human protein-protein interactome network module analysis, (2) pathway enrichment analysis, and (3) gene-set enrichment analysis.For drug screening, we conducted gene set enrichment analysis for all the comparisons with over 700,000 drug perturbation profiles connecting more than 10,000 human genes and 13,000 drugs/compounds.A total of over 400 analyses of cell-cell interactions against 6000 experimentally validated ligand-receptor interactions were conducted to reveal the disease-relevant cell-cell communications in AD.
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Affiliation(s)
- Yadi Zhou
- Genomic Medicine InstituteLerner Research InstituteCleveland ClinicClevelandOhioUSA
| | - Jielin Xu
- Genomic Medicine InstituteLerner Research InstituteCleveland ClinicClevelandOhioUSA
| | - Yuan Hou
- Genomic Medicine InstituteLerner Research InstituteCleveland ClinicClevelandOhioUSA
| | - Lynn Bekris
- Genomic Medicine InstituteLerner Research InstituteCleveland ClinicClevelandOhioUSA
- Department of Molecular MedicineCleveland Clinic Lerner College of MedicineCase Western Reserve UniversityClevelandOhioUSA
| | - James B. Leverenz
- Department of Molecular MedicineCleveland Clinic Lerner College of MedicineCase Western Reserve UniversityClevelandOhioUSA
- Lou Ruvo Center for Brain HealthNeurological InstituteCleveland ClinicClevelandOhioUSA
| | - Andrew A. Pieper
- Harrington Discovery InstituteUniversity Hospitals Cleveland Medical CenterClevelandOhioUSA
- Department of PsychiatryCase Western Reserve UniversityClevelandOhioUSA
- Geriatric Psychiatry, GRECCLouis Stokes Cleveland VA Medical CenterClevelandOhioUSA
- Institute for Transformative Molecular MedicineSchool of MedicineCase Western Reserve UniversityClevelandOhioUSA
- Department of NeuroscienceCase Western Reserve UniversitySchool of MedicineClevelandOhioUSA
| | - Jeffrey Cummings
- Chambers‐Grundy Center for Transformative NeurosciencePam Quirk Brain Health and Biomarker LaboratoryDepartment of Brain HealthSchool of Integrated Health SciencesUniversity of Nevada Las VegasLas VegasNevadaUSA
| | - Feixiong Cheng
- Genomic Medicine InstituteLerner Research InstituteCleveland ClinicClevelandOhioUSA
- Department of Molecular MedicineCleveland Clinic Lerner College of MedicineCase Western Reserve UniversityClevelandOhioUSA
- Case Comprehensive Cancer CenterCase Western Reserve University School of MedicineClevelandOhioUSA
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Nussinov R, Zhang M, Liu Y, Jang H. AlphaFold, Artificial Intelligence (AI), and Allostery. J Phys Chem B 2022; 126:6372-6383. [PMID: 35976160 PMCID: PMC9442638 DOI: 10.1021/acs.jpcb.2c04346] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 08/03/2022] [Indexed: 02/08/2023]
Abstract
AlphaFold has burst into our lives. A powerful algorithm that underscores the strength of biological sequence data and artificial intelligence (AI). AlphaFold has appended projects and research directions. The database it has been creating promises an untold number of applications with vast potential impacts that are still difficult to surmise. AI approaches can revolutionize personalized treatments and usher in better-informed clinical trials. They promise to make giant leaps toward reshaping and revamping drug discovery strategies, selecting and prioritizing combinations of drug targets. Here, we briefly overview AI in structural biology, including in molecular dynamics simulations and prediction of microbiota-human protein-protein interactions. We highlight the advancements accomplished by the deep-learning-powered AlphaFold in protein structure prediction and their powerful impact on the life sciences. At the same time, AlphaFold does not resolve the decades-long protein folding challenge, nor does it identify the folding pathways. The models that AlphaFold provides do not capture conformational mechanisms like frustration and allostery, which are rooted in ensembles, and controlled by their dynamic distributions. Allostery and signaling are properties of populations. AlphaFold also does not generate ensembles of intrinsically disordered proteins and regions, instead describing them by their low structural probabilities. Since AlphaFold generates single ranked structures, rather than conformational ensembles, it cannot elucidate the mechanisms of allosteric activating driver hotspot mutations nor of allosteric drug resistance. However, by capturing key features, deep learning techniques can use the single predicted conformation as the basis for generating a diverse ensemble.
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Affiliation(s)
- Ruth Nussinov
- Computational
Structural Biology Section, Frederick National
Laboratory for Cancer Research, Frederick, Maryland 21702, United States
- Department
of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
| | - Mingzhen Zhang
- Computational
Structural Biology Section, Frederick National
Laboratory for Cancer Research, Frederick, Maryland 21702, United States
| | - Yonglan Liu
- Cancer
Innovation Laboratory, National Cancer Institute, Frederick, Maryland 21702, United States
| | - Hyunbum Jang
- Computational
Structural Biology Section, Frederick National
Laboratory for Cancer Research, Frederick, Maryland 21702, United States
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Dai Z, Hu T, Su S, Liu J, Ma Y, Zhuo Y, Fang S, Wang Q, Mo Z, Pan H, Fang J. Comparative Metabolomics Analysis Reveals Key Metabolic Mechanisms and Protein Biomarkers in Alzheimer’s Disease. Front Pharmacol 2022; 13:904857. [PMID: 35694256 PMCID: PMC9174950 DOI: 10.3389/fphar.2022.904857] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Accepted: 04/19/2022] [Indexed: 12/13/2022] Open
Abstract
Alzheimer’s disease (AD) is one of the most common progressive neurodegenerative diseases, accompanied by global alterations in metabolic profiles. In the past 10 years, over hundreds of metabolomics studies have been conducted to unravel metabolic changes in AD, which provides insight into the identification of potential biomarkers for diagnosis, treatment, and prognostic assessment. However, since different species may lead to systemic abnormalities in metabolomic profiles, it is urgently needed to perform a comparative metabolomics analysis between AD animal models and human patients. In this study, we integrated 78 metabolic profiles from public literatures, including 11 metabolomics studies in different AD mouse models and 67 metabolomics studies from AD patients. Metabolites and enrichment analysis were further conducted to reveal key metabolic pathways and metabolites in AD. We totally identified 14 key metabolites and 16 pathways that are both differentially significant in AD mouse models and patients. Moreover, we built a metabolite-target network to predict potential protein markers in AD. Finally, we validated HER2 and NDF2 as key protein markers in APP/PS1 mice. Overall, this study provides a comprehensive strategy for AD metabolomics research, contributing to understanding the pathological mechanism of AD.
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Affiliation(s)
- Zhao Dai
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China
- Department of Rheumatology, First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Tian Hu
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Shijie Su
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Jinman Liu
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yinzhong Ma
- Institute of Biomedicine and Biotechnology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yue Zhuo
- Institute of Biomedicine and Biotechnology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Shuhuan Fang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Qi Wang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Zhizhun Mo
- Emergency Department, Shenzhen Traditional Chinese Medicine Hospital, Shenzhen, China
- *Correspondence: Zhizhun Mo, ; Huafeng Pan, ; Jiansong Fang,
| | - Huafeng Pan
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China
- *Correspondence: Zhizhun Mo, ; Huafeng Pan, ; Jiansong Fang,
| | - Jiansong Fang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China
- *Correspondence: Zhizhun Mo, ; Huafeng Pan, ; Jiansong Fang,
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Song L, Wu Q, Fu X, Wang W, Dai Z, Gu Y, Zhuo Y, Fang S, Zhao W, Wang X, Wang Q, Fang J. In Silico Identification and Mechanism Exploration of Active Ingredients against Stroke from An-Gong-Niu-Huang-Wan (AGNHW) Formula. OXIDATIVE MEDICINE AND CELLULAR LONGEVITY 2022; 2022:5218993. [PMID: 35432729 PMCID: PMC9006076 DOI: 10.1155/2022/5218993] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 02/22/2022] [Accepted: 03/10/2022] [Indexed: 11/17/2022]
Abstract
An-Gong-Niu-Huang-Wan (AGNHW) is a well-known formula for treating cerebrovascular diseases, with roles including clearing away heat, detoxification, and wake-up consciousness. In recent years, AGNHW has been commonly used for the treatment of ischemic stroke, but the mechanism by which AGNHW relieves stroke has not been clearly elucidated. In the current study, we developed a multiple systems pharmacology-based framework to identify the potential antistroke ingredients in AGNHW and explore the underlying mechanisms of action (MOA) of AGNHW against stroke from a holistic perspective. Specifically, we performed a network-based method to identify the potential antistroke ingredients in AGNHW by integrating the drug-target network and stroke-associated genes. Furthermore, the oxygen-glucose deprivation/reoxygenation (OGD/R) model was used to validate the anti-inflammatory effects of the key ingredients by determining the levels of inflammatory cytokines, including interleukin (IL)-6, IL-1β, and tumor necrosis factor (TNF)-α. The antiapoptotic effects of the key ingredients were also confirmed in vitro. Integrated pathway analysis of AGNHW revealed that it might regulate three biological signaling pathways, including IL-17, TNF, and PI3K-AKT, to play a protective role in stroke. Moreover, 30 key antistroke ingredients in AGNHW were identified via network-based in silico prediction and were confirmed to have known neuroprotective effects. After drug-like property evaluation and pharmacological validation in vitro, scutellarein (SCU) and caprylic acid (CA) were selected for further antistroke investigation. Finally, systems pharmacology-based analysis of CA and SCU indicated that they might exert antistroke effects via the apoptotic signaling pathway and inflammatory response, which was further validated in an in vitro stroke model. Overall, the current study proposes an integrative systems pharmacology approach to identify antistroke ingredients and demonstrate the underlying pharmacological MOA of AGNHW in stroke, which provides an alternative strategy to investigate novel traditional Chinese medicine formulas for complex diseases.
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Affiliation(s)
- Lei Song
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou 510405, China
- The Second Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou 510404, China
| | - Qihui Wu
- Clinical Research Center, Hainan Provincial Hospital of Traditional Chinese Medicine, Guangzhou University of Chinese Medicine, Haikou 570100, China
| | - Xiaomei Fu
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou 510405, China
| | - Wentao Wang
- School of Life Sciences, University of Liverpool, Liverpool L69 3BX, UK
| | - Zhao Dai
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou 510405, China
| | - Yong Gu
- Clinical Research Center, Hainan Provincial Hospital of Traditional Chinese Medicine, Guangzhou University of Chinese Medicine, Haikou 570100, China
| | - Yue Zhuo
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou 510405, China
| | - Shuhuan Fang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou 510405, China
| | - Wei Zhao
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou 510405, China
| | - Xiaoyun Wang
- The Second Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou 510404, China
| | - Qi Wang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou 510405, China
| | - Jiansong Fang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou 510405, China
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Nussinov R, Zhang M, Maloney R, Tsai C, Yavuz BR, Tuncbag N, Jang H. Mechanism of activation and the rewired network: New drug design concepts. Med Res Rev 2022; 42:770-799. [PMID: 34693559 PMCID: PMC8837674 DOI: 10.1002/med.21863] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 07/06/2021] [Accepted: 10/07/2021] [Indexed: 12/13/2022]
Abstract
Precision oncology benefits from effective early phase drug discovery decisions. Recently, drugging inactive protein conformations has shown impressive successes, raising the cardinal questions of which targets can profit and what are the principles of the active/inactive protein pharmacology. Cancer driver mutations have been established to mimic the protein activation mechanism. We suggest that the decision whether to target an inactive (or active) conformation should largely rest on the protein mechanism of activation. We next discuss the recent identification of double (multiple) same-allele driver mutations and their impact on cell proliferation and suggest that like single driver mutations, double drivers also mimic the mechanism of activation. We further suggest that the structural perturbations of double (multiple) in cis mutations may reveal new surfaces/pockets for drug design. Finally, we underscore the preeminent role of the cellular network which is deregulated in cancer. Our structure-based review and outlook updates the traditional Mechanism of Action, informs decisions, and calls attention to the intrinsic activation mechanism of the target protein and the rewired tumor-specific network, ushering innovative considerations in precision medicine.
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Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer ImmunometabolismNational Cancer InstituteFrederickMarylandUSA
- Department of Human Molecular Genetics and Biochemistry, Sackler School of MedicineTel Aviv UniversityTel AvivIsrael
| | - Mingzhen Zhang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer ImmunometabolismNational Cancer InstituteFrederickMarylandUSA
| | - Ryan Maloney
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer ImmunometabolismNational Cancer InstituteFrederickMarylandUSA
| | - Chung‐Jung Tsai
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer ImmunometabolismNational Cancer InstituteFrederickMarylandUSA
| | - Bengi Ruken Yavuz
- Department of Health Informatics, Graduate School of InformaticsMiddle East Technical UniversityAnkaraTurkey
| | - Nurcan Tuncbag
- Department of Health Informatics, Graduate School of InformaticsMiddle East Technical UniversityAnkaraTurkey
- Department of Chemical and Biological Engineering, College of EngineeringKoc UniversityIstanbulTurkey
- Koc University Research Center for Translational Medicine, School of MedicineKoc UniversityIstanbulTurkey
| | - Hyunbum Jang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer ImmunometabolismNational Cancer InstituteFrederickMarylandUSA
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Fang J, Zhang P, Wang Q, Chiang CW, Zhou Y, Hou Y, Xu J, Chen R, Zhang B, Lewis SJ, Leverenz JB, Pieper AA, Li B, Li L, Cummings J, Cheng F. Artificial intelligence framework identifies candidate targets for drug repurposing in Alzheimer's disease. Alzheimers Res Ther 2022; 14:7. [PMID: 35012639 PMCID: PMC8751379 DOI: 10.1186/s13195-021-00951-z] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Accepted: 12/16/2021] [Indexed: 02/07/2023]
Abstract
BACKGROUND Genome-wide association studies (GWAS) have identified numerous susceptibility loci for Alzheimer's disease (AD). However, utilizing GWAS and multi-omics data to identify high-confidence AD risk genes (ARGs) and druggable targets that can guide development of new therapeutics for patients suffering from AD has heretofore not been successful. METHODS To address this critical problem in the field, we have developed a network-based artificial intelligence framework that is capable of integrating multi-omics data along with human protein-protein interactome networks to accurately infer accurate drug targets impacted by GWAS-identified variants to identify new therapeutics. When applied to AD, this approach integrates GWAS findings, multi-omics data from brain samples of AD patients and AD transgenic animal models, drug-target networks, and the human protein-protein interactome, along with large-scale patient database validation and in vitro mechanistic observations in human microglia cells. RESULTS Through this approach, we identified 103 ARGs validated by various levels of pathobiological evidence in AD. Via network-based prediction and population-based validation, we then showed that three drugs (pioglitazone, febuxostat, and atenolol) are significantly associated with decreased risk of AD compared with matched control populations. Pioglitazone usage is significantly associated with decreased risk of AD (hazard ratio (HR) = 0.916, 95% confidence interval [CI] 0.861-0.974, P = 0.005) in a retrospective case-control validation. Pioglitazone is a peroxisome proliferator-activated receptor (PPAR) agonist used to treat type 2 diabetes, and propensity score matching cohort studies confirmed its association with reduced risk of AD in comparison to glipizide (HR = 0.921, 95% CI 0.862-0.984, P = 0.0159), an insulin secretagogue that is also used to treat type 2 diabetes. In vitro experiments showed that pioglitazone downregulated glycogen synthase kinase 3 beta (GSK3β) and cyclin-dependent kinase (CDK5) in human microglia cells, supporting a possible mechanism-of-action for its beneficial effect in AD. CONCLUSIONS In summary, we present an integrated, network-based artificial intelligence methodology to rapidly translate GWAS findings and multi-omics data to genotype-informed therapeutic discovery in AD.
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Affiliation(s)
- Jiansong Fang
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
| | - Pengyue Zhang
- Department of Biostatistics and Health Data Science, School of Medicine, Indiana University, Indianapolis, IN, 46202, USA
| | - Quan Wang
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37212, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, 37212, USA
| | - Chien-Wei Chiang
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH, 43210, USA
| | - 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
| | - Jielin Xu
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
| | - Rui Chen
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37212, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, 37212, USA
| | - Bin Zhang
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
| | - Stephen J Lewis
- Department of Pediatrics, Case Western Reserve University, Cleveland, Ohio, 44106, USA
| | - James B Leverenz
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, 44195, USA
- Lou Ruvo Center for Brain Health, Neurological Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
| | - Andrew A Pieper
- Harrington Discovery Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, 44106, USA
- Department of Psychiatry, Case Western Reserve University, Cleveland, OH, 44106, USA
- Geriatric Psychiatry, GRECC, Louis Stokes Cleveland VA Medical Center, Cleveland, OH, 44106, USA
- Institute for Transformative Molecular Medicine, School of Medicine, Case Western Reserve University, Cleveland, OH, 44106, USA
- Department of Neuroscience, Case Western Reserve University, School of Medicine, Cleveland, OH, 44106, USA
| | - Bingshan Li
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37212, USA.
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, 37212, USA.
| | - Lang Li
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH, 43210, USA.
| | - Jeffrey Cummings
- Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Sciences, University of Nevada Las Vegas, Las Vegas, NV, 89154, 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, Ohio, 44106, USA.
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Li Z, Jiang X, Wang Y, Kim Y. Applied machine learning in Alzheimer's disease research: omics, imaging, and clinical data. Emerg Top Life Sci 2021; 5:765-777. [PMID: 34881778 PMCID: PMC8786302 DOI: 10.1042/etls20210249] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 11/05/2021] [Accepted: 11/17/2021] [Indexed: 01/26/2023]
Abstract
Alzheimer's disease (AD) remains a devastating neurodegenerative disease with few preventive or curative treatments available. Modern technology developments of high-throughput omics platforms and imaging equipment provide unprecedented opportunities to study the etiology and progression of this disease. Meanwhile, the vast amount of data from various modalities, such as genetics, proteomics, transcriptomics, and imaging, as well as clinical features impose great challenges in data integration and analysis. Machine learning (ML) methods offer novel techniques to address high dimensional data, integrate data from different sources, model the etiological and clinical heterogeneity, and discover new biomarkers. These directions have the potential to help us better manage the disease progression and develop novel treatment strategies. This mini-review paper summarizes different ML methods that have been applied to study AD using single-platform or multi-modal data. We review the current state of ML applications for five key directions of AD research: disease classification, drug repurposing, subtyping, progression prediction, and biomarker discovery. This summary provides insights about the current research status of ML-based AD research and highlights potential directions for future research.
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Affiliation(s)
- Ziyi Li
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, U.S.A
| | - Xiaoqian Jiang
- School of Biomedical Informatics, The University of Texas Health Science Center, Houston, TX, U.S.A
| | - Yizhuo Wang
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, U.S.A
| | - Yejin Kim
- School of Biomedical Informatics, The University of Texas Health Science Center, Houston, TX, U.S.A
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Leysen H, Walter D, Christiaenssen B, Vandoren R, Harputluoğlu İ, Van Loon N, Maudsley S. GPCRs Are Optimal Regulators of Complex Biological Systems and Orchestrate the Interface between Health and Disease. Int J Mol Sci 2021; 22:ijms222413387. [PMID: 34948182 PMCID: PMC8708147 DOI: 10.3390/ijms222413387] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 12/08/2021] [Accepted: 12/09/2021] [Indexed: 02/06/2023] Open
Abstract
GPCRs arguably represent the most effective current therapeutic targets for a plethora of diseases. GPCRs also possess a pivotal role in the regulation of the physiological balance between healthy and pathological conditions; thus, their importance in systems biology cannot be underestimated. The molecular diversity of GPCR signaling systems is likely to be closely associated with disease-associated changes in organismal tissue complexity and compartmentalization, thus enabling a nuanced GPCR-based capacity to interdict multiple disease pathomechanisms at a systemic level. GPCRs have been long considered as controllers of communication between tissues and cells. This communication involves the ligand-mediated control of cell surface receptors that then direct their stimuli to impact cell physiology. Given the tremendous success of GPCRs as therapeutic targets, considerable focus has been placed on the ability of these therapeutics to modulate diseases by acting at cell surface receptors. In the past decade, however, attention has focused upon how stable multiprotein GPCR superstructures, termed receptorsomes, both at the cell surface membrane and in the intracellular domain dictate and condition long-term GPCR activities associated with the regulation of protein expression patterns, cellular stress responses and DNA integrity management. The ability of these receptorsomes (often in the absence of typical cell surface ligands) to control complex cellular activities implicates them as key controllers of the functional balance between health and disease. A greater understanding of this function of GPCRs is likely to significantly augment our ability to further employ these proteins in a multitude of diseases.
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Affiliation(s)
- Hanne Leysen
- Receptor Biology Lab, University of Antwerp, 2610 Wilrijk, Belgium; (H.L.); (D.W.); (B.C.); (R.V.); (İ.H.); (N.V.L.)
| | - Deborah Walter
- Receptor Biology Lab, University of Antwerp, 2610 Wilrijk, Belgium; (H.L.); (D.W.); (B.C.); (R.V.); (İ.H.); (N.V.L.)
| | - Bregje Christiaenssen
- Receptor Biology Lab, University of Antwerp, 2610 Wilrijk, Belgium; (H.L.); (D.W.); (B.C.); (R.V.); (İ.H.); (N.V.L.)
| | - Romi Vandoren
- Receptor Biology Lab, University of Antwerp, 2610 Wilrijk, Belgium; (H.L.); (D.W.); (B.C.); (R.V.); (İ.H.); (N.V.L.)
| | - İrem Harputluoğlu
- Receptor Biology Lab, University of Antwerp, 2610 Wilrijk, Belgium; (H.L.); (D.W.); (B.C.); (R.V.); (İ.H.); (N.V.L.)
- Department of Chemistry, Middle East Technical University, Çankaya, Ankara 06800, Turkey
| | - Nore Van Loon
- Receptor Biology Lab, University of Antwerp, 2610 Wilrijk, Belgium; (H.L.); (D.W.); (B.C.); (R.V.); (İ.H.); (N.V.L.)
| | - Stuart Maudsley
- Receptor Biology Lab, University of Antwerp, 2610 Wilrijk, Belgium; (H.L.); (D.W.); (B.C.); (R.V.); (İ.H.); (N.V.L.)
- Correspondence:
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Fang J, Zhang P, Zhou Y, Chiang CW, Tan J, Hou Y, Stauffer S, Li L, Pieper AA, Cummings J, Cheng F. Endophenotype-based in silico network medicine discovery combined with insurance record data mining identifies sildenafil as a candidate drug for Alzheimer's disease. NATURE AGING 2021; 1:1175-1188. [PMID: 35572351 PMCID: PMC9097949 DOI: 10.1038/s43587-021-00138-z] [Citation(s) in RCA: 85] [Impact Index Per Article: 28.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
We developed an endophenotype disease module-based methodology for Alzheimer's disease (AD) drug repurposing and identified sildenafil as a potential disease risk modifier. Based on retrospective case-control pharmacoepidemiologic analyses of insurance claims data for 7.23 million individuals, we found that sildenafil usage was significantly associated with a 69% reduced risk of AD (hazard ratio = 0.31, 95% confidence interval 0.25-0.39, P<1.0×10-8). Propensity score stratified analyses confirmed that sildenafil is significantly associated with a decreased risk of AD across all four drug cohorts we tested (diltiazem, glimepiride, losartan and metformin) after adjusting age, sex, race, and disease comorbidities. We also found that sildenafil increases neurite growth and decreases phospho-tau expression in AD patient-induced pluripotent stem cells-derived neuron models, supporting mechanistically its potential beneficial effect in Alzheimer's disease. The association between sildenafil use and decreased incidence of AD does not establish causality or its direction, which requires a randomized clinical trial approach.
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Affiliation(s)
- Jiansong Fang
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Pengyue Zhang
- Department of Biostatistics, School of Medicine, Indiana University
| | - Yadi Zhou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Chien-Wei Chiang
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH 43210, USA
| | - Juan Tan
- 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
| | - Shaun Stauffer
- Center for Therapeutics Discovery, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Lang Li
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH 43210, USA
| | - Andrew A. Pieper
- Harrington Discovery Institute, University Hospital Case Medical Center; Department of Psychiatry, Case Western Reserve University, Geriatric Research Education and Clinical Centers, Louis Stokes Cleveland VAMC, Cleveland, OH 44106, USA
| | - Jeffrey Cummings
- Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Sciences, University of Nevada Las Vegas, Las Vegas, NV 89154, 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, Ohio 44106, USA.,Correspondence to: Feixiong Cheng, Ph.D., Lerner Research Institute, Cleveland Clinic, , Tel: +1-216-4447654; Fax: +1-216-6361609
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Walgrave H, Zhou L, De Strooper B, Salta E. The promise of microRNA-based therapies in Alzheimer's disease: challenges and perspectives. Mol Neurodegener 2021; 16:76. [PMID: 34742333 PMCID: PMC8572071 DOI: 10.1186/s13024-021-00496-7] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2021] [Accepted: 10/17/2021] [Indexed: 02/06/2023] Open
Abstract
Multi-pathway approaches for the treatment of complex polygenic disorders are emerging as alternatives to classical monotarget therapies and microRNAs are of particular interest in that regard. MicroRNA research has come a long way from their initial discovery to the cumulative appreciation of their regulatory potential in healthy and diseased brain. However, systematic interrogation of putative therapeutic or toxic effects of microRNAs in (models of) Alzheimer's disease is currently missing and fundamental research findings are yet to be translated into clinical applications. Here, we review the literature to summarize the knowledge on microRNA regulation in Alzheimer's pathophysiology and to critically discuss whether and to what extent these increasing insights can be exploited for the development of microRNA-based therapeutics in the clinic.
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Affiliation(s)
- Hannah Walgrave
- VIB Center for Brain & Disease Research, Leuven, KU, Leuven, Belgium
- Department of Neurosciences, Leuven Brain Institute, Leuven, Belgium
| | - Lujia Zhou
- Division of Janssen Pharmaceutica NV, Discovery Neuroscience, Janssen Research and Development, Beerse, Belgium
| | - Bart De Strooper
- VIB Center for Brain & Disease Research, Leuven, KU, Leuven, Belgium
- Department of Neurosciences, Leuven Brain Institute, Leuven, Belgium
- UK Dementia Research Institute at University College London, London, UK
| | - Evgenia Salta
- Netherlands Institute for Neuroscience, Amsterdam, The Netherlands
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Li D, Cai C, Liao Y, Wu Q, Ke H, Guo P, Wang Q, Ding B, Fang J, Fang S. Systems pharmacology approach uncovers the therapeutic mechanism of medicarpin against scopolamine-induced memory loss. PHYTOMEDICINE : INTERNATIONAL JOURNAL OF PHYTOTHERAPY AND PHYTOPHARMACOLOGY 2021; 91:153662. [PMID: 34333326 DOI: 10.1016/j.phymed.2021.153662] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 06/10/2021] [Accepted: 07/12/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND Medicarpin is a natural pterocarpan-type phytoalexin widely distributed in many traditional Chinese medicines, such as Astragali Radix. A previous study showed that Astragali Radix demonstrated promising protective effects in neurons. However, there is no reported study on the neuroprotective function and the underlying mechanism of Medicarpin. PURPOSE This study aimed to demonstrate the neuroprotective effect of Medicarpin on Alzheimer's disease (AD) and explore the therapeutic mechanisms. METHOD First, we carried out animal behavioral tests and biochemical analysis to assess the anti-AD potential of Medicarpin for ameliorating spatial learning and memory and modulating cholinergic metabolism in scopolamine-induced amnesic mice. Subsequently, network proximity prediction was used to measure the network distance between the Medicarpin target network and AD-related endophenotype module. We identified Medicarpin-regulated AD pathological processes and highlighted the key disease targets via network analysis. Finally, experimental approaches including Nissl staining and Western blotting were conducted to validate our network-based findings. RESULT In this study, we first observed that Medicarpin can ameliorate cognitive and memory dysfunction and significantly modulate cholinergic metabolism in scopolamine-induced amnesic mice. We then proposed an endophenotype network-based framework to comprehensively explore the AD therapeutic mechanisms of Medicarpin by integrating 25 AD-related endophenotype modules, gold-standard AD seed genes, an experimentally validated drug-target network of Medicarpin, and a global human protein-protein interactome. In silico prediction revealed that the effect of Medicarpin is highly relevant to neuronal apoptosis and synaptic plasticity, which was validated by experimental assays. Network analysis and Western blotting further identified two key targets, GSK-3β and MAPK14 (p38), in the AD-related protein regulatory network, which play key roles in the regulation of neuronal apoptosis and synaptic plasticity by Medicarpin. CONCLUSIONS This study presented a powerful endophenotype network-based strategy to explore the mechanisms of action (MOAs) of new AD therapeutics, and first identified Medicarpin as a potential anti-AD candidate by targeting multiple pathways.
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Affiliation(s)
- Dongli Li
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, 510405, China; The Second Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, 510404, China
| | - Chuipu Cai
- Division of Data Intelligence, Department of Computer Science, Key Laboratory of Intelligent Manufacturing Technology of Ministry of Education, Shantou University, Shantou, China
| | - Yanfang Liao
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, 510405, China
| | - Qihui Wu
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, 510405, China
| | - Hanzhong Ke
- Department of Cancer Immunology and Virology, Dana-Farber Cancer Institute, Boston, MA, 02215, United States
| | - Pengfei Guo
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, 510405, China
| | - Qi Wang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, 510405, China
| | - Banghan Ding
- The Second Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, 510404, China
| | - Jiansong Fang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, 510405, China.
| | - Shuhuan Fang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, 510405, China; Department of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford, CA, 94305, United States.
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Xu J, Zhang P, Huang Y, Zhou Y, Hou Y, Bekris LM, Lathia J, Chiang CW, Li L, Pieper AA, Leverenz JB, Cummings J, Cheng F. Multimodal single-cell/nucleus RNA sequencing data analysis uncovers molecular networks between disease-associated microglia and astrocytes with implications for drug repurposing in Alzheimer's disease. Genome Res 2021; 31:1900-1912. [PMID: 33627474 PMCID: PMC8494225 DOI: 10.1101/gr.272484.120] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 02/18/2021] [Indexed: 11/25/2022]
Abstract
Because disease-associated microglia (DAM) and disease-associated astrocytes (DAA) are involved in the pathophysiology of Alzheimer's disease (AD), we systematically identified molecular networks between DAM and DAA to uncover novel therapeutic targets for AD. Specifically, we develop a network-based methodology that leverages single-cell/nucleus RNA sequencing data from both transgenic mouse models and AD patient brains, as well as drug-target network, metabolite-enzyme associations, the human protein-protein interactome, and large-scale longitudinal patient data. Through this approach, we find both common and unique gene network regulators between DAM (i.e., PAK1, MAPK14, and CSF1R) and DAA (i.e., NFKB1, FOS, and JUN) that are significantly enriched by neuro-inflammatory pathways and well-known genetic variants (i.e., BIN1). We identify shared immune pathways between DAM and DAA, including Th17 cell differentiation and chemokine signaling. Last, integrative metabolite-enzyme network analyses suggest that fatty acids and amino acids may trigger molecular alterations in DAM and DAA. Combining network-based prediction and retrospective case-control observations with 7.2 million individuals, we identify that usage of fluticasone (an approved glucocorticoid receptor agonist) is significantly associated with a reduced incidence of AD (hazard ratio [HR] = 0.86, 95% confidence interval [CI] 0.83-0.89, P < 1.0 × 10-8). Propensity score-stratified cohort studies reveal that usage of mometasone (a stronger glucocorticoid receptor agonist) is significantly associated with a decreased risk of AD (HR = 0.74, 95% CI 0.68-0.81, P < 1.0 × 10-8) compared to fluticasone after adjusting age, gender, and disease comorbidities. In summary, we present a network-based, multimodal methodology for single-cell/nucleus genomics-informed drug discovery and have identified fluticasone and mometasone as potential treatments in AD.
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Affiliation(s)
- Jielin Xu
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio 44195, USA
| | - Pengyue Zhang
- Department of Biostatistics, School of Medicine, Indiana University, Indianapolis, Indiana 46202, USA
| | - Yin Huang
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio 44195, USA
| | - Yadi Zhou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio 44195, USA
| | - Yuan Hou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio 44195, USA
| | - Lynn M Bekris
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio 44195, USA
| | - Justin Lathia
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio 44195, USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio 44106, USA
- Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio 44195, USA
| | - Chien-Wei Chiang
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, Ohio 43210, USA
| | - Lang Li
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, Ohio 43210, USA
| | - Andrew A Pieper
- Harrington Discovery Institute, University Hospitals Cleveland Medical Center, Cleveland, Ohio 44106, USA
- Department of Psychiatry, Case Western Reserve University, Cleveland, Ohio 44106, USA
- Geriatric Psychiatry, GRECC, Louis Stokes Cleveland VA Medical Center, Cleveland, Ohio 44106, USA
- Institute for Transformative Molecular Medicine, School of Medicine, Case Western Reserve University, Cleveland 44106, Ohio, USA
- Weill Cornell Autism Research Program, Weill Cornell Medicine of Cornell University, New York, New York 10065, USA
- Department of Neuroscience, Case Western Reserve University, School of Medicine, Cleveland, Ohio 44106, USA
| | - James B Leverenz
- Lou Ruvo Center for Brain Health, Neurological Institute, Cleveland Clinic, Cleveland, Ohio 44195, USA
| | - Jeffrey Cummings
- Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Sciences, University of Nevada Las Vegas, Las Vegas, Nevada 89154, USA
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio 44195, USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio 44106, USA
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Fang J, Wu Q, Ye F, Cai C, Xu L, Gu Y, Wang Q, Liu AL, Tan W, Du GH. Network-Based Identification and Experimental Validation of Drug Candidates Toward SARS-CoV-2 via Targeting Virus-Host Interactome. Front Genet 2021; 12:728960. [PMID: 34539756 PMCID: PMC8440948 DOI: 10.3389/fgene.2021.728960] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Accepted: 08/06/2021] [Indexed: 12/24/2022] Open
Abstract
Despite that several therapeutic agents have exhibited promising prevention or treatment on Coronavirus disease-2019 (COVID-19), there is no specific drug discovered for this pandemic. Targeting virus-host interactome provides a more effective strategy for antivirus drug discovery compared with targeting virus proteins. In this study, we developed a network-based infrastructure to prioritize promising drug candidates from natural products and approved drugs via targeting host proteins of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). We firstly measured the network distances between drug targets and COVID-19 disease module utilizing the network proximity approach, and identified 229 approved drugs as well as 432 natural products had significant associations with SARS-CoV-2. After searching for previous literature evidence, we found that 60.7% (139/229) of approved drugs and 39.6% (171/432) of natural products were confirmed with antivirus or anti-inflammation. We further integrated our network-based predictions and validated anti-SARS-CoV-2 activities of some compounds. Four drug candidates, including hesperidin, isorhapontigenin, salmeterol, and gallocatechin-7-gallate, have exhibited activity on SARS-COV-2 virus-infected Vero cells. Finally, we showcased the mechanism of actions of isorhapontigenin and salmeterol via network analysis. Overall, this study offers forceful approaches for in silico identification of drug candidates on COVID-19, which may facilitate the discovery of antiviral drug therapies.
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Affiliation(s)
- Jiansong Fang
- Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Qihui Wu
- Clinical Research Center, Hainan Provincial Hospital of Traditional Chinese Medicine, Guangzhou University of Chinese Medicine, Haikou, China
| | - Fei Ye
- MHC Key Laboratory of Biosafety, National Institute for Viral Disease Control and Prevention, Beijing, China
| | - Chuipu Cai
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Lvjie Xu
- Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yong Gu
- Clinical Research Center, Hainan Provincial Hospital of Traditional Chinese Medicine, Guangzhou University of Chinese Medicine, Haikou, China
| | - Qi Wang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Ai-lin Liu
- Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wenjie Tan
- MHC Key Laboratory of Biosafety, National Institute for Viral Disease Control and Prevention, Beijing, China
| | - Guan-hua Du
- Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Pan Y, Lei X, Zhang Y. Association predictions of genomics, proteinomics, transcriptomics, microbiome, metabolomics, pathomics, radiomics, drug, symptoms, environment factor, and disease networks: A comprehensive approach. Med Res Rev 2021; 42:441-461. [PMID: 34346083 DOI: 10.1002/med.21847] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2020] [Revised: 05/22/2021] [Accepted: 07/07/2021] [Indexed: 12/12/2022]
Abstract
Currently, the research of multi-omics, such as genomics, proteinomics, transcriptomics, microbiome, metabolomics, pathomics, and radiomics, are hot spots. The relationship between multi-omics data, drugs, and diseases has received extensive attention from researchers. At the same time, multi-omics can effectively predict the diagnosis, prognosis, and treatment of diseases. In essence, these research entities, such as genes, RNAs, proteins, microbes, metabolites, pathways as well as pathological and medical imaging data, can all be represented by the network at different levels. And some computer and biology scholars have tried to use computational methods to explore the potential relationships between biological entities. We summary a comprehensive research strategy, that is to build a multi-omics heterogeneous network, covering multimodal data, and use the current popular computational methods to make predictions. In this study, we first introduce the calculation method of the similarity of biological entities at the data level, second discuss multimodal data fusion and methods of feature extraction. Finally, the challenges and opportunities at this stage are summarized. Some scholars have used such a framework to calculate and predict. We also summarize them and discuss the challenges. We hope that our review could help scholars who are interested in the field of bioinformatics, biomedical image, and computer research.
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Affiliation(s)
- Yi Pan
- Faculty of Computer Science and Control Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Xiujuan Lei
- School of Computer Science, Shaanxi Normal University, Xi'an, China
| | - Yuchen Zhang
- School of Computer Science, Shaanxi Normal University, Xi'an, China
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Kashyap K, Siddiqi MI. Recent trends in artificial intelligence-driven identification and development of anti-neurodegenerative therapeutic agents. Mol Divers 2021; 25:1517-1539. [PMID: 34282519 DOI: 10.1007/s11030-021-10274-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 07/05/2021] [Indexed: 12/12/2022]
Abstract
Neurological disorders affect various aspects of life. Finding drugs for the central nervous system is a very challenging and complex task due to the involvement of the blood-brain barrier, P-glycoprotein, and the drug's high attrition rates. The availability of big data present in online databases and resources has enabled the emergence of artificial intelligence techniques including machine learning to analyze, process the data, and predict the unknown data with high efficiency. The use of these modern techniques has revolutionized the whole drug development paradigm, with an unprecedented acceleration in the central nervous system drug discovery programs. Also, the new deep learning architectures proposed in many recent works have given a better understanding of how artificial intelligence can tackle big complex problems that arose due to central nervous system disorders. Therefore, the present review provides comprehensive and up-to-date information on machine learning/artificial intelligence-triggered effort in the brain care domain. In addition, a brief overview is presented on machine learning algorithms and their uses in structure-based drug design, ligand-based drug design, ADMET prediction, de novo drug design, and drug repurposing. Lastly, we conclude by discussing the major challenges and limitations posed and how they can be tackled in the future by using these modern machine learning/artificial intelligence approaches.
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Affiliation(s)
- Kushagra Kashyap
- Academy of Scientific and Innovative Research (AcSIR), CSIR-Central Drug Research Institute (CSIR-CDRI) Campus, Lucknow, India.,Molecular and Structural Biology Division, CSIR-Central Drug Research Institute (CSIR-CDRI), Sector 10, Jankipuram Extension, Sitapur Road, Lucknow, 226031, India
| | - Mohammad Imran Siddiqi
- Academy of Scientific and Innovative Research (AcSIR), CSIR-Central Drug Research Institute (CSIR-CDRI) Campus, Lucknow, India. .,Molecular and Structural Biology Division, CSIR-Central Drug Research Institute (CSIR-CDRI), Sector 10, Jankipuram Extension, Sitapur Road, Lucknow, 226031, India.
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Zhou Y, Xu J, Hou Y, Leverenz JB, Kallianpur A, Mehra R, Liu Y, Yu H, Pieper AA, Jehi L, Cheng F. Network medicine links SARS-CoV-2/COVID-19 infection to brain microvascular injury and neuroinflammation in dementia-like cognitive impairment. Alzheimers Res Ther 2021; 13:110. [PMID: 34108016 PMCID: PMC8189279 DOI: 10.1186/s13195-021-00850-3] [Citation(s) in RCA: 84] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Accepted: 05/28/2021] [Indexed: 12/13/2022]
Abstract
BACKGROUND Dementia-like cognitive impairment is an increasingly reported complication of SARS-CoV-2 infection. However, the underlying mechanisms responsible for this complication remain unclear. A better understanding of causative processes by which COVID-19 may lead to cognitive impairment is essential for developing preventive and therapeutic interventions. METHODS In this study, we conducted a network-based, multimodal omics comparison of COVID-19 and neurologic complications. We constructed the SARS-CoV-2 virus-host interactome from protein-protein interaction assay and CRISPR-Cas9-based genetic assay results and compared network-based relationships therein with those of known neurological manifestations using network proximity measures. We also investigated the transcriptomic profiles (including single-cell/nuclei RNA-sequencing) of Alzheimer's disease (AD) marker genes from patients infected with COVID-19, as well as the prevalence of SARS-CoV-2 entry factors in the brains of AD patients not infected with SARS-CoV-2. RESULTS We found significant network-based relationships between COVID-19 and neuroinflammation and brain microvascular injury pathways and processes which are implicated in AD. We also detected aberrant expression of AD biomarkers in the cerebrospinal fluid and blood of patients with COVID-19. While transcriptomic analyses showed relatively low expression of SARS-CoV-2 entry factors in human brain, neuroinflammatory changes were pronounced. In addition, single-nucleus transcriptomic analyses showed that expression of SARS-CoV-2 host factors (BSG and FURIN) and antiviral defense genes (LY6E, IFITM2, IFITM3, and IFNAR1) was elevated in brain endothelial cells of AD patients and healthy controls relative to neurons and other cell types, suggesting a possible role for brain microvascular injury in COVID-19-mediated cognitive impairment. Overall, individuals with the AD risk allele APOE E4/E4 displayed reduced expression of antiviral defense genes compared to APOE E3/E3 individuals. CONCLUSION Our results suggest significant mechanistic overlap between AD and COVID-19, centered on neuroinflammation and microvascular injury. These results help improve our understanding of COVID-19-associated neurological manifestations and provide guidance for future development of preventive or treatment interventions, although causal relationship and mechanistic pathways between COVID-19 and AD need future investigations.
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Affiliation(s)
- Yadi Zhou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
| | - Jielin Xu
- 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
| | - James B Leverenz
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, 44195, USA
- Lou Ruvo Center for Brain Health, Neurological 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
| | - Reena Mehra
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, 44195, USA
- Neurological Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
| | - Yunlong Liu
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Haiyuan Yu
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, 14850, USA
- Department of Computational Biology, Cornell University, Ithaca, NY, 14850, USA
- Tri-Institutional Training Program in Computational Biology and Medicine, Cornell University, Ithaca, NY, 14850, USA
| | - Andrew A Pieper
- Harrington Discovery Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, 44106, USA
- Department of Psychiatry, Case Western Reserve University, Cleveland, OH, 44106, USA
- Geriatric Psychiatry, GRECC, Louis Stokes Cleveland VA Medical Center, Cleveland, OH, 44106, USA
- Institute for Transformative Molecular Medicine, School of Medicine, Case Western Reserve University, Cleveland, OH, 44106, USA
- Weill Cornell Autism Research Program, Weill Cornell Medicine of Cornell University, New York, NY, 10065, USA
- Department of Neuroscience, School of Medicine, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Lara Jehi
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, 44195, USA
- Lou Ruvo Center for Brain Health, Neurological 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, School of Medicine, Case Western Reserve University, Cleveland, OH, 44106, USA.
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Guo L, Ravindran N, Shamsher E, Hill D, Cordeiro MF. Retinal Changes in Transgenic Mouse Models of Alzheimer's Disease. Curr Alzheimer Res 2021; 18:89-102. [PMID: 33855942 DOI: 10.2174/1567205018666210414113634] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2020] [Revised: 02/09/2021] [Accepted: 04/05/2021] [Indexed: 11/22/2022]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder, the most common form of dementia. AD is characterised by amyloid-β (Aβ) plaques and neurofibrillary tangles (NFT) in the brain, in association with neuronal loss and synaptic failure, causing cognitive deficits. Accurate and early diagnosis is currently unavailable in lifespan, hampering early intervention of potential new treatments. Visual deficits have been well documented in AD patients, and the pathological changes identified in the brain are also believed to be found in the retina, an integral part of the central nervous system. Retinal changes can be detected by real-time non-invasive imaging, due to the transparent nature of the ocular media, potentially allowing an earlier diagnosis as well as monitoring disease progression and treatment outcome. Animal models are essential for AD research, and this review has a focus on retinal changes in various transgenic AD mouse models with retinal imaging and immunohistochemical analysis as well as therapeutic effects in those models. We also discuss the limitations of transgenic AD models in clinical translations.
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Affiliation(s)
- Li Guo
- Glaucoma & Retinal Neurodegeneration Research Group, Institute of Ophthalmology, University College London, London, United Kingdom
| | - Nivedita Ravindran
- Glaucoma & Retinal Neurodegeneration Research Group, Institute of Ophthalmology, University College London, London, United Kingdom
| | - Ehtesham Shamsher
- Glaucoma & Retinal Neurodegeneration Research Group, Institute of Ophthalmology, University College London, London, United Kingdom
| | - Daniel Hill
- Glaucoma & Retinal Neurodegeneration Research Group, Institute of Ophthalmology, University College London, London, United Kingdom
| | - M Francesca Cordeiro
- Glaucoma & Retinal Neurodegeneration Research Group, Institute of Ophthalmology, University College London, London, United Kingdom
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Zhou Y, Xu J, Hou Y, Leverenz JB, Kallianpur A, Mehra R, Liu Y, Yu H, Pieper AA, Jehi L, Cheng F. Network medicine links SARS-CoV-2/COVID-19 infection to brain microvascular injury and neuroinflammation in dementia-like cognitive impairment. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2021:2021.03.15.435423. [PMID: 33791705 PMCID: PMC8010732 DOI: 10.1101/2021.03.15.435423] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
BACKGROUND Dementia-like cognitive impairment is an increasingly reported complication of SARS-CoV-2 infection. However, the underlying mechanisms responsible for this complication remain unclear. A better understanding of causative processes by which COVID-19 may lead to cognitive impairment is essential for developing preventive interventions. METHODS In this study, we conducted a network-based, multimodal genomics comparison of COVID-19 and neurologic complications. We constructed the SARS-CoV-2 virus-host interactome from protein-protein interaction assay and CRISPR-Cas9 based genetic assay results, and compared network-based relationships therein with those of known neurological manifestations using network proximity measures. We also investigated the transcriptomic profiles (including single-cell/nuclei RNA-sequencing) of Alzheimer's disease (AD) marker genes from patients infected with COVID-19, as well as the prevalence of SARS-CoV-2 entry factors in the brains of AD patients not infected with SARS-CoV-2. RESULTS We found significant network-based relationships between COVID-19 and neuroinflammation and brain microvascular injury pathways and processes which are implicated in AD. We also detected aberrant expression of AD biomarkers in the cerebrospinal fluid and blood of patients with COVID-19. While transcriptomic analyses showed relatively low expression of SARS-CoV-2 entry factors in human brain, neuroinflammatory changes were pronounced. In addition, single-nucleus transcriptomic analyses showed that expression of SARS-CoV-2 host factors ( BSG and FURIN ) and antiviral defense genes ( LY6E , IFITM2 , IFITM3 , and IFNAR1 ) was significantly elevated in brain endothelial cells of AD patients and healthy controls relative to neurons and other cell types, suggesting a possible role for brain microvascular injury in COVID-19-mediated cognitive impairment. Notably, individuals with the AD risk allele APOE E4/E4 displayed reduced levels of antiviral defense genes compared to APOE E3/E3 individuals. CONCLUSION Our results suggest significant mechanistic overlap between AD and COVID-19, strongly centered on neuroinflammation and microvascular injury. These results help improve our understanding of COVID-19-associated neurological manifestations and provide guidance for future development of preventive or treatment interventions.
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Affiliation(s)
- Yadi Zhou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Jielin Xu
- 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
| | - James B. Leverenz
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
- Lou Ruvo Center for Brain Health, Neurological 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
| | - Reena Mehra
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
- Neurological Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Yunlong Liu
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Haiyuan Yu
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14850, USA
- Department of Computational Biology, Cornell University, Ithaca, NY 14850, USA
- Tri-Institutional Training Program in Computational Biology and Medicine, Cornell University, Ithaca, NY 14850, USA
| | - Andrew A. Pieper
- Harrington Discovery Institute, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA
- Department of Psychiatry, Case Western Reserve University, Cleveland, OH 44106, USA
- Geriatric Psychiatry, GRECC, Louis Stokes Cleveland VA Medical Center; Cleveland, OH 44106, USA
- Institute for Transformative Molecular Medicine, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA
- Weill Cornell Autism Research Program, Weill Cornell Medicine of Cornell University, New York, NY 10065, USA
- Department of Neuroscience, Case Western Reserve University, School of Medicine, Cleveland, OH 44106, USA
| | - Lara Jehi
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
- Lou Ruvo Center for Brain Health, Neurological 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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Zhou Y, Fang J, Bekris LM, Kim YH, Pieper AA, Leverenz JB, Cummings J, Cheng F. AlzGPS: a genome-wide positioning systems platform to catalyze multi-omics for Alzheimer's drug discovery. Alzheimers Res Ther 2021; 13:24. [PMID: 33441136 PMCID: PMC7804907 DOI: 10.1186/s13195-020-00760-w] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Accepted: 12/23/2020] [Indexed: 12/24/2022]
Abstract
BACKGROUND Recent DNA/RNA sequencing and other multi-omics technologies have advanced the understanding of the biology and pathophysiology of AD, yet there is still a lack of disease-modifying treatments for AD. A new approach to integration of the genome, transcriptome, proteome, and human interactome in the drug discovery and development process is essential for this endeavor. METHODS In this study, we developed AlzGPS (Genome-wide Positioning Systems platform for Alzheimer's Drug Discovery, https://alzgps.lerner.ccf.org ), a comprehensive systems biology tool to enable searching, visualizing, and analyzing multi-omics, various types of heterogeneous biological networks, and clinical databases for target identification and development of effective prevention and treatment for AD. RESULTS Via AlzGPS: (1) we curated more than 100 AD multi-omics data sets capturing DNA, RNA, protein, and small molecule profiles underlying AD pathogenesis (e.g., early vs. late stage and tau or amyloid endophenotype); (2) we constructed endophenotype disease modules by incorporating multi-omics findings and human protein-protein interactome networks; (3) we provided possible treatment information from ~ 3000 FDA approved/investigational drugs for AD using state-of-the-art network proximity analyses; (4) we curated nearly 300 literature references for high-confidence drug candidates; (5) we included information from over 1000 AD clinical trials noting drug's mechanisms-of-action and primary drug targets, and linking them to our integrated multi-omics view for targets and network analysis results for the drugs; (6) we implemented a highly interactive web interface for database browsing and network visualization. CONCLUSIONS Network visualization enabled by AlzGPS includes brain-specific neighborhood networks for genes-of-interest, endophenotype disease module networks for omics-of-interest, and mechanism-of-action networks for drugs targeting disease modules. By virtue of combining systems pharmacology and network-based integrative analysis of multi-omics data, AlzGPS offers actionable systems biology tools for accelerating therapeutic development in AD.
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Affiliation(s)
- Yadi Zhou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
| | - Jiansong Fang
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
| | - Lynn M Bekris
- 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
| | - Young Heon Kim
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
| | - Andrew A Pieper
- Harrington Discovery Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, 44106, USA
- Department of Psychiatry, Case Western Reserve University, Cleveland, OH, 44106, USA
- Geriatric Psychiatry, GRECC, Louis Stokes Cleveland VA Medical Center, Cleveland, OH, 44106, USA
- Institute for Transformative Molecular Medicine, School of Medicine, Case Western Reserve University, Cleveland, OH, 44106, USA
- Weill Cornell Autism Research Program, Weill Cornell Medicine of Cornell University, New York, NY, 10065, USA
- Department of Neuroscience, School of Medicine, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - James B Leverenz
- Lou Ruvo Center for Brain Health, Neurological Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
| | - Jeffrey Cummings
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, 89106, USA
- Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Sciences, UNLV, Las Vegas, NV, 89154, 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, School of Medicine, Case Western Reserve University, Cleveland, OH, 44106, USA.
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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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Wu Q, Fan X, Hong H, Gu Y, Liu Z, Fang S, Wang Q, Cai C, Fang J. Comprehensive assessment of side effects in COVID-19 drug pipeline from a network perspective. Food Chem Toxicol 2020; 145:111767. [PMID: 32971210 PMCID: PMC7505223 DOI: 10.1016/j.fct.2020.111767] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 09/09/2020] [Accepted: 09/17/2020] [Indexed: 12/21/2022]
Abstract
Currently, coronavirus disease 2019 (COVID-19), has posed an imminent threat to global public health. Although some current therapeutic agents have showed potential prevention or treatment, a growing number of associated adverse events have occurred on patients with COVID-19 in the course of medical treatment. Therefore, a comprehensive assessment of the safety profile of therapeutic agents against COVID-19 is urgently needed. In this study, we proposed a network-based framework to identify the potential side effects of current COVID-19 drugs in clinical trials. We established the associations between 116 COVID-19 drugs and 30 kinds of human tissues based on network proximity and gene-set enrichment analysis (GSEA) approaches. Additionally, we focused on four types of drug-induced toxicities targeting four tissues, including hepatotoxicity, renal toxicity, lung toxicity, and neurotoxicity, and validated our network-based predictions by preclinical and clinical evidence available. Finally, we further performed pharmacovigilance analysis to validate several drug-tissue toxicities via data mining adverse event reporting data, and we identified several new drug-induced side effects without labeling in Food and Drug Administration (FDA) drug instructions. Overall, this study provides forceful approaches to assess potential side effects on COVID-19 drugs, which will be helpful for their safe use in clinical practice and promoting the discovery of antiviral therapeutics against SARS-CoV-2.
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Affiliation(s)
- Qihui Wu
- Clinical Research Center, Hainan Provincial Hospital of Traditional Chinese Medicine, Guangzhou University of Chinese Medicine, Haikou, China; Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China.
| | - Xiude Fan
- Lerner Research Institute, Cleveland Clinic, Cleveland, USA.
| | - Honghai Hong
- Department of Clinical Laboratory, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
| | - Yong Gu
- Clinical Research Center, Hainan Provincial Hospital of Traditional Chinese Medicine, Guangzhou University of Chinese Medicine, Haikou, China.
| | - Zhihong Liu
- Guangdong Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou, China.
| | - Shuhuan Fang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China.
| | - Qi Wang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China.
| | - Chuipu Cai
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China.
| | - Jiansong Fang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China; Lerner Research Institute, Cleveland Clinic, Cleveland, USA.
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Bauzon J, Lee G, Cummings J. Repurposed agents in the Alzheimer's disease drug development pipeline. Alzheimers Res Ther 2020; 12:98. [PMID: 32807237 PMCID: PMC7433208 DOI: 10.1186/s13195-020-00662-x] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Accepted: 07/29/2020] [Indexed: 12/28/2022]
Abstract
BACKGROUND Treatments are needed to address the growing prevalence of Alzheimer's disease (AD). Clinical trials have failed to produce any AD drugs for Food and Drug Administration (FDA) approval since 2003, and the pharmaceutical development process is both time-consuming and costly. Drug repurposing provides an opportunity to accelerate this process by investigating the AD-related effects of agents approved for other indications. These drugs have known safety profiles, pharmacokinetic characterization, formulations, doses, and manufacturing processes. METHODS We assessed repurposed AD therapies represented in Phase I, Phase II, and Phase III of the current AD pipeline as registered on ClinicalTrials.gov as of February 27, 2020. RESULTS We identified 53 clinical trials involving 58 FDA-approved agents. Seventy-eight percent of the agents in trials had putative disease-modifying mechanisms of action. Of the repurposed drugs in the pipeline 20% are hematologic-oncologic agents, 18% are drugs derived from cardiovascular indications, 14% are agents with psychiatric uses, 12% are drug used to treat diabetes, 10% are neurologic agents, and the remaining 26% of drugs fall under other conditions. Intellectual property strategies utilized in these programs included using the same drug but altering doses, routes of administration, or formulations. Most repurposing trials were supported by Academic Medical Centers and were not funded through the biopharmaceutical industry. We compared our results to a European trial registry and found results similar to those derived from ClinicalTrials.gov. CONCLUSIONS Drug repurposing is a common approach to AD drug development and represents 39% of trials in the current AD pipeline. Therapies from many disease areas provide agents potentially useful in AD. Most of the repurposed agents are generic and a variety of intellectual property strategies have been adopted to enhance their economic value.
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Affiliation(s)
- Justin Bauzon
- School of Medicine, University of Nevada, Las Vegas (UNLV), Las Vegas, NV, 89154, USA
| | - Garam Lee
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, 89106, USA
| | - Jeffrey Cummings
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, 89106, USA.
- Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Sciences, University of Nevada, Las Vegas (UNLV), Las Vegas, NV, 89154, USA.
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