1
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Guan J, Fan Y, Wang S, Zhou F. Functions of MAP3Ks in antiviral immunity. Immunol Res 2023; 71:814-832. [PMID: 37286768 PMCID: PMC10247270 DOI: 10.1007/s12026-023-09401-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 06/01/2023] [Indexed: 06/09/2023]
Abstract
Immune signal transduction is crucial to the body's defense against viral infection. Recognition of pathogen-associated molecular patterns by pattern recognition receptors (PRRs) activates the transcription of interferon regulators and nuclear factor-κB (NF-κB); this promotes the release of interferons and inflammatory factors. Efficient regulation of type I interferon and NF-κB signaling by members of the mitogen-activated protein (MAP) kinase kinase kinase (MAP3K) family plays an important role in antiviral immunity. Elucidating the specific roles of MAP3K activation during viral infection is essential to develop effective antiviral therapies. In this review, we outline the specific regulatory mechanisms of MAP3Ks in antiviral immunity and discuss the feasibility of targeting MAP3Ks for the treatment of virus-induced diseases.
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Affiliation(s)
- Jizhong Guan
- Institutes of Biology and Medical Science, Soochow University, Suzhou, 215123, China
| | - Yao Fan
- The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen, 518033, China
| | - Shuai Wang
- Institutes of Biology and Medical Science, Soochow University, Suzhou, 215123, China
| | - Fangfang Zhou
- Institutes of Biology and Medical Science, Soochow University, Suzhou, 215123, China.
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2
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Mohamed EAR, Abdel-Rahman IM, Zaki MEA, Al-Khdhairawi A, Abdelhamid MM, Alqaisi AM, Rahim LBA, Abu-Hussein B, El-Sheikh AAK, Abdelwahab SF, Hassan HA. In silico prediction of potential inhibitors for SARS-CoV-2 Omicron variant using molecular docking and dynamics simulation-based drug repurposing. J Mol Model 2023; 29:70. [PMID: 36808314 PMCID: PMC9939377 DOI: 10.1007/s00894-023-05457-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 01/16/2023] [Indexed: 02/23/2023]
Abstract
BACKGROUND In November 2021, variant B.1.1.529 of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was identified by the World Health Organization (WHO) and designated Omicron. Omicron is characterized by a high number of mutations, thirty-two in total, making it more transmissible than the original virus. More than half of those mutations were found in the receptor-binding domain (RBD) that directly interacts with human angiotensin-converting enzyme 2 (ACE2). This study aimed to discover potent drugs against Omicron, which were previously repurposed for coronavirus disease 2019 (COVID-19). All repurposed anti-COVID-19 drugs were compiled from previous studies and tested against the RBD of SARS-CoV-2 Omicron. METHODS As a preliminary step, a molecular docking study was performed to investigate the potency of seventy-one compounds from four classes of inhibitors. The molecular characteristics of the best-performing five compounds were predicted by estimating the drug-likeness and drug score. Molecular dynamics simulations (MD) over 100 ns were performed to inspect the relative stability of the best compound within the Omicron receptor-binding site. RESULTS The current findings point out the crucial roles of Q493R, G496S, Q498R, N501Y, and Y505H in the RBD region of SARS-CoV-2 Omicron. Raltegravir, hesperidin, pyronaridine, and difloxacin achieved the highest drug scores compared with the other compounds in the four classes, with values of 81%, 57%, 18%, and 71%, respectively. The calculated results showed that raltegravir and hesperidin had high binding affinities and stabilities to Omicron with ΔGbinding of - 75.7304 ± 0.98324 and - 42.693536 ± 0.979056 kJ/mol, respectively. Further clinical studies should be performed for the two best compounds from this study.
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Affiliation(s)
- Eslam A. R. Mohamed
- Department of Chemistry, Faculty of Science, Minia University, Minia, 61511 Egypt
| | - Islam M. Abdel-Rahman
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Deraya University, New-Minia, 61519 Minia Egypt
| | - Magdi E. A. Zaki
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
| | - Ahmad Al-Khdhairawi
- Department of Biological Science and Biotechnology, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor Malaysia
| | - Mahmoud M. Abdelhamid
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Al-Azhar University, Asyut, 71524 Egypt
| | - Ahmad M. Alqaisi
- Chemistry Department, University of Jordan, Amman, 11942 Jordan
- Present Address: School for Engineering of Matter, Transport and Energy, Arizona State University, Tempe, AZ 85287 USA
| | - Lyana binti Abd Rahim
- Department of Medicine, Hospital Tuanku Ampuan Najihah, Kuala Pilah, Negeri Sembilan Malaysia
| | - Bilal Abu-Hussein
- Albayader Specialty Hospital, Amman, Jordan
- Present Address: Department of General Surgery, Cumberland Infirmary Hospital, Carlisle, England
| | - Azza A. K. El-Sheikh
- Basic Health Sciences Department, College of Medicine, Princess Nourah bint Abdulrahman University, P.O. 13 Box 84428, Riyadh, 11671 Saudi Arabia
| | - Sayed F. Abdelwahab
- Department of Pharmaceutics and Industrial Pharmacy, College of Pharmacy, Taif University, PO Box 11099, Taif, 21944 Saudi Arabia
| | - Heba Ali Hassan
- Department of Pharmacognosy, Faculty of Pharmacy, Sohag University, Sohag, 82524 Egypt
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3
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A systematic review of artificial intelligence-based COVID-19 modeling on multimodal genetic information. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2023; 179:1-9. [PMID: 36809830 PMCID: PMC9938959 DOI: 10.1016/j.pbiomolbio.2023.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 02/07/2023] [Accepted: 02/12/2023] [Indexed: 02/21/2023]
Abstract
This study systematically reviews the Artificial Intelligence (AI) methods developed to resolve the critical process of COVID-19 gene data analysis, including diagnosis, prognosis, biomarker discovery, drug responsiveness, and vaccine efficacy. This systematic review follows the guidelines of Preferred Reporting for Systematic Reviews and Meta-Analyses (PRISMA). We searched PubMed, Embase, Web of Science, and Scopus databases to identify the relevant articles from January 2020 to June 2022. It includes the published studies of AI-based COVID-19 gene modeling extracted through relevant keyword searches in academic databases. This study included 48 articles discussing AI-based genetic studies for several objectives. Ten articles confer about the COVID-19 gene modeling with computational tools, and five articles evaluated ML-based diagnosis with observed accuracy of 97% on SARS-CoV-2 classification. Gene-based prognosis study reviewed three articles and found host biomarkers detecting COVID-19 progression with 90% accuracy. Twelve manuscripts reviewed the prediction models with various genome analysis studies, nine articles examined the gene-based in silico drug discovery, and another nine investigated the AI-based vaccine development models. This study compiled the novel coronavirus gene biomarkers and targeted drugs identified through ML approaches from published clinical studies. This review provided sufficient evidence to delineate the potential of AI in analyzing complex gene information for COVID-19 modeling on multiple aspects like diagnosis, drug discovery, and disease dynamics. AI models entrenched a substantial positive impact by enhancing the efficiency of the healthcare system during the COVID-19 pandemic.
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4
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Turki T, Taguchi YH. A new machine learning based computational framework identifies therapeutic targets and unveils influential genes in pancreatic islet cells. Gene 2023; 853:147038. [PMID: 36503891 DOI: 10.1016/j.gene.2022.147038] [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: 09/16/2022] [Revised: 10/19/2022] [Accepted: 11/04/2022] [Indexed: 11/29/2022]
Abstract
Pancreatic islets comprise a group of cells that produce hormones regulating blood glucose levels. Particularly, the alpha and beta islet cells produce glucagon and insulin to stabilize blood glucose. When beta islet cells are dysfunctional, insulin is not secreted, inducing a glucose metabolic disorder. Identifying effective therapeutic targets against the disease is a complicated task and is not yet conclusive. To close the wide gap between understanding the molecular mechanism of pancreatic islet cells and providing effective therapeutic targets, we present a computational framework to identify potential therapeutic targets against pancreatic disorders. First, we downloaded three transcriptome expression profiling datasets pertaining to pancreatic islet cells (GSE87375, GSE79457, GSE110154) from the Gene Expression Omnibus database. For each dataset, we extracted expression profiles for two cell types. We then provided these expression profiles along with the cell types to our proposed constrained optimization problem of a support vector machine and to other existing methods, selecting important genes from the expression profiles. Finally, we performed (1) an evaluation from a classification perspective which showed the superiority of our methods against the baseline; and (2) an enrichment analysis which indicated that our methods achieved better outcomes. Results for the three datasets included 44 unique genes and 10 unique transcription factors (SP1, HDAC1, EGR1, E2F1, AR, STAT6, RELA, SP3, NFKB1, and ESR1) which are reportedly related to pancreatic islet functions, diseases, and therapeutic targets.
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Affiliation(s)
- Turki Turki
- King Abdulaziz University, Department of Computer Science, Jeddah 21589, Saudi Arabia.
| | - Y-H Taguchi
- Chuo University, Department of Physics, Tokyo 112-8551, Japan.
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5
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Adapted tensor decomposition and PCA based unsupervised feature extraction select more biologically reasonable differentially expressed genes than conventional methods. Sci Rep 2022; 12:17438. [PMID: 36261574 PMCID: PMC9580456 DOI: 10.1038/s41598-022-21474-z] [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: 03/07/2022] [Accepted: 09/27/2022] [Indexed: 01/12/2023] Open
Abstract
Tensor decomposition- and principal component analysis-based unsupervised feature extraction were proposed almost 5 and 10 years ago, respectively; although these methods have been successfully applied to a wide range of genome analyses, including drug repositioning, biomarker identification, and disease-causing genes' identification, some fundamental problems have been identified: the number of genes identified was too small to assume that there were no false negatives, and the histogram of P values derived was not fully coincident with the null hypothesis that principal component and singular value vectors follow the Gaussian distribution. Optimizing the standard deviation such that the histogram of P values is as much as possible coincident with the null hypothesis results in an increase in the number and biological reliability of the selected genes. Our contribution was that we improved these methods so as to be able to select biologically more reasonable differentially expressed genes than the state of art methods that must empirically assume negative binomial distributions and dispersion relation, which is required for the selecting more expressed genes than less expressed ones, which can be achieved by the proposed methods that do not have to assume these.
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6
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Taguchi YH, Turki T. Projection in genomic analysis: A theoretical basis to rationalize tensor decomposition and principal component analysis as feature selection tools. PLoS One 2022; 17:e0275472. [PMID: 36173994 PMCID: PMC9521941 DOI: 10.1371/journal.pone.0275472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 09/17/2022] [Indexed: 11/25/2022] Open
Abstract
Identifying differentially expressed genes is difficult because of the small number of available samples compared with the large number of genes. Conventional gene selection methods employing statistical tests have the critical problem of heavy dependence of P-values on sample size. Although the recently proposed principal component analysis (PCA) and tensor decomposition (TD)-based unsupervised feature extraction (FE) has often outperformed these statistical test-based methods, the reason why they worked so well is unclear. In this study, we aim to understand this reason in the context of projection pursuit (PP) that was proposed a long time ago to solve the problem of dimensions; we can relate the space spanned by singular value vectors with that spanned by the optimal cluster centroids obtained from K-means. Thus, the success of PCA- and TD-based unsupervised FE can be understood by this equivalence. In addition to this, empirical threshold adjusted P-values of 0.01 assuming the null hypothesis that singular value vectors attributed to genes obey the Gaussian distribution empirically corresponds to threshold-adjusted P-values of 0.1 when the null distribution is generated by gene order shuffling. For this purpose, we newly applied PP to the three data sets to which PCA and TD based unsupervised FE were previously applied; these data sets treated two topics, biomarker identification for kidney cancers (the first two) and the drug discovery for COVID-19 (the thrid one). Then we found the coincidence between PP and PCA or TD based unsupervised FE is pretty well. Shuffling procedures described above are also successfully applied to these three data sets. These findings thus rationalize the success of PCA- and TD-based unsupervised FE for the first time.
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Affiliation(s)
- Y-h. Taguchi
- Department of Physics, Chuo University, Bunkyo-ku, Tokyo, Japan
- * E-mail:
| | - Turki Turki
- Department of Computer Science, King Abdulaziz University, Jeddah, Saudi Arabia
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7
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Fuzo CA, Martins RB, Fraga-Silva TFC, Amstalden MK, Canassa De Leo T, Souza JP, Lima TM, Faccioli LH, Okamoto DN, Juliano MA, França SC, Juliano L, Bonato VLD, Arruda E, Dias-Baruffi M. Celastrol: A lead compound that inhibits SARS-CoV-2 replication, the activity of viral and human cysteine proteases, and virus-induced IL-6 secretion. Drug Dev Res 2022; 83:1623-1640. [PMID: 35989498 PMCID: PMC9539158 DOI: 10.1002/ddr.21982] [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: 03/31/2022] [Revised: 06/30/2022] [Accepted: 07/11/2022] [Indexed: 11/21/2022]
Abstract
The global emergence of coronavirus disease 2019 (COVID‐19) has caused substantial human casualties. Clinical manifestations of this disease vary from asymptomatic to lethal, and the symptomatic form can be associated with cytokine storm and hyperinflammation. In face of the urgent demand for effective drugs to treat COVID‐19, we have searched for candidate compounds using in silico approach followed by experimental validation. Here we identified celastrol, a pentacyclic triterpene isolated from Tripterygium wilfordii Hook F, as one of the best compounds out of 39 drug candidates. Celastrol reverted the gene expression signature from severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2)‐infected cells and irreversibly inhibited the recombinant forms of the viral and human cysteine proteases involved in virus invasion, such as Mpro (main protease), PLpro (papain‐like protease), and recombinant human cathepsin L. Celastrol suppressed SARS‐CoV‐2 replication in human and monkey cell lines and decreased interleukin‐6 (IL‐6) secretion in the SARS‐CoV‐2‐infected human cell line. Celastrol acted in a concentration‐dependent manner, with undetectable signs of cytotoxicity, and inhibited in vitro replication of the parental and SARS‐CoV‐2 variant. Therefore, celastrol is a promising lead compound to develop new drug candidates to face COVID‐19 due to its ability to suppress SARS‐CoV‐2 replication and IL‐6 production in infected cells.
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Affiliation(s)
- Carlos A Fuzo
- Departamento de Análises Clínicas, Toxicológicas e Bromatológicas, Faculdade de Ciências Farmacêuticas de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, São Paulo, Brazil
| | - Ronaldo B Martins
- Departamento de Biologia Celular e Molecular e Bioagentes Patogênicos, Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, São Paulo, Brazil
| | - Thais F C Fraga-Silva
- Departamento de Bioquímica e Imunologia, Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, São Paulo, Brazil
| | - Martin K Amstalden
- Departamento de Análises Clínicas, Toxicológicas e Bromatológicas, Faculdade de Ciências Farmacêuticas de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, São Paulo, Brazil
| | - Thais Canassa De Leo
- Departamento de Análises Clínicas, Toxicológicas e Bromatológicas, Faculdade de Ciências Farmacêuticas de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, São Paulo, Brazil
| | - Juliano P Souza
- Departamento de Biologia Celular e Molecular e Bioagentes Patogênicos, Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, São Paulo, Brazil
| | - Thais M Lima
- Departamento de Biologia Celular e Molecular e Bioagentes Patogênicos, Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, São Paulo, Brazil
| | - Lucia H Faccioli
- Departamento de Análises Clínicas, Toxicológicas e Bromatológicas, Faculdade de Ciências Farmacêuticas de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, São Paulo, Brazil
| | - Débora Noma Okamoto
- Departamento de Biofísica, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, São Paulo, Brazil
| | - Maria Aparecida Juliano
- Departamento de Biofísica, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, São Paulo, Brazil
| | - Suzelei C França
- Unidade de Biotecnologia, Universidade de Ribeirão Preto, Ribeirão Preto, São Paulo, Brazil
| | - Luiz Juliano
- Departamento de Biofísica, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, São Paulo, Brazil
| | - Vania L D Bonato
- Departamento de Bioquímica e Imunologia, Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, São Paulo, Brazil
| | - Eurico Arruda
- Departamento de Biologia Celular e Molecular e Bioagentes Patogênicos, Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, São Paulo, Brazil
| | - Marcelo Dias-Baruffi
- Departamento de Análises Clínicas, Toxicológicas e Bromatológicas, Faculdade de Ciências Farmacêuticas de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, São Paulo, Brazil
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8
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Barati N, Motavallihaghi S, Nikfar B, Chaichian S, Momtazi-Borojeni AA. Potential therapeutic effects of Ivermectin in COVID-19. Exp Biol Med (Maywood) 2022; 247:1388-1396. [PMID: 35686662 PMCID: PMC9442455 DOI: 10.1177/15353702221099579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
COVID-19 is a critical pandemic that affected communities around the world, and there is currently no specific drug treatment for it. The virus enters the human cells via spikes and induces cytokine production and finally arrests the cell cycle. Ivermectin shows therapeutic potential for treating COVID-19 infection based on in vitro studies. Docking studies have shown a strong affinity between Ivermectin and some virulence factors of COVID-19. Notably, clinical evidence has demonstrated that Ivermectin with usual doses is effective by both the prophylactic and therapeutic approaches in all phases of the disease. Ivermectin inhibits both the adhesion and replication of the virus. Local therapy of the lung with Ivermectin or combination therapy may get better results and decrease the dose of the drug.
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Affiliation(s)
- Nastaran Barati
- Research Center For Molecular
Medicine, Hamadan University of Medical Sciences, Hamadan 9174223425,
Iran,Medicinal Plants and Natural
Products Research Center, Hamadan University of Medical Sciences, Hamadan
9174223425, Iran
| | | | - Banafsheh Nikfar
- Pars Advanced and Minimally
Invasive Medical Manners Research Center, Pars Hospital, Iran University of
Medical Sciences, Tehran 1415944911, Iran
| | - Shahla Chaichian
- Pars Advanced and Minimally
Invasive Medical Manners Research Center, Pars Hospital, Iran University of
Medical Sciences, Tehran 1415944911, Iran
| | - Amir Abbas Momtazi-Borojeni
- Department of Medical
Biotechnology, Faculty of Medicine, Mashhad University of Medical Sciences,
Mashhad 8167994434, Iran,Amir Abbas Momtazi-Borojeni.
Emails: ;
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9
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Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system. Mol Divers 2022; 27:959-985. [PMID: 35819579 DOI: 10.1007/s11030-022-10489-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 06/21/2022] [Indexed: 12/11/2022]
Abstract
CNS disorders are indications with a very high unmet medical needs, relatively smaller number of available drugs, and a subpar satisfaction level among patients and caregiver. Discovery of CNS drugs is extremely expensive affair with its own unique challenges leading to extremely high attrition rates and low efficiency. With explosion of data in information age, there is hardly any aspect of life that has not been touched by data driven technologies such as artificial intelligence (AI) and machine learning (ML). Drug discovery is no exception, emergence of big data via genomic, proteomic, biological, and chemical technologies has driven pharmaceutical giants to collaborate with AI oriented companies to revolutionise drug discovery, with the goal of increasing the efficiency of the process. In recent years many examples of innovative applications of AI and ML techniques in CNS drug discovery has been reported. Research on therapeutics for diseases such as schizophrenia, Alzheimer's and Parkinsonism has been provided with a new direction and thrust from these developments. AI and ML has been applied to both ligand-based and structure-based drug discovery and design of CNS therapeutics. In this review, we have summarised the general aspects of AI and ML from the perspective of drug discovery followed by a comprehensive coverage of the recent developments in the applications of AI/ML techniques in CNS drug discovery.
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10
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Analyzing the Systems Biology Effects of COVID-19 mRNA Vaccines to Assess Their Safety and Putative Side Effects. Pathogens 2022; 11:pathogens11070743. [PMID: 35889989 PMCID: PMC9320269 DOI: 10.3390/pathogens11070743] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 06/11/2022] [Accepted: 06/25/2022] [Indexed: 01/25/2023] Open
Abstract
COVID-19 vaccines have been instrumental tools in reducing the impact of SARS-CoV-2 infections around the world by preventing 80% to 90% of hospitalizations and deaths from reinfection, in addition to preventing 40% to 65% of symptomatic illnesses. However, the simultaneous large-scale vaccination of the global population will indubitably unveil heterogeneity in immune responses as well as in the propensity to developing post-vaccine adverse events, especially in vulnerable individuals. Herein, we applied a systems biology workflow, integrating vaccine transcriptional signatures with chemogenomics, to study the pharmacological effects of mRNA vaccines. First, we derived transcriptional signatures and predicted their biological effects using pathway enrichment and network approaches. Second, we queried the Connectivity Map (CMap) to prioritize adverse events hypotheses. Finally, we accepted higher-confidence hypotheses that have been predicted by independent approaches. Our results reveal that the mRNA-based BNT162b2 vaccine affects immune response pathways related to interferon and cytokine signaling, which should lead to vaccine success, but may also result in some adverse events. Our results emphasize the effects of BNT162b2 on calcium homeostasis, which could be contributing to some frequently encountered adverse events related to mRNA vaccines. Notably, cardiac side effects were signaled in the CMap query results. In summary, our approach has identified mechanisms underlying both the expected protective effects of vaccination as well as possible post-vaccine adverse effects. Our study illustrates the power of systems biology approaches in improving our understanding of the comprehensive biological response to vaccination against COVID-19.
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11
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Kumari P, Pradhan B, Koromina M, Patrinos GP, Steen KV. Discovery of new drug indications for COVID-19: A drug repurposing approach. PLoS One 2022; 17:e0267095. [PMID: 35609015 PMCID: PMC9129022 DOI: 10.1371/journal.pone.0267095] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 04/03/2022] [Indexed: 11/19/2022] Open
Abstract
Motivation
The outbreak of coronavirus health issues caused by COVID-19(SARS-CoV-2) creates a global threat to public health. Therefore, there is a need for effective remedial measures using existing and approved therapies with proven safety measures has several advantages. Dexamethasone (Pubchem ID: CID0000005743), baricitinib(Pubchem ID: CID44205240), remdesivir (PubchemID: CID121304016) are three generic drugs that have demonstrated in-vitro high antiviral activity against SARS-CoV-2. The present study aims to widen the search and explore the anti-SARS-CoV-2 properties of these potential drugs while looking for new drug indications with optimised benefits via in-silico research.
Method
Here, we designed a unique drug-similarity model to repurpose existing drugs against SARS-CoV-2, using the anti-Covid properties of dexamethasone, baricitinib, and remdesivir as references. Known chemical-chemical interactions of reference drugs help extract interactive compounds withimprovedanti-SARS-CoV-2 properties. Here, we calculated the likelihood of these drug compounds treating SARS-CoV-2 related symptoms using chemical-protein interactions between the interactive compounds of the reference drugs and SARS-CoV-2 target genes. In particular, we adopted a two-tier clustering approach to generate a drug similarity model for the final selection of potential anti-SARS-CoV-2 drug molecules. Tier-1 clustering was based on t-Distributed Stochastic Neighbor Embedding (t-SNE) and aimed to filter and discard outlier drugs. The tier-2 analysis incorporated two cluster analyses performed in parallel using Ordering Points To Identify the Clustering Structure (OPTICS) and Hierarchical Agglomerative Clustering (HAC). As a result, itidentified clusters of drugs with similar actions. In addition, we carried out a docking study for in-silico validation of top candidate drugs.
Result
Our drug similarity model highlighted ten drugs, including reference drugs that can act as potential therapeutics against SARS-CoV-2. The docking results suggested that doxorubicin showed the least binding energy compared to reference drugs. Their practical utility as anti-SARS-CoV-2 drugs, either individually or in combination, warrants further investigation.
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Affiliation(s)
- Priyanka Kumari
- GIGA-R Medical Genomics - BIO3 Systems Genomics, University of Liège, Liège, Belgium
- Laboratory of Pharmaceutical Analytical Chemistry, CIRM, University of Liège, Liège, Belgium
- * E-mail: (PK); (KVS)
| | - Bikram Pradhan
- Indian Space Research Organisation (ISRO) Headquarters, Bengaluru, India
| | - Maria Koromina
- University of Patras, School of Health Sciences, Department of Pharmacy, Patras, Greece
| | - George P. Patrinos
- University of Patras, School of Health Sciences, Department of Pharmacy, Patras, Greece
- Department of Pathology, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, UAE
- Zayed Bin Sultan Center for Health Sciences, United Arab Emirates University, Al Ain, UAE
| | - Kristel Van Steen
- GIGA-R Medical Genomics - BIO3 Systems Genomics, University of Liège, Liège, Belgium
- Department of Human Genetics - BIO3 Systems Medicine, University of Leuven, Leuven, Belgium
- * E-mail: (PK); (KVS)
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12
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Vázquez-Mendoza LH, Mendoza-Figueroa HL, García-Vázquez JB, Correa-Basurto J, García-Machorro J. In Silico Drug Repositioning to Target the SARS-CoV-2 Main Protease as Covalent Inhibitors Employing a Combined Structure-Based Virtual Screening Strategy of Pharmacophore Models and Covalent Docking. Int J Mol Sci 2022; 23:ijms23073987. [PMID: 35409348 PMCID: PMC8999907 DOI: 10.3390/ijms23073987] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Revised: 02/17/2022] [Accepted: 02/21/2022] [Indexed: 02/01/2023] Open
Abstract
The epidemic caused by the SARS-CoV-2 coronavirus, which has spread rapidly throughout the world, requires urgent and effective treatments considering that the appearance of viral variants limits the efficacy of vaccines. The main protease of SARS-CoV-2 (Mpro) is a highly conserved cysteine proteinase, fundamental for the replication of the coronavirus and with a specific cleavage mechanism that positions it as an attractive therapeutic target for the proposal of irreversible inhibitors. A structure-based strategy combining 3D pharmacophoric modeling, virtual screening, and covalent docking was employed to identify the interactions required for molecular recognition, as well as the spatial orientation of the electrophilic warhead, of various drugs, to achieve a covalent interaction with Cys145 of Mpro. The virtual screening on the structure-based pharmacophoric map of the SARS-CoV-2 Mpro in complex with an inhibitor N3 (reference compound) provided high efficiency by identifying 53 drugs (FDA and DrugBank databases) with probabilities of covalent binding, including N3 (Michael acceptor) and others with a variety of electrophilic warheads. Adding the energy contributions of affinity for non-covalent and covalent docking, 16 promising drugs were obtained. Our findings suggest that the FDA-approved drugs Vaborbactam, Cimetidine, Ixazomib, Scopolamine, and Bicalutamide, as well as the other investigational peptide-like drugs (DB04234, DB03456, DB07224, DB7252, and CMX-2043) are potential covalent inhibitors of SARS-CoV-2 Mpro.
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Affiliation(s)
- Luis Heriberto Vázquez-Mendoza
- Laboratorio de Diseño y Desarrollo de Nuevos Fármacos e Innovación Biotecnológica, Posgrado en Farmacología de la Escuela Superior de Medicina del Instituto Politécnico Nacional, Plan de San Luis y Salvador Díaz Mirón s/n, Casco de Santo Tomás, Ciudad de Mexico 11340, Mexico; (L.H.V.-M.); (J.C.-B.)
| | - Humberto L. Mendoza-Figueroa
- Laboratorio de Diseño y Desarrollo de Nuevos Fármacos e Innovación Biotecnológica, Posgrado en Farmacología de la Escuela Superior de Medicina del Instituto Politécnico Nacional, Plan de San Luis y Salvador Díaz Mirón s/n, Casco de Santo Tomás, Ciudad de Mexico 11340, Mexico; (L.H.V.-M.); (J.C.-B.)
- Correspondence: (H.L.M.-F.); (J.B.G.-V.)
| | - Juan Benjamín García-Vázquez
- Laboratorio de Diseño y Desarrollo de Nuevos Fármacos e Innovación Biotecnológica, Posgrado en Farmacología de la Escuela Superior de Medicina del Instituto Politécnico Nacional, Plan de San Luis y Salvador Díaz Mirón s/n, Casco de Santo Tomás, Ciudad de Mexico 11340, Mexico; (L.H.V.-M.); (J.C.-B.)
- Cátedras CONACyT-Sección de Estudios de Posgrado e Investigación, Escuela Superior de Medicina, Instituto Politécnico Nacional, Plan de San Luis y Díaz Mirón s/n, Casco de Santo Tomás, Ciudad de Mexico 11340, Mexico
- Correspondence: (H.L.M.-F.); (J.B.G.-V.)
| | - José Correa-Basurto
- Laboratorio de Diseño y Desarrollo de Nuevos Fármacos e Innovación Biotecnológica, Posgrado en Farmacología de la Escuela Superior de Medicina del Instituto Politécnico Nacional, Plan de San Luis y Salvador Díaz Mirón s/n, Casco de Santo Tomás, Ciudad de Mexico 11340, Mexico; (L.H.V.-M.); (J.C.-B.)
| | - Jazmín García-Machorro
- Laboratorio de Medicina de la Conservación, Escuela Superior de Medicina del Instituto Politécnico Nacional, Plan de San Luis y Salvador Díaz Mirón s/n, Casco de Santo Tomás, Ciudad de Mexico 11340, Mexico;
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Qu N, Hui Z, Shen Z, Kan C, Hou N, Sun X, Han F. Thyroid Cancer and COVID-19: Prospects for Therapeutic Approaches and Drug Development. Front Endocrinol (Lausanne) 2022; 13:873027. [PMID: 35600591 PMCID: PMC9114699 DOI: 10.3389/fendo.2022.873027] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 04/04/2022] [Indexed: 02/05/2023] Open
Abstract
Thyroid cancer is the most prevalent endocrine malignancy and the reported incidence of thyroid cancer has continued to increase in recent years. Since 2019, coronavirus disease 2019 (COVID-19) has been spreading worldwide in a global pandemic. COVID-19 aggravates primary illnesses and affects disease management; relevant changes include delayed diagnosis and treatment. The thyroid is an endocrine organ that is susceptible to autoimmune attack; thus, thyroid cancer after COVID-19 has gradually attracted attention. Whether COVID-19 affects the diagnosis and treatment of thyroid cancer has also attracted the attention of many researchers. This review examines the literature regarding the influence of COVID-19 on the pathogenesis, diagnosis, and treatment of thyroid cancer; it also focuses on drug therapies to promote research into strategies for improving therapy and management in thyroid cancer patients with COVID-19.
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Affiliation(s)
- Na Qu
- Department of Endocrinology and Metabolism, Affiliated Hospital of Weifang Medical University, Weifang, China
- Clinical Research Center, Affiliated Hospital of Weifang Medical University, Weifang, China
- Department of Pathology, Affiliated Hospital of Weifang Medical University, Weifang, China
| | - Zongguang Hui
- Department of Endocrinology and Metabolism, Affiliated Hospital of Weifang Medical University, Weifang, China
- Clinical Research Center, Affiliated Hospital of Weifang Medical University, Weifang, China
| | - Zhixin Shen
- Department of Breast and Thyroid Surgery, Affiliated Hospital of Weifang Medical University, Weifang, China
| | - Chengxia Kan
- Department of Endocrinology and Metabolism, Affiliated Hospital of Weifang Medical University, Weifang, China
- Clinical Research Center, Affiliated Hospital of Weifang Medical University, Weifang, China
| | - Ningning Hou
- Department of Endocrinology and Metabolism, Affiliated Hospital of Weifang Medical University, Weifang, China
- Clinical Research Center, Affiliated Hospital of Weifang Medical University, Weifang, China
| | - Xiaodong Sun
- Department of Endocrinology and Metabolism, Affiliated Hospital of Weifang Medical University, Weifang, China
- Clinical Research Center, Affiliated Hospital of Weifang Medical University, Weifang, China
- *Correspondence: Fang Han, ; Xiaodong Sun,
| | - Fang Han
- Department of Endocrinology and Metabolism, Affiliated Hospital of Weifang Medical University, Weifang, China
- Clinical Research Center, Affiliated Hospital of Weifang Medical University, Weifang, China
- Department of Pathology, Affiliated Hospital of Weifang Medical University, Weifang, China
- *Correspondence: Fang Han, ; Xiaodong Sun,
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14
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Kashid S, Suttee A, Kadam P, Khatik GL, Kasarla R. An In- Silico Studies for Immunomodulatory Potential of Phytoconstituents from a Naturally Occurring Herb Nigella Sativa. PHARMACOPHORE 2022. [DOI: 10.51847/e6ckqdrhe4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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15
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Aronskyy I, Masoudi-Sobhanzadeh Y, Cappuccio A, Zaslavsky E. Advances in the computational landscape for repurposed drugs against COVID-19. Drug Discov Today 2021; 26:2800-2815. [PMID: 34339864 PMCID: PMC8323501 DOI: 10.1016/j.drudis.2021.07.026] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 04/30/2021] [Accepted: 07/26/2021] [Indexed: 02/07/2023]
Abstract
The COVID-19 pandemic has caused millions of deaths and massive societal distress worldwide. Therapeutic solutions are urgently needed, but de novo drug development remains a lengthy process. One promising alternative is computational drug repurposing, which enables the prioritization of existing compounds through fast in silico analyses. Recent efforts based on molecular docking, machine learning, and network analysis have produced actionable predictions. Some predicted drugs, targeting viral proteins and pathological host pathways are undergoing clinical trials. Here, we review this work, highlight drugs with high predicted efficacy and classify their mechanisms of action. We discuss the strengths and limitations of the published methodologies and outline possible future directions. Finally, we curate a list of COVID-19 data portals and other repositories that could be used to accelerate future research.
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Affiliation(s)
- Illya Aronskyy
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Yosef Masoudi-Sobhanzadeh
- Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Antonio Cappuccio
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA,Corresponding authors
| | - Elena Zaslavsky
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA,Corresponding authors
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16
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Lucchetta M, Pellegrini M. Drug repositioning by merging active subnetworks validated in cancer and COVID-19. Sci Rep 2021; 11:19839. [PMID: 34615934 PMCID: PMC8494853 DOI: 10.1038/s41598-021-99399-2] [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: 06/20/2021] [Accepted: 09/23/2021] [Indexed: 02/08/2023] Open
Abstract
Computational drug repositioning aims at ranking and selecting existing drugs for novel diseases or novel use in old diseases. In silico drug screening has the potential for speeding up considerably the shortlisting of promising candidates in response to outbreaks of diseases such as COVID-19 for which no satisfactory cure has yet been found. We describe DrugMerge as a methodology for preclinical computational drug repositioning based on merging multiple drug rankings obtained with an ensemble of disease active subnetworks. DrugMerge uses differential transcriptomic data on drugs and diseases in the context of a large gene co-expression network. Experiments with four benchmark diseases demonstrate that our method detects in first position drugs in clinical use for the specified disease, in all four cases. Application of DrugMerge to COVID-19 found rankings with many drugs currently in clinical trials for COVID-19 in top positions, thus showing that DrugMerge can mimic human expert judgment.
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Affiliation(s)
- Marta Lucchetta
- Institute of Informatics and Telematics (IIT), CNR, Pisa, 56124, Italy
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, Siena, 53100, Italy
| | - Marco Pellegrini
- Institute of Informatics and Telematics (IIT), CNR, Pisa, 56124, Italy.
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Asada K, Komatsu M, Shimoyama R, Takasawa K, Shinkai N, Sakai A, Bolatkan A, Yamada M, Takahashi S, Machino H, Kobayashi K, Kaneko S, Hamamoto R. Application of Artificial Intelligence in COVID-19 Diagnosis and Therapeutics. J Pers Med 2021; 11:886. [PMID: 34575663 PMCID: PMC8471764 DOI: 10.3390/jpm11090886] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 09/01/2021] [Accepted: 09/02/2021] [Indexed: 12/12/2022] Open
Abstract
The coronavirus disease 2019 (COVID-19) pandemic began at the end of December 2019, giving rise to a high rate of infections and causing COVID-19-associated deaths worldwide. It was first reported in Wuhan, China, and since then, not only global leaders, organizations, and pharmaceutical/biotech companies, but also researchers, have directed their efforts toward overcoming this threat. The use of artificial intelligence (AI) has recently surged internationally and has been applied to diverse aspects of many problems. The benefits of using AI are now widely accepted, and many studies have shown great success in medical research on tasks, such as the classification, detection, and prediction of disease, or even patient outcome. In fact, AI technology has been actively employed in various ways in COVID-19 research, and several clinical applications of AI-equipped medical devices for the diagnosis of COVID-19 have already been reported. Hence, in this review, we summarize the latest studies that focus on medical imaging analysis, drug discovery, and therapeutics such as vaccine development and public health decision-making using AI. This survey clarifies the advantages of using AI in the fight against COVID-19 and provides future directions for tackling the COVID-19 pandemic using AI techniques.
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Affiliation(s)
- Ken Asada
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Masaaki Komatsu
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Ryo Shimoyama
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Ken Takasawa
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Norio Shinkai
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
- Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Akira Sakai
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
- Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Amina Bolatkan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Masayoshi Yamada
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
- Department of Endoscopy, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan
| | - Satoshi Takahashi
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Hidenori Machino
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Kazuma Kobayashi
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Syuzo Kaneko
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Ryuji Hamamoto
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
- Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
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Taguchi YH, Turki T. Application of Tensor Decomposition to Gene Expression of Infection of Mouse Hepatitis Virus Can Identify Critical Human Genes and Efffective Drugs for SARS-CoV-2 Infection. IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2021; 15:746-758. [PMID: 34812273 PMCID: PMC8545047 DOI: 10.1109/jstsp.2021.3061251] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 12/06/2020] [Accepted: 02/18/2021] [Indexed: 01/19/2023]
Abstract
To better understand the genes with altered expression caused by infection with the novel coronavirus strain SARS-CoV-2 causing COVID-19 infectious disease, a tensor decomposition (TD)-based unsupervised feature extraction (FE) approach was applied to a gene expression profile dataset of the mouse liver and spleen with experimental infection of mouse hepatitis virus, which is regarded as a suitable model of human coronavirus infection. TD-based unsupervised FE selected 134 altered genes, which were enriched in protein-protein interactions with orf1ab, polyprotein, and 3C-like protease that are well known to play critical roles in coronavirus infection, suggesting that these 134 genes can represent the coronavirus infectious process. We then selected compounds targeting the expression of the 134 selected genes based on a public domain database. The identified drug compounds were mainly related to known antiviral drugs, several of which were also included in those previously screened with an in silico method to identify candidate drugs for treating COVID-19.
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Affiliation(s)
- Y-H. Taguchi
- Department of PhysicsChuo UniversityTokyo112-8551Japan
| | - Turki Turki
- Department of Computer ScienceKing Abdulaziz UniversityJeddah21589Saudi Arabia
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19
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Mathematical formulation and application of kernel tensor decomposition based unsupervised feature extraction. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2021.106834] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Wu YH, Yeh IJ, Phan NN, Yen MC, Hung JH, Chiao CC, Chen CF, Sun Z, Hsu HP, Wang CY, Lai MD. Gene signatures and potential therapeutic targets of Middle East respiratory syndrome coronavirus (MERS-CoV)-infected human lung adenocarcinoma epithelial cells. JOURNAL OF MICROBIOLOGY, IMMUNOLOGY, AND INFECTION = WEI MIAN YU GAN RAN ZA ZHI 2021; 54:845-857. [PMID: 34176764 PMCID: PMC7997684 DOI: 10.1016/j.jmii.2021.03.007] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Revised: 12/03/2020] [Accepted: 03/07/2021] [Indexed: 12/23/2022]
Abstract
Background Pathogenic coronaviruses include Middle East respiratory syndrome coronavirus (MERS-CoV), severe acute respiratory syndrome coronavirus (SARS-CoV), and SARS-CoV-2. These viruses have induced outbreaks worldwide, and there are currently no effective medications against them. Therefore, there is an urgent need to develop potential drugs against coronaviruses. Methods High-throughput technology is widely used to explore differences in messenger (m)RNA and micro (mi)RNA expression profiles, especially to investigate protein–protein interactions and search for new therapeutic compounds. We integrated miRNA and mRNA expression profiles in MERS-CoV-infected cells and compared them to mock-infected controls from public databases. Results Through the bioinformatics analysis, there were 251 upregulated genes and eight highly differentiated miRNAs that overlapped in the two datasets. External validation verified that these genes had high expression in MERS-CoV-infected cells, including RC3H1, NF-κB, CD69, TNFAIP3, LEAP-2, DUSP10, CREB5, CXCL2, etc. We revealed that immune, olfactory or sensory system-related, and signal-transduction networks were discovered from upregulated mRNAs in MERS-CoV-infected cells. In total, 115 genes were predicted to be related to miRNAs, with the intersection of upregulated mRNAs and miRNA-targeting prediction genes such as TCF4, NR3C1, and POU2F2. Through the Connectivity Map (CMap) platform, we suggested potential compounds to use against MERS-CoV infection, including diethylcarbamazine, harpagoside, bumetanide, enalapril, and valproic acid. Conclusions The present study illustrates the crucial roles of miRNA-mRNA interacting networks in MERS-CoV-infected cells. The genes we identified are potential targets for treating MERS-CoV infection; however, these could possibly be extended to other coronavirus infections.
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Affiliation(s)
- Yen-Hung Wu
- Department of Emergency Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 80708, Taiwan; Graduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan
| | - I-Jeng Yeh
- Department of Emergency Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 80708, Taiwan; Graduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan
| | - Nam Nhut Phan
- NTT Institute of Hi-Technology, Nguyen Tat Thanh University, Ho Chi Minh City, Viet Nam
| | - Meng-Chi Yen
- Department of Emergency Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 80708, Taiwan; Graduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan
| | - Jui-Hsiang Hung
- Department of Biotechnology, Chia Nan University of Pharmacy and Science, Tainan 71710, Taiwan
| | - Chung-Chieh Chiao
- School of Chinese Medicine for Post-Baccalaureate, I-Shou University, Kaohsiung 82445, Taiwan
| | - Chien-Fu Chen
- School of Chinese Medicine for Post-Baccalaureate, I-Shou University, Kaohsiung 82445, Taiwan
| | - Zhengda Sun
- Kaiser Permanente, Northern California Regional Laboratories, The Permanente Medical Group, 1725 Eastshore Hwy, Berkeley, CA 94710, USA
| | - Hui-Ping Hsu
- Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 70101, Taiwan; Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN37232, USA.
| | - Chih-Yang Wang
- Ph.D. Program for Cancer Molecular Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, Taiwan; Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, Taiwan.
| | - Ming-Derg Lai
- Department of Biochemistry and Molecular Biology, National Cheng Kung University, Tainan 70101, Taiwan; Institute of Basic Medical Sciences, College of Medicine, National Cheng Kung University, Tainan 70101, Taiwan.
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Goris T, Pérez‐Valero Á, Martínez I, Yi D, Fernández‐Calleja L, San León D, Bornscheuer UT, Magadán‐Corpas P, Lombó F, Nogales J. Repositioning microbial biotechnology against COVID-19: the case of microbial production of flavonoids. Microb Biotechnol 2021; 14:94-110. [PMID: 33047877 PMCID: PMC7675739 DOI: 10.1111/1751-7915.13675] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 09/17/2020] [Accepted: 09/18/2020] [Indexed: 12/19/2022] Open
Abstract
Coronavirus-related disease 2019 (COVID-19) became a pandemic in February 2020, and worldwide researchers try to tackle the disease with approved drugs of all kinds, or to develop novel compounds inhibiting viral spreading. Flavonoids, already investigated as antivirals in general, also might bear activities specific for the viral agent causing COVID-19, SARS-CoV-2. Microbial biotechnology and especially synthetic biology may help to produce flavonoids, which are exclusive plant secondary metabolites, at a larger scale or indeed to find novel pharmaceutically active flavonoids. Here, we review the state of the art in (i) antiviral activity of flavonoids specific for coronaviruses and (ii) results derived from computational studies, mostly docking studies mainly inhibiting specific coronaviral proteins such as the 3CL (main) protease, the spike protein or the RNA-dependent RNA polymerase. In the end, we strive towards a synthetic biology pipeline making the fast and tailored production of valuable antiviral flavonoids possible by applying the last concepts of division of labour through co-cultivation/microbial community approaches to the DBTL (Design, Build, Test, Learn) principle.
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Affiliation(s)
- Tobias Goris
- Department of Molecular Toxicology, Research Group Intestinal MicrobiologyGerman Institute of Human Nutrition Potsdam‐RehbrueckeArthur‐Scheunert‐Allee 114‐116NuthetalBrandenburg14558Germany
| | - Álvaro Pérez‐Valero
- Research Unit “Biotechnology in Nutraceuticals and Bioactive Compounds‐BIONUC”Departamento de Biología Funcional, Área de MicrobiologíaUniversidad de OviedoOviedoSpain
- Instituto Universitario de Oncología del Principado de AsturiasOviedoSpain
- Instituto de Investigación Sanitaria del Principado de AsturiasOviedoSpain
| | - Igor Martínez
- Department of Systems BiologyCentro Nacional de BiotecnologíaCSICMadridSpain
| | - Dong Yi
- Department of Biotechnology & Enzyme CatalysisInstitute of BiochemistryUniversity GreifswaldFelix‐Hausdorff‐Str. 4GreifswaldD‐17487Germany
| | - Luis Fernández‐Calleja
- Research Unit “Biotechnology in Nutraceuticals and Bioactive Compounds‐BIONUC”Departamento de Biología Funcional, Área de MicrobiologíaUniversidad de OviedoOviedoSpain
- Instituto Universitario de Oncología del Principado de AsturiasOviedoSpain
- Instituto de Investigación Sanitaria del Principado de AsturiasOviedoSpain
| | - David San León
- Department of Systems BiologyCentro Nacional de BiotecnologíaCSICMadridSpain
| | - Uwe T. Bornscheuer
- Department of Biotechnology & Enzyme CatalysisInstitute of BiochemistryUniversity GreifswaldFelix‐Hausdorff‐Str. 4GreifswaldD‐17487Germany
| | - Patricia Magadán‐Corpas
- Research Unit “Biotechnology in Nutraceuticals and Bioactive Compounds‐BIONUC”Departamento de Biología Funcional, Área de MicrobiologíaUniversidad de OviedoOviedoSpain
- Instituto Universitario de Oncología del Principado de AsturiasOviedoSpain
- Instituto de Investigación Sanitaria del Principado de AsturiasOviedoSpain
| | - Felipe Lombó
- Research Unit “Biotechnology in Nutraceuticals and Bioactive Compounds‐BIONUC”Departamento de Biología Funcional, Área de MicrobiologíaUniversidad de OviedoOviedoSpain
- Instituto Universitario de Oncología del Principado de AsturiasOviedoSpain
- Instituto de Investigación Sanitaria del Principado de AsturiasOviedoSpain
| | - Juan Nogales
- Department of Systems BiologyCentro Nacional de BiotecnologíaCSICMadridSpain
- Interdisciplinary Platform for Sustainable Plastics towards a Circular Economy‐Spanish National Research Council (SusPlast‐CSIC)MadridSpain
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22
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Lardone RD, Garay YC, Parodi P, de la Fuente S, Angeloni G, Bravo EO, Schmider AK, Irazoqui FJ. How glycobiology can help us treat and beat the COVID-19 pandemic. J Biol Chem 2021; 296:100375. [PMID: 33548227 PMCID: PMC7857991 DOI: 10.1016/j.jbc.2021.100375] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 02/01/2021] [Accepted: 02/02/2021] [Indexed: 12/12/2022] Open
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged during the last months of 2019, spreading throughout the world as a highly transmissible infectious illness designated as COVID-19. Vaccines have now appeared, but the challenges in producing sufficient material and distributing them around the world means that effective treatments to limit infection and improve recovery are still urgently needed. This review focuses on the relevance of different glycobiological molecules that could potentially serve as or inspire therapeutic tools during SARS-CoV-2 infection. As such, we highlight the glycobiology of the SARS-CoV-2 infection process, where glycans on viral proteins and on host glycosaminoglycans have critical roles in efficient infection. We also take notice of the glycan-binding proteins involved in the infective capacity of virus and in human defense. In addition, we critically evaluate the glycobiological contribution of candidate drugs for COVID-19 therapy such as glycans for vaccines, anti-glycan antibodies, recombinant lectins, lectin inhibitors, glycosidase inhibitors, polysaccharides, and numerous glycosides, emphasizing some opportunities to repurpose FDA-approved drugs. For the next-generation drugs suggested here, biotechnological engineering of new probes to block the SARS-CoV-2 infection might be based on the essential glycobiological insight on glycosyltransferases, glycans, glycan-binding proteins, and glycosidases related to this pathology.
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Affiliation(s)
- Ricardo D Lardone
- Centro de Investigaciones en Química Biológica de Córdoba, CIQUIBIC, CONICET and Departamento de Química Biológica Ranwel Caputto, Facultad de Ciencias Químicas, Universidad Nacional de Córdoba, Ciudad Universitaria, Córdoba, Argentina
| | - Yohana C Garay
- Centro de Investigaciones en Química Biológica de Córdoba, CIQUIBIC, CONICET and Departamento de Química Biológica Ranwel Caputto, Facultad de Ciencias Químicas, Universidad Nacional de Córdoba, Ciudad Universitaria, Córdoba, Argentina
| | - Pedro Parodi
- Centro de Investigaciones en Química Biológica de Córdoba, CIQUIBIC, CONICET and Departamento de Química Biológica Ranwel Caputto, Facultad de Ciencias Químicas, Universidad Nacional de Córdoba, Ciudad Universitaria, Córdoba, Argentina
| | - Sofia de la Fuente
- Centro de Investigaciones en Química Biológica de Córdoba, CIQUIBIC, CONICET and Departamento de Química Biológica Ranwel Caputto, Facultad de Ciencias Químicas, Universidad Nacional de Córdoba, Ciudad Universitaria, Córdoba, Argentina
| | - Genaro Angeloni
- Centro de Investigaciones en Química Biológica de Córdoba, CIQUIBIC, CONICET and Departamento de Química Biológica Ranwel Caputto, Facultad de Ciencias Químicas, Universidad Nacional de Córdoba, Ciudad Universitaria, Córdoba, Argentina
| | - Eduardo O Bravo
- Medicina Interna, Nuevo Hospital San Roque, Ministerio de Salud de la Provincia de Córdoba, Córdoba, Argentina
| | - Anneke K Schmider
- Klinik für Kinder- und Jugendpsychiatrie und Psychotherapie, Psychiatrische Klinik Lüneburg, Lüneburg, Germany
| | - Fernando J Irazoqui
- Centro de Investigaciones en Química Biológica de Córdoba, CIQUIBIC, CONICET and Departamento de Química Biológica Ranwel Caputto, Facultad de Ciencias Químicas, Universidad Nacional de Córdoba, Ciudad Universitaria, Córdoba, Argentina.
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23
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Seoane R, Vidal S, Bouzaher YH, El Motiam A, Rivas C. The Interaction of Viruses with the Cellular Senescence Response. BIOLOGY 2020; 9:E455. [PMID: 33317104 PMCID: PMC7764305 DOI: 10.3390/biology9120455] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Revised: 11/30/2020] [Accepted: 12/07/2020] [Indexed: 01/10/2023]
Abstract
Cellular senescence is viewed as a mechanism to prevent malignant transformation, but when it is chronic, as occurs in age-related diseases, it may have adverse effects on cancer. Therefore, targeting senescent cells is a novel therapeutic strategy against senescence-associated diseases. In addition to its role in cancer protection, cellular senescence is also considered a mechanism to control virus replication. Both interferon treatment and some viral infections can trigger cellular senescence as a way to restrict virus replication. However, activation of the cellular senescence program is linked to the alteration of different pathways, which can be exploited by some viruses to improve their replication. It is, therefore, important to understand the potential impact of senolytic agents on viral propagation. Here we focus on the relationship between virus and cellular senescence and the reported effects of senolytic compounds on virus replication.
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Affiliation(s)
- Rocío Seoane
- Centro de Investigación en Medicina Molecular y Enfermedades Crónicas (CIMUS), Universidad de Santiago de Compostela, 15706 Santiago de Compostela, Spain; (R.S.); (S.V.); (Y.H.B.); (A.E.M.)
| | - Santiago Vidal
- Centro de Investigación en Medicina Molecular y Enfermedades Crónicas (CIMUS), Universidad de Santiago de Compostela, 15706 Santiago de Compostela, Spain; (R.S.); (S.V.); (Y.H.B.); (A.E.M.)
| | - Yanis Hichem Bouzaher
- Centro de Investigación en Medicina Molecular y Enfermedades Crónicas (CIMUS), Universidad de Santiago de Compostela, 15706 Santiago de Compostela, Spain; (R.S.); (S.V.); (Y.H.B.); (A.E.M.)
| | - Ahmed El Motiam
- Centro de Investigación en Medicina Molecular y Enfermedades Crónicas (CIMUS), Universidad de Santiago de Compostela, 15706 Santiago de Compostela, Spain; (R.S.); (S.V.); (Y.H.B.); (A.E.M.)
| | - Carmen Rivas
- Centro de Investigación en Medicina Molecular y Enfermedades Crónicas (CIMUS), Universidad de Santiago de Compostela, 15706 Santiago de Compostela, Spain; (R.S.); (S.V.); (Y.H.B.); (A.E.M.)
- Centro Nacional de Biotecnología (CNB), CSIC, 28049 Madrid, Spain
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24
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Mousavi SZ, Rahmanian M, Sami A. A connectivity map-based drug repurposing study and integrative analysis of transcriptomic profiling of SARS-CoV-2 infection. INFECTION, GENETICS AND EVOLUTION : JOURNAL OF MOLECULAR EPIDEMIOLOGY AND EVOLUTIONARY GENETICS IN INFECTIOUS DISEASES 2020; 86:104610. [PMID: 33130005 PMCID: PMC7598903 DOI: 10.1016/j.meegid.2020.104610] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/13/2020] [Revised: 09/29/2020] [Accepted: 10/23/2020] [Indexed: 12/15/2022]
Abstract
AIMS The recent outbreak of COVID-19 has become a global health concern. There are currently no effective treatment strategies and vaccines for the treatment or prevention of this fatal disease. The current study aims to determine promising treatment options for the COVID-19 through a computational drug repurposing approach. MATERIALS AND METHODS In this study, we focus on differentially expressed genes (DEGs), detected in SARS-CoV-2 infected cell lines including "the primary human lung epithelial cell line NHBE" and "the transformed lung alveolar cell line A549". Next, the identified DEGs are used in the connectivity map (CMap) analysis to identify similarly acting therapeutic candidates. Furthermore, to interpret lists of DEGs, pathway enrichment and protein network analysis are performed. Genes are categorized into easily interpretable pathways based on their biological functions, and overrepresentation of each pathway is tested in comparison to what is expected randomly. KEY FINDINGS The results suggest the effectiveness of lansoprazole, folic acid, sulfamonomethoxine, tolnaftate, diclofenamide, halcinonide, saquinavir, metronidazole, ebselen, lidocaine and benzocaine, histone deacetylase (HDAC) inhibitors, heat shock protein 90 (HSP90) inhibitors, and many other clinically approved drugs as potent drugs against COVID-19 outbreak. SIGNIFICANCE Making new drugs remain a lengthy process, so the drug repurposing approach provides an insight into the therapeutics that might be helpful in this pandemic. In this study, pathway enrichment and protein network analysis are also performed, and the effectiveness of some drugs obtained from the CMap analysis has been investigated according to previous researches.
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25
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De Vita S, Chini MG, Lauro G, Bifulco G. Accelerating the repurposing of FDA-approved drugs against coronavirus disease-19 (COVID-19). RSC Adv 2020; 10:40867-40875. [PMID: 35519188 PMCID: PMC9057693 DOI: 10.1039/d0ra09010g] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 11/02/2020] [Indexed: 12/24/2022] Open
Abstract
The recent release of the main protein structures belonging to SARS CoV-2, responsible for the coronavirus disease-19 (COVID-19), strongly pushed for identifying valuable drug treatments. With this aim, we show a repurposing study on FDA-approved drugs applying a new computational protocol and introducing a novel parameter called IVSratio. Starting with a virtual screening against three SARS CoV-2 targets (main protease, papain-like protease, spike protein), the top-ranked molecules were reassessed combining the Inverse Virtual Screening novel approach and MM-GBSA calculations. Applying this protocol, a list of drugs was identified against the three investigated targets. Also, the top-ranked selected compounds on each target (rutin vs. main protease, velpatasvir vs. papain-like protease, lomitapide vs. spike protein) were further tested with molecular dynamics simulations to confirm the promising binding modes, obtaining encouraging results such as high stability of the complex during the simulation and a good protein–ligand interaction network involving some important residues of each target. Moreover, the recent outcomes highlighting the inhibitory activity of quercetin, a natural compound strictly related to rutin, on the SARS-CoV-2 main protease, strengthened the applicability of the proposed workflow. New computational protocol applied to a repurposing campaign against SARS-CoV-2.![]()
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Affiliation(s)
- Simona De Vita
- Department of Pharmacy
- University of Salerno
- Fisciano 84084
- Italy
| | - Maria Giovanna Chini
- Department of Biosciences and Territory
- University of Molise
- 86090 Pesche (IS)
- Italy
| | - Gianluigi Lauro
- Department of Pharmacy
- University of Salerno
- Fisciano 84084
- Italy
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