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Zhang S, Wang P, Shi L, Wang C, Zhu Z, Qi C, Xie Y, Yuan S, Cheng L, Yin X, Zhang X. Exploring COVID-19 causal genes through disease-specific Cis-eQTLs. Virus Res 2024; 342:199341. [PMID: 38403000 PMCID: PMC10904281 DOI: 10.1016/j.virusres.2024.199341] [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: 01/29/2024] [Revised: 02/20/2024] [Accepted: 02/22/2024] [Indexed: 02/27/2024]
Abstract
Genome-wide association study (GWAS) analysis has exposed that genetic factors play important roles in COVID-19. Whereas a deeper understanding of the underlying mechanism of COVID-19 was hindered by the lack of expression of quantitative trait loci (eQTL) data specific for disease. To this end, we identified COVID-19-specific cis-eQTLs by integrating nucleotide sequence variations and RNA-Seq data from COVID-19 samples. These identified eQTLs have different regulatory effect on genes between patients and controls, indicating that SARS-CoV-2 infection may cause alterations in the human body's internal environment. Individuals with the TT genotype in the rs1128320 region seemed more susceptible to SARS-CoV-2 infection and developed into severe COVID-19 due to the abnormal expression of IFITM1. We subsequently discovered potential causal genes, of the result, a total of 48 genes from six tissues were identified. siRNA-mediated depletion assays in SARS-CoV-2 infection proved that 14 causal genes were directly associated with SARS-CoV-2 infection. These results enriched existing research on COVID-19 causal genes and provided a new sight in the mechanism exploration for COVID-19.
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Affiliation(s)
- Sainan Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Ping Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Lei Shi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Chao Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Zijun Zhu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Changlu Qi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Yubin Xie
- Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam 999077, Hong Kong Special Administrative Region of China; State Key Laboratory of Emerging Infectious Diseases, Li Ka Shing Faculty of Medicine, the University of Hong Kong, Pokfulam 999077, Hong Kong Special Administrative Region of China
| | - Shuofeng Yuan
- Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam 999077, Hong Kong Special Administrative Region of China; State Key Laboratory of Emerging Infectious Diseases, Li Ka Shing Faculty of Medicine, the University of Hong Kong, Pokfulam 999077, Hong Kong Special Administrative Region of China
| | - Liang Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China; NHC Key Laboratory of Molecular Probes and Targeted Diagnosis and Therapy, Harbin Medical University, Harbin, Heilongjiang 150028, China.
| | - Xin Yin
- State Key Laboratory of Veterinary Biotechnology, Harbin Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Harbin 150040, China
| | - Xue Zhang
- NHC Key Laboratory of Molecular Probes and Targeted Diagnosis and Therapy, Harbin Medical University, Harbin, Heilongjiang 150028, China; McKusick-Zhang Center for Genetic Medicine, State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100005, China
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2
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Chen Y, Zhang M, Li W, Wang X, Chen X, Wu Y, Zhang H, Yang L, Han B, Tang J. Drug repurposing based on the similarity gene expression signatures to explore for potential indications of quercetin: a case study of multiple sclerosis. Front Chem 2023; 11:1250043. [PMID: 37744058 PMCID: PMC10514366 DOI: 10.3389/fchem.2023.1250043] [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: 06/29/2023] [Accepted: 08/14/2023] [Indexed: 09/26/2023] Open
Abstract
Quercetin (QR) is a natural flavonol compound widely distributed in the plant kingdom with extensive pharmacological effects. To find the potential clinical indications of QR, 156 differentially expressed genes (DEGs) regulated by QR were obtained from the Gene Expression Omnibus database, and new potential pharmacological effects and clinical indications of QR were repurposed by integrating compounds with similar gene perturbation signatures and associated-disease signatures to QR based on the Connectivity Map and Coexpedia platforms. The results suggested QR has mainly potential therapeutic effects on multiple sclerosis (MS), osteoarthritis, type 2 diabetes mellitus, and acute leukemia. Then, MS was selected for subsequent animal experiments as a representative potential indication, and it found that QR significantly delays the onset time of classical MS model animal mice and ameliorates the inflammatory infiltration and demyelination in the central nervous system. Combined with network pharmacology technology, the therapeutic mechanism of QR on MS was further demonstrated to be related to the inhibition of the expression of inflammatory cytokines (TNF-α, IL-6, IL-1β, IFN-γ, IL-17A, and IL-2) related to TNF-α/TNFR1 signaling pathway. In conclusion, this study expanded the clinical indications of QR and preliminarily confirmed the therapeutic effect and potential mechanism of QR on MS.
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Affiliation(s)
- Yulong Chen
- School of Pharmacy, Henan University of Chinese Medicine, Zhengzhou, China
| | - Mingliang Zhang
- Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Henan Provincial Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Henan Province Engineering Research Center of Safety Evaluation and Risk Management of Traditional Chinese Medicine, Department of Pharmacy, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
| | - Weixia Li
- School of Pharmacy, Henan University of Chinese Medicine, Zhengzhou, China
- Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Henan Provincial Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Henan Province Engineering Research Center of Safety Evaluation and Risk Management of Traditional Chinese Medicine, Department of Pharmacy, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
| | - Xiaoyan Wang
- School of Pharmacy, Henan University of Chinese Medicine, Zhengzhou, China
- Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Henan Provincial Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Henan Province Engineering Research Center of Safety Evaluation and Risk Management of Traditional Chinese Medicine, Department of Pharmacy, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
| | - Xiaofei Chen
- Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Henan Provincial Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Henan Province Engineering Research Center of Safety Evaluation and Risk Management of Traditional Chinese Medicine, Department of Pharmacy, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
| | - Yali Wu
- Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Henan Provincial Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Henan Province Engineering Research Center of Safety Evaluation and Risk Management of Traditional Chinese Medicine, Department of Pharmacy, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
| | - Hui Zhang
- Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Henan Provincial Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Henan Province Engineering Research Center of Safety Evaluation and Risk Management of Traditional Chinese Medicine, Department of Pharmacy, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
| | - Liuqing Yang
- Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Henan Provincial Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Henan Province Engineering Research Center of Safety Evaluation and Risk Management of Traditional Chinese Medicine, Department of Pharmacy, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
| | - Bing Han
- School of Pharmacy, Henan University of Chinese Medicine, Zhengzhou, China
| | - Jinfa Tang
- School of Pharmacy, Henan University of Chinese Medicine, Zhengzhou, China
- Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Henan Provincial Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Henan Province Engineering Research Center of Safety Evaluation and Risk Management of Traditional Chinese Medicine, Department of Pharmacy, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
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3
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Nainu F, Mamada SS, Emran TB. Prospective role of NSAIDs with antiviral properties for pharmacological management of postsurgical procedures during COVID-19. Int J Surg 2023; 109:109-111. [PMID: 36799818 PMCID: PMC10389334 DOI: 10.1097/js9.0000000000000101] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 11/16/2022] [Indexed: 02/18/2023]
Affiliation(s)
- Firzan Nainu
- Department of Pharmacy, Faculty of Pharmacy, Hasanuddin University, Makassar, Indonesia
| | - Sukamto S. Mamada
- Department of Pharmacy, Faculty of Pharmacy, Hasanuddin University, Makassar, Indonesia
| | - Talha B. Emran
- Department of Pharmacy, BGC Trust University Bangladesh, Chittagong
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka, Bangladesh
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He J, Liu S, Tan Q, Liu Z, Fu J, Li T, Wei C, Liu X, Mei Z, Cheng J, Wang K, Fu J. Antiviral Potential of Small Molecules Cordycepin, Thymoquinone, and N6, N6-Dimethyladenosine Targeting SARS-CoV-2 Entry Protein ADAM17. Molecules 2022; 27:molecules27249044. [PMID: 36558177 PMCID: PMC9781528 DOI: 10.3390/molecules27249044] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 12/06/2022] [Accepted: 12/14/2022] [Indexed: 12/23/2022] Open
Abstract
COVID-19 is an acute respiratory disease caused by SARS-CoV-2 that has spawned a worldwide pandemic. ADAM17 is a sheddase associated with the modulation of the receptor ACE2 of SARS-CoV-2. Studies have revealed that malignant phenotypes of several cancer types are closely relevant to highly expressed ADAM17. However, ADAM17 regulation in SARS-CoV-2 invasion and its role on small molecules are unclear. Here, we evaluated the ADAM17 inhibitory effects of cordycepin (CD), thymoquinone (TQ), and N6, N6-dimethyladenosine (m62A), on cancer cells and predicted the anti-COVID-19 potential of the three compounds and their underlying signaling pathways by network pharmacology. It was found that CD, TQ, and m62A repressed the ADAM17 expression upon different cancer cells remarkably. Moreover, CD inhibited GFP-positive syncytia formation significantly, suggesting its potential against SARS-CoV-2. Pharmacological analysis by constructing CD-, TQ-, and m62A-based drug-target COVID-19 networks further indicated that ADAM17 is a potential target for anti-COVID-19 therapy with these compounds, and the mechanism might be relevant to viral infection and transmembrane receptors-mediated signal transduction. These findings imply that ADAM17 is of potentially medical significance for cancer patients infected with SARS-CoV-2, which provides potential new targets and insights for developing innovative drugs against COVID-19.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | - Kai Wang
- Correspondence: (J.C.); (K.W.); (J.F.)
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Network Pharmacology and Molecular Docking Analysis Reveal Insights into the Molecular Mechanism of Shengma-Gegen Decoction on Monkeypox. Pathogens 2022; 11:pathogens11111342. [DOI: 10.3390/pathogens11111342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 11/08/2022] [Accepted: 11/10/2022] [Indexed: 11/16/2022] Open
Abstract
Background: A new viral outbreak caused by monkeypox has appeared after COVID-19. As of yet, no specific drug has been found for its treatment. Shengma-Gegen decoction (SMGGD), a pathogen-eliminating and detoxifying agent composed of four kinds of Chinese herbs, has been demonstrated to be effective against several viruses in China, suggesting that it may be effective in treating monkeypox, however, the precise role and mechanisms are still unknown. Methods: Network pharmacology was used to investigate the monkeypox-specific SMGGD targets. These targets were analyzed via String for protein-to-protein interaction (PPI), followed by identification of hub genes with Cytoscape software. Function enrichment analysis of the hub targets was performed. The interactions between hub targets and corresponding ligands were validated via molecular docking. Results: Through screening and analysis, a total of 94 active components and 8 hub targets were identified in the TCM-bioactive compound-hub gene network. Molecular docking results showed that the active components of SMGGD have strong binding affinity for their corresponding targets. According to functional analysis, these hub genes are mainly involved in the TNF, AGE-RAGE, IL-17, and MAPK pathways, which are linked to the host inflammatory response to infection and viral replication. Therefore, SMGGD might suppress the replication of monkeypox virus through the MAPK signaling pathway while also reducing inflammatory damage caused by viral infection. Conclusion: SMGGD may have positive therapeutic effects on monkeypox by reducing inflammatory damage and limiting virus replication.
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Severity of COVID-19 patients with coexistence of asthma and vitamin D deficiency. INFORMATICS IN MEDICINE UNLOCKED 2022; 34:101116. [PMID: 36338941 PMCID: PMC9616486 DOI: 10.1016/j.imu.2022.101116] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Revised: 10/21/2022] [Accepted: 10/22/2022] [Indexed: 11/06/2022] Open
Abstract
Coronavirus disease 2019 (COVID-19)-driven global pandemic triggered innumerable health complications, imposing great challenges in managing other respiratory diseases like asthma. Furthermore, increases in the underlying inflammation involved in the fatality of COVID-19 have been linked with lack of vitamin D. In this research work, we intend to investigate the possible genetic linkage of asthma and vitamin D deficiency with the severity and fatality of COVID-19 using a network-based approach. We identified and analysed 41 and 14 differentially expressed genes (DEGs) of COVID-19 being common with asthma and vitamin D deficiency, respectively, through the comparative differential gene expression analysis and their footprints on signalling pathways. Gene set enrichment analysis for GO terms and signalling pathways reveals key biological activities, including inflammatory response-related pathways (e.g., cytokine- and chemokine-mediated signalling pathways, IL-17, and TNF signalling pathways). Besides, the Protein–Protein Interaction network analysis of those DEGs reveals hub proteins, some of which are reported as inflammatory antiviral interferon-stimulated biomarkers that potentially drive the cytokine storm leading to COVID-19 severity and fatality, and contributes in the early stage of viral replication, respectively. Moreover, the regulatory network analysis found these DEGs associated with antiviral and tumour inhibitory transcription factors and micro-RNAs. Finally, drug–target enrichment analysis yields tetradioxin, estradiol, arsenenous acid, and zinc, which have been reported to be effective in suppressing the pro-inflammatory cytokines production, and other respiratory tract infections. Our results yield shared biomarker-driven key hypotheses followed by network-based analytics, demystifying the mechanistic details of COVID-19 comorbidity of asthma and vitamin D deficiency with their potential therapeutic implications.
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Avnat E, Shapira G, Gurwitz D, Shomron N. Elevated Expression of RGS2 May Underlie Reduced Olfaction in COVID-19 Patients. J Pers Med 2022; 12:jpm12091396. [PMID: 36143181 PMCID: PMC9504192 DOI: 10.3390/jpm12091396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 08/15/2022] [Accepted: 08/25/2022] [Indexed: 12/03/2022] Open
Abstract
Anosmia is common in COVID-19 patients, lasting for weeks or months following recovery. The biological mechanism underlying olfactory deficiency in COVID-19 does not involve direct damage to nasal olfactory neurons, which do not express the proteins required for SARS-CoV-2 infection. A recent study suggested that anosmia results from downregulation of olfactory receptors. We hypothesized that anosmia in COVID-19 may also reflect SARS-CoV-2 infection-driven elevated expression of regulator of G protein signaling 2 (RGS2), a key regulator of odorant receptors, thereby silencing their signaling. To test our hypothesis, we analyzed gene expression of nasopharyngeal swabs from SARS-CoV-2 positive patients and non-infected controls (two published RNA-sequencing datasets, 580 individuals). Our analysis found upregulated RGS2 expression in SARS-CoV-2 positive patients (FC = 14.5, Padj = 1.69 × 10−5 and FC = 2.4; Padj = 0.001, per dataset). Additionally, RGS2 expression was strongly correlated with PTGS2, IL1B, CXCL8, NAMPT and other inflammation markers with substantial upregulation in early infection. These observations suggest that upregulated expression of RGS2 may underlie anosmia in COVID-19 patients. As a regulator of numerous G-protein coupled receptors, RGS2 may drive further neurological symptoms of COVID-19. Studies are required for clarifying the cellular mechanisms by which SARS-CoV-2 infection drives the upregulation of RGS2 and other genes implicated in inflammation. Insights on these pathway(s) may assist in understanding anosmia and additional neurological symptoms reported in COVID-19 patients.
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Affiliation(s)
- Eden Avnat
- Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
| | - Guy Shapira
- Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
- Edmond J Safra Center for Bioinformatics, Tel Aviv University, Tel Aviv 69978, Israel
| | - David Gurwitz
- Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 69978, Israel
- Correspondence: (D.G.); (N.S.); Tel.: +972-3-640-7611 (D.G.); +972-3-640-6594 (N.S.)
| | - Noam Shomron
- Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
- Edmond J Safra Center for Bioinformatics, Tel Aviv University, Tel Aviv 69978, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 69978, Israel
- Correspondence: (D.G.); (N.S.); Tel.: +972-3-640-7611 (D.G.); +972-3-640-6594 (N.S.)
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Karimabad MN, Hassanshahi G, Kounis NG, Mplani V, Roditis P, Gogos C, Lagadinou M, Assimakopoulos SF, Dousdampanis P, Koniari I. The Chemokines CXC, CC and C in the Pathogenesis of COVID-19 Disease and as Surrogates of Vaccine-Induced Innate and Adaptive Protective Responses. Vaccines (Basel) 2022; 10:vaccines10081299. [PMID: 36016187 PMCID: PMC9416781 DOI: 10.3390/vaccines10081299] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 08/08/2022] [Accepted: 08/08/2022] [Indexed: 02/07/2023] Open
Abstract
COVID-19 is one of the progressive viral pandemics that originated from East Asia. COVID-19 or SARS-CoV-2 has been shown to be associated with a chain of physio-pathological mechanisms that are basically immunological in nature. In addition, chemokines have been proposed as a subgroup of chemotactic cytokines with different activities ranging from leukocyte recruitment to injury sites, irritation, and inflammation to angiostasis and angiogenesis. Therefore, researchers have categorized the chemotactic elements into four classes, including CX3C, CXC, CC, and C, based on the location of the cysteine motifs in their structures. Considering the severe cases of COVID-19, the hyperproduction of particular chemokines occurring in lung tissue as well as pro-inflammatory cytokines significantly worsen the disease prognosis. According to the studies conducted in the field documenting the changing expression of CXC and CC chemokines in COVID-19 cases, the CC and CXC chemokines contribute to this pandemic, and their impact could reflect the development of reasonable strategies for COVID-19 management. The CC and the CXC families of chemokines are important in host immunity to viral infections and along with other biomarkers can serve as the surrogates of vaccine-induced innate and adaptive protective responses, facilitating the improvement of vaccine efficacy. Furthermore, the immunogenicity elicited by the chemokine response to adenovirus vector vaccines may constitute the basis of vaccine-induced immune thrombotic thrombocytopaenia.
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Affiliation(s)
- Mojgan Noroozi Karimabad
- Molecular Medicine Research Center, Research Institute of Basic Medical Sciences, Rafsanjan University of Medical Sciences, Rafsanjan 7717933777, Iran
| | - Gholamhossein Hassanshahi
- Molecular Medicine Research Center, Research Institute of Basic Medical Sciences, Rafsanjan University of Medical Sciences, Rafsanjan 7717933777, Iran
| | - Nicholas G. Kounis
- Department of Internal Medicine, Division of Cardiology, University of Patras Medical School, 26500 Patras, Greece
- Correspondence:
| | - Virginia Mplani
- Intensive Care Unit, Patras University Hospital, 26500 Patras, Greece
| | - Pavlos Roditis
- Department of Cardiology, Mamatsio Kozanis General Hospital, 50100 Kozani, Greece
| | - Christos Gogos
- COVID-19 Unit, Papageorgiou General Hospital, 56403 Thessaloniki, Greece
| | - Maria Lagadinou
- Department of Internal Medicine, Division of Infectious Diseases, University of Patras Medical School, 26500 Patras, Greece
| | - Stelios F. Assimakopoulos
- Department of Internal Medicine, Division of Infectious Diseases, University of Patras Medical School, 26500 Patras, Greece
| | - Periklis Dousdampanis
- Department of Nephrology, Saint Andrews State General Hospital, 26221 Patras, Greece
| | - Ioanna Koniari
- Department of Cardiology, University Hospital of South Manchester, NHS Foundation Trust, Manchester M23 9LT, UK
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Niranjan V, Setlur AS, Karunakaran C, Uttarkar A, Kumar KM, Skariyachan S. Scope of repurposed drugs against the potential targets of the latest variants of SARS-CoV-2. Struct Chem 2022; 33:1585-1608. [PMID: 35938064 PMCID: PMC9346052 DOI: 10.1007/s11224-022-02020-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: 04/18/2022] [Accepted: 07/19/2022] [Indexed: 11/21/2022]
Abstract
The unprecedented outbreak of the severe acute respiratory syndrome (SARS) Coronavirus-2, across the globe, triggered a worldwide uproar in the search for immediate treatment strategies. With no specific drug and not much data available, alternative approaches such as drug repurposing came to the limelight. To date, extensive research on the repositioning of drugs has led to the identification of numerous drugs against various important protein targets of the coronavirus strains, with hopes of the drugs working against the major variants of concerns (alpha, beta, gamma, delta, omicron) of the virus. Advancements in computational sciences have led to improved scope of repurposing via techniques such as structure-based approaches including molecular docking, molecular dynamic simulations and quantitative structure activity relationships, network-based approaches, and artificial intelligence-based approaches with other core machine and deep learning algorithms. This review highlights the various approaches to repurposing drugs from a computational biological perspective, with various mechanisms of action of the drugs against some of the major protein targets of SARS-CoV-2. Additionally, clinical trials data on potential COVID-19 repurposed drugs are also highlighted with stress on the major SARS-CoV-2 targets and the structural effect of variants on these targets. The interaction modelling of some important repurposed drugs has also been elucidated. Furthermore, the merits and demerits of drug repurposing are also discussed, with a focus on the scope and applications of the latest advancements in repurposing.
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Affiliation(s)
- Vidya Niranjan
- Department of Biotechnology, RV College of Engineering, Bengaluru, Karnataka India
| | | | | | - Akshay Uttarkar
- Department of Biotechnology, RV College of Engineering, Bengaluru, Karnataka India
| | - Kalavathi Murugan Kumar
- Department of Bioinformatics, Pondicherry University, Chinna Kalapet, Kalapet, Puducherry, Tamil Nadu India
| | - Sinosh Skariyachan
- Department of Microbiology, St. Pius X College, Rajapuram, Kasaragod, Kerala India
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Sagulkoo P, Suratanee A, Plaimas K. Immune-Related Protein Interaction Network in Severe COVID-19 Patients toward the Identification of Key Proteins and Drug Repurposing. Biomolecules 2022; 12:biom12050690. [PMID: 35625619 PMCID: PMC9138873 DOI: 10.3390/biom12050690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 05/07/2022] [Accepted: 05/09/2022] [Indexed: 02/05/2023] Open
Abstract
Coronavirus disease 2019 (COVID-19) is still an active global public health issue. Although vaccines and therapeutic options are available, some patients experience severe conditions and need critical care support. Hence, identifying key genes or proteins involved in immune-related severe COVID-19 is necessary to find or develop the targeted therapies. This study proposed a novel construction of an immune-related protein interaction network (IPIN) in severe cases with the use of a network diffusion technique on a human interactome network and transcriptomic data. Enrichment analysis revealed that the IPIN was mainly associated with antiviral, innate immune, apoptosis, cell division, and cell cycle regulation signaling pathways. Twenty-three proteins were identified as key proteins to find associated drugs. Finally, poly (I:C), mitomycin C, decitabine, gemcitabine, hydroxyurea, tamoxifen, and curcumin were the potential drugs interacting with the key proteins to heal severe COVID-19. In conclusion, IPIN can be a good representative network for the immune system that integrates the protein interaction network and transcriptomic data. Thus, the key proteins and target drugs in IPIN help to find a new treatment with the use of existing drugs to treat the disease apart from vaccination and conventional antiviral therapy.
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Affiliation(s)
- Pakorn Sagulkoo
- Program in Bioinformatics and Computational Biology, Graduate School, Chulalongkorn University, Bangkok 10330, Thailand;
- Center of Biomedical Informatics, Department of Family Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai 50200, Thailand
| | - Apichat Suratanee
- Department of Mathematics, Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok, Bangkok 10800, Thailand;
- Intelligent and Nonlinear Dynamics Innovations Research Center, Science and Technology Research Institute, King Mongkut’s University of Technology North Bangkok, Bangkok 10800, Thailand
| | - Kitiporn Plaimas
- Advance Virtual and Intelligent Computing (AVIC) Center, Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand
- Omics Science and Bioinformatics Center, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand
- Correspondence:
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Bajo-Morales J, Prieto-Prieto JC, Herrera LJ, Rojas I, Castillo-Secilla D. COVID-19 Biomarkers Recognition & Classification Using Intelligent Systems. Curr Bioinform 2022. [DOI: 10.2174/1574893617666220328125029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Background:
SARS-CoV-2 has paralyzed mankind due to its high transmissibility and its associated mortality, causing millions of infections and deaths worldwide. The search for gene expression biomarkers from the host transcriptional response to infection may help understand the underlying mechanisms by which the virus causes COVID-19. This research proposes a smart methodology integrating different RNA-Seq datasets from SARS-CoV-2, other respiratory diseases, and healthy patients.
Methods:
The proposed pipeline exploits the functionality of the ‘KnowSeq’ R/Bioc package, integrating different data sources and attaining a significantly larger gene expression dataset, thus endowing the results with higher statistical significance and robustness in comparison with previous studies in the literature. A detailed preprocessing step was carried out to homogenize the samples and build a clinical decision system for SARS-CoV-2. It uses machine learning techniques such as feature selection algorithm and supervised classification system. This clinical decision system uses the most differentially expressed genes among different diseases (including SARS-Cov-2) to develop a four-class classifier.
Results:
The multiclass classifier designed can discern SARS-CoV-2 samples, reaching an accuracy equal to 91.5%, a mean F1-Score equal to 88.5%, and a SARS-CoV-2 AUC equal to 94% by using only 15 genes as predictors. A biological interpretation of the gene signature extracted reveals relations with processes involved in viral responses.
Conclusion:
This work proposes a COVID-19 gene signature composed of 15 genes, selected after applying the feature selection ‘minimum Redundancy Maximum Relevance’ algorithm. The integration among several RNA-Seq datasets was a success, allowing for a considerable large number of samples and therefore providing greater statistical significance to the results than previous studies. Biological interpretation of the selected genes was also provided.
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Affiliation(s)
- Javier Bajo-Morales
- Department of Computer Architecture and Technology, University of Granada. C.I.T.I.C., Periodista Rafael Gómez Montero, 2, 18014, Granada, Spain
| | - Juan Carlos Prieto-Prieto
- Nuclear Medicine Department, IMIBIC, University Hospital Reina Sofia, Menéndez Pidal Avenue, 14004, Córdoba, Spain
| | - Luis Javier Herrera
- Department of Computer Architecture and Technology, University of Granada. C.I.T.I.C., Periodista Rafael Gómez Montero, 2, 18014, Granada, Spain
| | - Ignacio Rojas
- Department of Computer Architecture and Technology, University of Granada. C.I.T.I.C., Periodista Rafael Gómez Montero, 2, 18014, Granada, Spain
| | - Daniel Castillo-Secilla
- Department of Computer Architecture and Technology, University of Granada. C.I.T.I.C., Periodista Rafael Gómez Montero, 2, 18014, Granada, Spain
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12
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Ahmed SM, Nasr MA, Elshenawy SE, Hussein AE, El-Betar AH, Mohamed RH, El-Badri N. BCG vaccination and the risk of COVID 19: A possible correlation. Virology 2022; 565:73-81. [PMID: 34742127 PMCID: PMC8552046 DOI: 10.1016/j.virol.2021.10.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 10/01/2021] [Accepted: 10/17/2021] [Indexed: 01/04/2023]
Abstract
Bacillus Calmette-Guérin (BCG) vaccine is currently used to prevent tuberculosis infection. The vaccine was found to enhance resistance to certain types of infection including positive sense RNA viruses. The current COVID-19 pandemic is caused by positive sense RNA, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). A higher mortality rate of COVID-19 patients was reported in countries where BCG vaccination is not routinely administered, when compared to the vaccinated ones. We hypothesized that BCG vaccine may control SARS-CoV2 infection via modulating the monocyte immune response. We analyzed GSE104149 dataset to investigate whether human monocytes of BCG-vaccinated individuals acquire resistance to SARS-CoV-2 infection. Differentially expressed genes obtained from the dataset were used to determine enriched pathways, biological processes, and molecular functions for monocytes post BCG vaccination. Our data show that BCG vaccine promotes a more effective immune response of monocytes against SARS-CoV2, but probably not sufficient to prevent the infection.
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Affiliation(s)
- Sara M Ahmed
- Center of Excellence for Stem Cells and Regenerative Medicine (CESC), Zewail City of Science and Technology, October Gardens, 12582, 6th of October City, Giza, Egypt
| | - Mohamed A Nasr
- Center of Excellence for Stem Cells and Regenerative Medicine (CESC), Zewail City of Science and Technology, October Gardens, 12582, 6th of October City, Giza, Egypt
| | - Shimaa E Elshenawy
- Center of Excellence for Stem Cells and Regenerative Medicine (CESC), Zewail City of Science and Technology, October Gardens, 12582, 6th of October City, Giza, Egypt
| | - Alaa E Hussein
- Center of Excellence for Stem Cells and Regenerative Medicine (CESC), Zewail City of Science and Technology, October Gardens, 12582, 6th of October City, Giza, Egypt
| | - Ahmed H El-Betar
- Department of Urology, Ahmed Maher Teaching Hospital, Cairo, Egypt
| | | | - Nagwa El-Badri
- Center of Excellence for Stem Cells and Regenerative Medicine (CESC), Zewail City of Science and Technology, October Gardens, 12582, 6th of October City, Giza, Egypt.
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13
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Identifying potential novel insights for COVID-19 pathogenesis and therapeutics using an integrated bioinformatics analysis of host transcriptome. Int J Biol Macromol 2022; 194:770-780. [PMID: 34826456 PMCID: PMC8610562 DOI: 10.1016/j.ijbiomac.2021.11.124] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 10/09/2021] [Accepted: 11/17/2021] [Indexed: 01/24/2023]
Abstract
The molecular mechanisms underlying the pathogenesis of COVID-19 have not been fully discovered. This study aims to decipher potentially hidden parts of the pathogenesis of COVID-19, potential novel drug targets, and identify potential drug candidates. Two gene expression profiles were analyzed, and overlapping differentially expressed genes (DEGs) were selected for which top enriched transcription factors and kinases were identified, and pathway analysis was performed. Protein-protein interaction (PPI) of DEGs was constructed, hub genes were identified, and module analysis was also performed. DGIdb database was used to identify drugs for the potential targets (hub genes and the most enriched transcription factors and kinases for DEGs). A drug-potential target network was constructed, and drugs were ranked according to the degree. L1000FDW was used to identify drugs that can reverse transcriptional profiles of COVID-19. We identified drugs currently in clinical trials, others predicted by different methods, and novel potential drug candidates Entrectinib, Omeprazole, and Exemestane for combating COVID-19. Besides the well-known pathogenic pathways, it was found that axon guidance is a potential pathogenic pathway. Sema7A, which may exacerbate hypercytokinemia, is considered a potential novel drug target. Another potential novel pathway is related to TINF2 overexpression, which may induce potential telomere dysfunction and damage DNA that may exacerbate lung fibrosis. This study identified new potential insights regarding COVID-19 pathogenesis and treatment, which might help us improve our understanding of the mechanisms of COVID-19.
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14
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Bernasconi A, Cascianelli S. Scenarios for the Integration of Microarray Gene Expression Profiles in COVID-19-Related Studies. Methods Mol Biol 2022; 2401:195-215. [PMID: 34902130 DOI: 10.1007/978-1-0716-1839-4_13] [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] [Indexed: 06/14/2023]
Abstract
The COVID-19 pandemic has hit heavily many aspects of our lives. At this time, genomic research is concerned with exploiting available datasets and knowledge to fuel discovery on this novel disease. Studies that can precisely characterize the gene expression profiles of human hosts infected by SARS-CoV-2 are of significant relevance. However, not many such experiments have yet been produced to date, nor made publicly available online. Thus, it is of paramount importance that data analysts explore all possibilities to integrate information coming from similar viruses and related diseases; interestingly, microarray gene profile experiments become extremely valuable for this purpose. This chapter reviews the aspects that should be considered when integrating transcriptomics data, considering mainly samples infected by different viruses and combining together various data types and also the extracted knowledge. It describes a series of scenarios from studies performed in literature and it suggests possible other directions of noteworthy integration.
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Affiliation(s)
- Anna Bernasconi
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy.
| | - Silvia Cascianelli
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy
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15
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Imami AS, McCullumsmith RE, O’Donovan SM. Strategies to identify candidate repurposable drugs: COVID-19 treatment as a case example. Transl Psychiatry 2021; 11:591. [PMID: 34785660 PMCID: PMC8594646 DOI: 10.1038/s41398-021-01724-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 10/26/2021] [Accepted: 11/02/2021] [Indexed: 02/07/2023] Open
Abstract
Drug repurposing is an invaluable strategy to identify new uses for existing drug therapies that overcome many of the time and financial costs associated with novel drug development. The COVID-19 pandemic has driven an unprecedented surge in the development and use of bioinformatic tools to identify candidate repurposable drugs. Using COVID-19 as a case study, we discuss examples of machine-learning and signature-based approaches that have been adapted to rapidly identify candidate drugs. The Library of Integrated Network-based Signatures (LINCS) and Connectivity Map (CMap) are commonly used repositories and have the advantage of being amenable to use by scientists with limited bioinformatic training. Next, we discuss how these recent advances in bioinformatic drug repurposing approaches might be adapted to identify repurposable drugs for CNS disorders. As the development of novel therapies that successfully target the cause of neuropsychiatric and neurological disorders has stalled, there is a pressing need for innovative strategies to treat these complex brain disorders. Bioinformatic approaches to identify repurposable drugs provide an exciting avenue of research that offer promise for improved treatments for CNS disorders.
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Affiliation(s)
- Ali S. Imami
- grid.267337.40000 0001 2184 944XDepartment of Neurosciences, University of Toledo, Toledo, OH USA
| | - Robert E. McCullumsmith
- grid.267337.40000 0001 2184 944XDepartment of Neurosciences, University of Toledo, Toledo, OH USA ,grid.422550.40000 0001 2353 4951Neurosciences Institute, Promedica, Toledo, OH USA
| | - Sinead M. O’Donovan
- grid.267337.40000 0001 2184 944XDepartment of Neurosciences, University of Toledo, Toledo, OH USA
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16
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Tomazou M, Bourdakou MM, Minadakis G, Zachariou M, Oulas A, Karatzas E, Loizidou EM, Kakouri AC, Christodoulou CC, Savva K, Zanti M, Onisiforou A, Afxenti S, Richter J, Christodoulou CG, Kyprianou T, Kolios G, Dietis N, Spyrou GM. Multi-omics data integration and network-based analysis drives a multiplex drug repurposing approach to a shortlist of candidate drugs against COVID-19. Brief Bioinform 2021; 22:bbab114. [PMID: 34009288 PMCID: PMC8135326 DOI: 10.1093/bib/bbab114] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 02/01/2021] [Accepted: 03/13/2021] [Indexed: 02/06/2023] Open
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic is undeniably the most severe global health emergency since the 1918 Influenza outbreak. Depending on its evolutionary trajectory, the virus is expected to establish itself as an endemic infectious respiratory disease exhibiting seasonal flare-ups. Therefore, despite the unprecedented rally to reach a vaccine that can offer widespread immunization, it is equally important to reach effective prevention and treatment regimens for coronavirus disease 2019 (COVID-19). Contributing to this effort, we have curated and analyzed multi-source and multi-omics publicly available data from patients, cell lines and databases in order to fuel a multiplex computational drug repurposing approach. We devised a network-based integration of multi-omic data to prioritize the most important genes related to COVID-19 and subsequently re-rank the identified candidate drugs. Our approach resulted in a highly informed integrated drug shortlist by combining structural diversity filtering along with experts' curation and drug-target mapping on the depicted molecular pathways. In addition to the recently proposed drugs that are already generating promising results such as dexamethasone and remdesivir, our list includes inhibitors of Src tyrosine kinase (bosutinib, dasatinib, cytarabine and saracatinib), which appear to be involved in multiple COVID-19 pathophysiological mechanisms. In addition, we highlight specific immunomodulators and anti-inflammatory drugs like dactolisib and methotrexate and inhibitors of histone deacetylase like hydroquinone and vorinostat with potential beneficial effects in their mechanisms of action. Overall, this multiplex drug repurposing approach, developed and utilized herein specifically for SARS-CoV-2, can offer a rapid mapping and drug prioritization against any pathogen-related disease.
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Affiliation(s)
- Marios Tomazou
- Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, Cyprus
- The Cyprus School of Molecular Medicine, Cyprus
- Neurogenetics Department, The Cyprus Institute of Neurology and Genetics, Cyprus
| | - Marilena M Bourdakou
- Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, Cyprus
- Laboratory of Pharmacology, Faculty of Medicine, Democritus University of Thrace, Greece
| | - George Minadakis
- Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, Cyprus
- The Cyprus School of Molecular Medicine, Cyprus
| | - Margarita Zachariou
- Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, Cyprus
- The Cyprus School of Molecular Medicine, Cyprus
| | - Anastasis Oulas
- Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, Cyprus
- The Cyprus School of Molecular Medicine, Cyprus
| | - Evangelos Karatzas
- Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, Cyprus
- Institute for Fundamental Biomedical Research, BSRC “Alexander Fleming”, Vari, Greece
| | - Eleni M Loizidou
- Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, Cyprus
- Institute for Fundamental Biomedical Research, BSRC “Alexander Fleming”, Vari, Greece
| | - Andrea C Kakouri
- Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, Cyprus
- The Cyprus School of Molecular Medicine, Cyprus
- Neurogenetics Department, The Cyprus Institute of Neurology and Genetics, Cyprus
| | - Christiana C Christodoulou
- Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, Cyprus
- The Cyprus School of Molecular Medicine, Cyprus
- Neuroepidemiology Department, The Cyprus Institute of Neurology and Genetics, Cyprus
| | - Kyriaki Savva
- Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, Cyprus
- The Cyprus School of Molecular Medicine, Cyprus
| | - Maria Zanti
- Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, Cyprus
- The Cyprus School of Molecular Medicine, Cyprus
- Cancer Genetics, Therapeutics … Ultrastructural Pathology, The Cyprus Institute of Neurology and Genetics, Cyprus
| | - Anna Onisiforou
- Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, Cyprus
- The Cyprus School of Molecular Medicine, Cyprus
| | - Sotiroula Afxenti
- Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, Cyprus
- The Cyprus School of Molecular Medicine, Cyprus
- Neuroimmunology Department, The Cyprus Institute of Neurology and Genetics, Cyprus
| | - Jan Richter
- Department of Molecular Virology, The Cyprus Institute of Neurology and Genetics, Cyprus
- The Cyprus School of Molecular Medicine, Cyprus
| | - Christina G Christodoulou
- Department of Molecular Virology, The Cyprus Institute of Neurology and Genetics, Cyprus
- The Cyprus School of Molecular Medicine, Cyprus
| | - Theodoros Kyprianou
- Medical School, University of Nicosia, Cyprus
- University Hospitals Bristol and Weston NHS Foundation Trust, United Kingdom
| | - George Kolios
- Laboratory of Pharmacology, Faculty of Medicine, Democritus University of Thrace, Greece
| | - Nikolas Dietis
- Experimental Pharmacology Laboratory, Medical School, University of Cyprus, Cyprus
| | - George M Spyrou
- Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, Cyprus
- The Cyprus School of Molecular Medicine, Cyprus
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17
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Tayara H, Abdelbaky I, To Chong K. Recent omics-based computational methods for COVID-19 drug discovery and repurposing. Brief Bioinform 2021; 22:6355836. [PMID: 34423353 DOI: 10.1093/bib/bbab339] [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: 05/22/2021] [Revised: 07/09/2021] [Indexed: 12/22/2022] Open
Abstract
The coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is the main reason for the increasing number of deaths worldwide. Although strict quarantine measures were followed in many countries, the disease situation is still intractable. Thus, it is needed to utilize all possible means to confront this pandemic. Therefore, researchers are in a race against the time to produce potential treatments to cure or reduce the increasing infections of COVID-19. Computational methods are widely proving rapid successes in biological related problems, including diagnosis and treatment of diseases. Many efforts in recent months utilized Artificial Intelligence (AI) techniques in the context of fighting the spread of COVID-19. Providing periodic reviews and discussions of recent efforts saves the time of researchers and helps to link their endeavors for a faster and efficient confrontation of the pandemic. In this review, we discuss the recent promising studies that used Omics-based data and utilized AI algorithms and other computational tools to achieve this goal. We review the established datasets and the developed methods that were basically directed to new or repurposed drugs, vaccinations and diagnosis. The tools and methods varied depending on the level of details in the available information such as structures, sequences or metabolic data.
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Affiliation(s)
- Hilal Tayara
- School of international Engineering and Science, Jeonbuk National University, Jeonju 54896, Republic of Korea
| | - Ibrahim Abdelbaky
- Artificial Intelligence Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha 13518, Egypt
| | - Kil To Chong
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju, Jeollabukdo 54896, Republic of Korea.,Advances Electronics and Information Research Center, Jeonbuk National University, Jeonju 54896, Republic of Korea
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18
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Karimi A, Shobeiri P, Kulasinghe A, Rezaei N. Novel Systemic Inflammation Markers to Predict COVID-19 Prognosis. Front Immunol 2021; 12:741061. [PMID: 34745112 PMCID: PMC8569430 DOI: 10.3389/fimmu.2021.741061] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Accepted: 09/28/2021] [Indexed: 12/15/2022] Open
Abstract
Coronavirus disease 2019 (COVID-19) has resulted in a global pandemic, challenging both the medical and scientific community for the development of novel vaccines and a greater understanding of the effects of the SARS-CoV-2 virus. COVID-19 has been associated with a pronounced and out-of-control inflammatory response. Studies have sought to understand the effects of inflammatory response markers to prognosticate the disease. Herein, we aimed to review the evidence of 11 groups of systemic inflammatory markers for risk-stratifying patients and prognosticating outcomes related to COVID-19. Numerous studies have demonstrated the effectiveness of neutrophil to lymphocyte ratio (NLR) in prognosticating patient outcomes, including but not limited to severe disease, hospitalization, intensive care unit (ICU) admission, intubation, and death. A few markers outperformed NLR in predicting outcomes, including 1) systemic immune-inflammation index (SII), 2) prognostic nutritional index (PNI), 3) C-reactive protein (CRP) to albumin ratio (CAR) and high-sensitivity CAR (hsCAR), and 4) CRP to prealbumin ratio (CPAR) and high-sensitivity CPAR (hsCPAR). However, there are a limited number of studies comparing NLR with these markers, and such conclusions require larger validation studies. Overall, the evidence suggests that most of the studied markers are able to predict COVID-19 prognosis, however NLR seems to be the most robust marker.
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Affiliation(s)
- Amirali Karimi
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
- Network of Immunity in Infection, Malignancy and Autoimmunity (NIIMA), Universal Scientific Education and Research Network (USERN), Tehran, Iran
| | - Parnian Shobeiri
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
- Network of Immunity in Infection, Malignancy and Autoimmunity (NIIMA), Universal Scientific Education and Research Network (USERN), Tehran, Iran
- Research Center for Immunodeficiencies, Pediatrics Center of Excellence, Children’s Medical Center, Tehran University of Medical Sciences, Tehran, Iran
- Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Arutha Kulasinghe
- Centre for Genomics and Personalised Health, School of Biomedical Q6 Sciences, Queensland University of Technology, Brisbane, QL, Australia
| | - Nima Rezaei
- Network of Immunity in Infection, Malignancy and Autoimmunity (NIIMA), Universal Scientific Education and Research Network (USERN), Tehran, Iran
- Research Center for Immunodeficiencies, Pediatrics Center of Excellence, Children’s Medical Center, Tehran University of Medical Sciences, Tehran, Iran
- Department of Immunology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
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19
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Aghdam R, Habibi M, Taheri G. Using informative features in machine learning based method for COVID-19 drug repurposing. J Cheminform 2021; 13:70. [PMID: 34544500 PMCID: PMC8451172 DOI: 10.1186/s13321-021-00553-9] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Accepted: 09/06/2021] [Indexed: 01/14/2023] Open
Abstract
Coronavirus disease 2019 (COVID-19) is caused by a novel virus named Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). This virus induced a large number of deaths and millions of confirmed cases worldwide, creating a serious danger to public health. However, there are no specific therapies or drugs available for COVID-19 treatment. While new drug discovery is a long process, repurposing available drugs for COVID-19 can help recognize treatments with known clinical profiles. Computational drug repurposing methods can reduce the cost, time, and risk of drug toxicity. In this work, we build a graph as a COVID-19 related biological network. This network is related to virus targets or their associated biological processes. We select essential proteins in the constructed biological network that lead to a major disruption in the network. Our method from these essential proteins chooses 93 proteins related to COVID-19 pathology. Then, we propose multiple informative features based on drug-target and protein-protein interaction information. Through these informative features, we find five appropriate clusters of drugs that contain some candidates as potential COVID-19 treatments. To evaluate our results, we provide statistical and clinical evidence for our candidate drugs. From our proposed candidate drugs, 80% of them were studied in other studies and clinical trials.
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Affiliation(s)
- Rosa Aghdam
- School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.
| | - Mahnaz Habibi
- Department of Mathematics, Qazvin Branch, Islamic Azad University, Qazvin, Iran
| | - Golnaz Taheri
- Department of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden
- Science for Life Laboratory, Stockholm, Sweden
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20
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Mignani S, Shi X, Karpus A, Lentini G, Majoral JP. Functionalized Dendrimer Platforms as a New Forefront Arsenal Targeting SARS-CoV-2: An Opportunity. Pharmaceutics 2021; 13:1513. [PMID: 34575589 PMCID: PMC8466088 DOI: 10.3390/pharmaceutics13091513] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 09/10/2021] [Accepted: 09/15/2021] [Indexed: 12/23/2022] Open
Abstract
The novel human coronavirus SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) has caused a pandemic. There are currently several marketed vaccines and many in clinical trials targeting SARS-CoV-2. Another strategy is to repurpose approved drugs to decrease the burden of the COVID-19 (official name for the coronavirus disease) pandemic. as the FDA (U.S. Food and Drug Administration) approved antiviral drugs and anti-inflammatory drugs to arrest the cytokine storm, inducing the production of pro-inflammatory cytokines. Another view to solve these unprecedented challenges is to analyze the diverse nanotechnological approaches which are able to improve the COVID-19 pandemic. In this original minireview, as promising candidates we analyze the opportunity to develop biocompatible dendrimers as drugs themselves or as nanocarriers against COVID-19 disease. From the standpoint of COVID-19, we suggest developing dendrimers as shields against COVID-19 infection based on their capacity to be incorporated in several environments outside the patients and as important means to stop transmission of SARS-CoV-2.
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Affiliation(s)
- Serge Mignani
- Laboratoire de Chimie et de Biochimie Pharmacologiques et Toxicologique, Université Paris Descartes, PRES Sorbonne Paris Cité, CNRS UMR 860, 75006 Paris, France
- CQM—Centro de Química da Madeira, MMRG, Campus da Penteada, Universidade da Madeira, 9020-105 Funchal, Portugal
| | - Xiangyang Shi
- CQM—Centro de Química da Madeira, MMRG, Campus da Penteada, Universidade da Madeira, 9020-105 Funchal, Portugal
- College of Chemistry, Chemical Engineering and Biotechnology, Donghua University, Shanghai 201620, China
| | - Andrii Karpus
- Laboratoire de Chimie de Coordination du CNRS, 205 Route de Narbonne, CEDEX 4, 31077 Toulouse, France;
- Université Toulouse 118 Route de Narbonne, CEDEX 4, 31077 Toulouse, France
| | - Giovanni Lentini
- Dipartimento di Farmacia—Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy;
| | - Jean-Pierre Majoral
- Laboratoire de Chimie de Coordination du CNRS, 205 Route de Narbonne, CEDEX 4, 31077 Toulouse, France;
- Université Toulouse 118 Route de Narbonne, CEDEX 4, 31077 Toulouse, France
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21
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Pan X, Li X, Ning S, Zhi H. Inferring SARS-CoV-2 functional genomics from viral transcriptome with identification of potential antiviral drugs and therapeutic targets. Cell Biosci 2021; 11:171. [PMID: 34496960 PMCID: PMC8424170 DOI: 10.1186/s13578-021-00684-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 08/24/2021] [Indexed: 01/30/2023] Open
Abstract
Coronavirus disease 2019 (COVID-19) is an emerging infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and has posed a serious threat to global health. Here, we systematically characterized the transcription levels of the SARS-CoV-2 genes and identified the responsive human genes associated with virus infection. We inferred the possible biological functions of each viral gene and depicted the functional landscape based on guilt-by-association and functional enrichment analyses. Subsequently, the transcription factor regulatory network, protein-protein interaction network, and non-coding RNA regulatory network were constructed to discover more potential antiviral targets. In addition, several potential drugs for COVID-19 treatment and prevention were recognized, including known cell proliferation-related, immune-related, and antiviral drugs, in which proteasome inhibitors (bortezomib, carfilzomib, and ixazomib citrate) may play an important role in the treatment of COVID-19. These results provided novel insights into the understanding of SARS-CoV-2 functional genomics and host-targeting antiviral strategies for SARS-CoV-2 infection.
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Affiliation(s)
- Xu Pan
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Xin Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.,Department of Dermatology, Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Shangwei Ning
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
| | - Hui Zhi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
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22
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Samart K, Tuyishime P, Krishnan A, Ravi J. Reconciling multiple connectivity scores for drug repurposing. Brief Bioinform 2021; 22:6278144. [PMID: 34013329 PMCID: PMC8597919 DOI: 10.1093/bib/bbab161] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 04/02/2021] [Accepted: 04/07/2021] [Indexed: 12/16/2022] Open
Abstract
The basis of several recent methods for drug repurposing is the key principle that an
efficacious drug will reverse the disease molecular ‘signature’ with minimal side effects.
This principle was defined and popularized by the influential ‘connectivity map’ study in
2006 regarding reversal relationships between disease- and drug-induced gene expression
profiles, quantified by a disease-drug ‘connectivity score.’ Over the past 15 years,
several studies have proposed variations in calculating connectivity scores toward
improving accuracy and robustness in light of massive growth in reference drug profiles.
However, these variations have been formulated inconsistently using various notations and
terminologies even though they are based on a common set of conceptual and statistical
ideas. Therefore, we present a systematic reconciliation of multiple disease-drug
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}{}$EWCos$\end{document}) and connectivity scores
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}{}$EMUDRA$\end{document}) by defining them using consistent
notation and terminology. In addition to providing clarity and deeper insights, this
coherent definition of connectivity scores and their relationships provides a unified
scheme that newer methods can adopt, enabling the computational drug-development community
to compare and investigate different approaches easily. To facilitate the continuous and
transparent integration of newer methods, this article will be available as a live
document (https://jravilab.github.io/connectivity_scores) coupled with a GitHub
repository (https://github.com/jravilab/connectivity_scores) that any researcher can
build on and push changes to.
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Affiliation(s)
- Kewalin Samart
- Computational Mathematics, and Computational Math, Science & Engineering at Michigan State University, East Lansing, MI, USA
| | - Phoebe Tuyishime
- College of Agriculture and Natural Resources at Michigan State University, East Lansing, MI, USA
| | - Arjun Krishnan
- Departments of Computational Math, Science & Engineering, and Biochemistry & Molecular Biology at Michigan State University, East Lansing, MI, USA
| | - Janani Ravi
- Pathobiology and Diagnostic Investigation at Michigan State University, East Lansing, MI, USA
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23
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Nashiry A, Sarmin Sumi S, Islam S, Quinn JMW, Moni MA. Bioinformatics and system biology approach to identify the influences of COVID-19 on cardiovascular and hypertensive comorbidities. Brief Bioinform 2021; 22:1387-1401. [PMID: 33458761 PMCID: PMC7929376 DOI: 10.1093/bib/bbaa426] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 12/06/2020] [Indexed: 01/08/2023] Open
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infected individuals that have hypertension or cardiovascular comorbidities have an elevated risk of serious coronavirus disease 2019 (COVID-19) disease and high rates of mortality but how COVID-\documentclass[12pt]{minimal}
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}{}$19$\end{document} and cardiovascular diseases interact are unclear. We therefore sought to identify novel mechanisms of interaction by identifying genes with altered expression in SARS-CoV-\documentclass[12pt]{minimal}
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}{}$2$\end{document} infection that are relevant to the pathogenesis of cardiovascular disease and hypertension. Some recent research shows the SARS-CoV-\documentclass[12pt]{minimal}
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}{}$2$\end{document}) as a receptor to infect human susceptible cells. The ACE2 gene is expressed in many human tissues, including intestine, testis, kidneys, heart and lungs. ACE2 usually converts Angiotensin I in the renin–angiotensin-aldosterone system to Angiotensin II, which affects blood pressure levels. ACE inhibitors prescribed for cardiovascular disease and hypertension may increase the levels of ACE-\documentclass[12pt]{minimal}
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}{}$2$\end{document}, although there are claims that such medications actually reduce lung injury caused by COVID-\documentclass[12pt]{minimal}
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}{}$19$\end{document}. We employed bioinformatics and systematic approaches to identify such genetic links, using messenger RNA data peripheral blood cells from COVID-\documentclass[12pt]{minimal}
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}{}$19$\end{document} patients and compared them with blood samples from patients with either chronic heart failure disease or hypertensive diseases. We have also considered the immune response genes with elevated expression in COVID-\documentclass[12pt]{minimal}
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}{}$19$\end{document} to those active in cardiovascular diseases and hypertension. Differentially expressed genes (DEGs) common to COVID-\documentclass[12pt]{minimal}
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}{}$19$\end{document} and hypertension, were identified; the involvement of these common genes in the signalling pathways and ontologies studied. COVID-\documentclass[12pt]{minimal}
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}{}$19$\end{document} does not share a large number of differentially expressed genes with the conditions under consideration. However, those that were identified included genes playing roles in T cell functions, toll-like receptor pathways, cytokines, chemokines, cell stress, type 2 diabetes and gastric cancer. We also identified protein–protein interactions, gene regulatory networks and suggested drug and chemical compound interactions using the differentially expressed genes. The result of this study may help in identifying significant targets of treatment that can combat the ongoing pandemic due to SARS-CoV-\documentclass[12pt]{minimal}
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}{}$2$\end{document} infection.
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Affiliation(s)
- Asif Nashiry
- Department of Computer Science and Engineering, Jashore University of Science and Technology, Bangladesh
| | - Shauli Sarmin Sumi
- Department of Computer Science and Engineering, Jashore University of Science and Technology, Bangladesh
| | - Salequl Islam
- Department of Microbiology, Jahangirnagar University, Savar, Dhaka-1342, Bangladesh
| | - Julian M W Quinn
- Healthy Ageing Theme, The Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia
| | - Mohammad Ali Moni
- Healthy Ageing Theme, The Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia.,WHO Collaborating Centre on eHealth, UNSW Digital Health, School of Public Health and Community Medicine, Faculty of Medicine, UNSW Sydney, Australia
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24
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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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25
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Kumar Das J, Tradigo G, Veltri P, H Guzzi P, Roy S. Data science in unveiling COVID-19 pathogenesis and diagnosis: evolutionary origin to drug repurposing. Brief Bioinform 2021; 22:855-872. [PMID: 33592108 PMCID: PMC7929414 DOI: 10.1093/bib/bbaa420] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 11/09/2020] [Accepted: 12/19/2020] [Indexed: 12/20/2022] Open
Abstract
MOTIVATION The outbreak of novel severe acute respiratory syndrome coronavirus (SARS-CoV-2, also known as COVID-19) in Wuhan has attracted worldwide attention. SARS-CoV-2 causes severe inflammation, which can be fatal. Consequently, there has been a massive and rapid growth in research aimed at throwing light on the mechanisms of infection and the progression of the disease. With regard to this data science is playing a pivotal role in in silico analysis to gain insights into SARS-CoV-2 and the outbreak of COVID-19 in order to forecast, diagnose and come up with a drug to tackle the virus. The availability of large multiomics, radiological, bio-molecular and medical datasets requires the development of novel exploratory and predictive models, or the customisation of existing ones in order to fit the current problem. The high number of approaches generates the need for surveys to guide data scientists and medical practitioners in selecting the right tools to manage their clinical data. RESULTS Focusing on data science methodologies, we conduct a detailed study on the state-of-the-art of works tackling the current pandemic scenario. We consider various current COVID-19 data analytic domains such as phylogenetic analysis, SARS-CoV-2 genome identification, protein structure prediction, host-viral protein interactomics, clinical imaging, epidemiological research and drug discovery. We highlight data types and instances, their generation pipelines and the data science models currently in use. The current study should give a detailed sketch of the road map towards handling COVID-19 like situations by leveraging data science experts in choosing the right tools. We also summarise our review focusing on prime challenges and possible future research directions. CONTACT hguzzi@unicz.it, sroy01@cus.ac.in.
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Affiliation(s)
- Jayanta Kumar Das
- Department of Pediatrics, School of Medicine, Johns Hopkins University, Maryland, USA
| | - Giuseppe Tradigo
- eCampus University, Via Isimbardi 10, 22060 Novedrate, CO, Italy
| | - Pierangelo Veltri
- Department of Surgical and Medical Sciences, Magna Graecia University, Catanzaro, 88100, Italy
| | - Pietro H Guzzi
- Department of Surgical and Medical Sciences, Magna Graecia University, Catanzaro, 88100, Italy
| | - Swarup Roy
- Network Reconstruction & Analysis (NetRA) Lab, Department of Computer Applications, Sikkim University, Gangtok, India
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26
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Li Z, Yao Y, Cheng X, Chen Q, Zhao W, Ma S, Li Z, Zhou H, Li W, Fei T. A computational framework of host-based drug repositioning for broad-spectrum antivirals against RNA viruses. iScience 2021; 24:102148. [PMID: 33665567 PMCID: PMC7900436 DOI: 10.1016/j.isci.2021.102148] [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: 09/15/2020] [Revised: 01/11/2021] [Accepted: 02/01/2021] [Indexed: 12/18/2022] Open
Abstract
RNA viruses are responsible for many zoonotic diseases that post great challenges for public health. Effective therapeutics against these viral infections remain limited. Here, we deployed a computational framework for host-based drug repositioning to predict potential antiviral drugs from 2,352 approved drugs and 1,062 natural compounds embedded in herbs of traditional Chinese medicine. By systematically interrogating public genetic screening data, we comprehensively cataloged host dependency genes (HDGs) that are indispensable for successful viral infection corresponding to 10 families and 29 species of RNA viruses. We then utilized these HDGs as potential drug targets and interrogated extensive drug-target interactions through database retrieval, literature mining, and de novo prediction using artificial intelligence-based algorithms. Repurposed drugs or natural compounds were proposed against many viral pathogens such as coronaviruses including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), flaviviruses, and influenza viruses. This study helps to prioritize promising drug candidates for in-depth evaluation against these virus-related diseases.
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Affiliation(s)
- Zexu Li
- College of Life and Health Sciences, Northeastern University, Shenyang 110819, People's Republic of China
- Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University), Ministry of Education, Shenyang 110819, People's Republic of China
| | - Yingjia Yao
- College of Life and Health Sciences, Northeastern University, Shenyang 110819, People's Republic of China
- Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University), Ministry of Education, Shenyang 110819, People's Republic of China
| | - Xiaolong Cheng
- Center for Genetic Medicine Research, Children's National Hospital, 111 Michigan Avenue NW, Washington, DC 20010, USA
- Department of Genomics and Precision Medicine, George Washington University, 111 Michigan Avenue NW, Washington, DC 20010, USA
| | - Qing Chen
- Center for Genetic Medicine Research, Children's National Hospital, 111 Michigan Avenue NW, Washington, DC 20010, USA
- Department of Genomics and Precision Medicine, George Washington University, 111 Michigan Avenue NW, Washington, DC 20010, USA
| | - Wenchang Zhao
- College of Life and Health Sciences, Northeastern University, Shenyang 110819, People's Republic of China
- Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University), Ministry of Education, Shenyang 110819, People's Republic of China
| | - Shixin Ma
- College of Life and Health Sciences, Northeastern University, Shenyang 110819, People's Republic of China
- Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University), Ministry of Education, Shenyang 110819, People's Republic of China
| | - Zihan Li
- College of Life and Health Sciences, Northeastern University, Shenyang 110819, People's Republic of China
- Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University), Ministry of Education, Shenyang 110819, People's Republic of China
| | - Hu Zhou
- School of Pharmaceutical Sciences, Fujian Provincial Key Laboratory of Innovative Drug Target Research, Xiamen University, Xiamen, Fujian 361102, China
- High Throughput Drug Screening Platform, Xiamen University, Xiamen, Fujian 361102, China
| | - Wei Li
- Center for Genetic Medicine Research, Children's National Hospital, 111 Michigan Avenue NW, Washington, DC 20010, USA
- Department of Genomics and Precision Medicine, George Washington University, 111 Michigan Avenue NW, Washington, DC 20010, USA
| | - Teng Fei
- College of Life and Health Sciences, Northeastern University, Shenyang 110819, People's Republic of China
- Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University), Ministry of Education, Shenyang 110819, People's Republic of China
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27
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Adhami M, Sadeghi B, Rezapour A, Haghdoost AA, MotieGhader H. Repurposing novel therapeutic candidate drugs for coronavirus disease-19 based on protein-protein interaction network analysis. BMC Biotechnol 2021; 21:22. [PMID: 33711981 PMCID: PMC7952507 DOI: 10.1186/s12896-021-00680-z] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Accepted: 02/24/2021] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND The coronavirus disease-19 (COVID-19) emerged in Wuhan, China and rapidly spread worldwide. Researchers are trying to find a way to treat this disease as soon as possible. The present study aimed to identify the genes involved in COVID-19 and find a new drug target therapy. Currently, there are no effective drugs targeting SARS-CoV-2, and meanwhile, drug discovery approaches are time-consuming and costly. To address this challenge, this study utilized a network-based drug repurposing strategy to rapidly identify potential drugs targeting SARS-CoV-2. To this end, seven potential drugs were proposed for COVID-19 treatment using protein-protein interaction (PPI) network analysis. First, 524 proteins in humans that have interaction with the SARS-CoV-2 virus were collected, and then the PPI network was reconstructed for these collected proteins. Next, the target miRNAs of the mentioned module genes were separately obtained from the miRWalk 2.0 database because of the important role of miRNAs in biological processes and were reported as an important clue for future analysis. Finally, the list of the drugs targeting module genes was obtained from the DGIDb database, and the drug-gene network was separately reconstructed for the obtained protein modules. RESULTS Based on the network analysis of the PPI network, seven clusters of proteins were specified as the complexes of proteins which are more associated with the SARS-CoV-2 virus. Moreover, seven therapeutic candidate drugs were identified to control gene regulation in COVID-19. PACLITAXEL, as the most potent therapeutic candidate drug and previously mentioned as a therapy for COVID-19, had four gene targets in two different modules. The other six candidate drugs, namely, BORTEZOMIB, CARBOPLATIN, CRIZOTINIB, CYTARABINE, DAUNORUBICIN, and VORINOSTAT, some of which were previously discovered to be efficient against COVID-19, had three gene targets in different modules. Eventually, CARBOPLATIN, CRIZOTINIB, and CYTARABINE drugs were found as novel potential drugs to be investigated as a therapy for COVID-19. CONCLUSIONS Our computational strategy for predicting repurposable candidate drugs against COVID-19 provides efficacious and rapid results for therapeutic purposes. However, further experimental analysis and testing such as clinical applicability, toxicity, and experimental validations are required to reach a more accurate and improved treatment. Our proposed complexes of proteins and associated miRNAs, along with discovered candidate drugs might be a starting point for further analysis by other researchers in this urgency of the COVID-19 pandemic.
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Affiliation(s)
- Masoumeh Adhami
- Pathology and Stem Cell Research Center, Kerman University of Medical Sciences, Kerman, Iran
| | - Balal Sadeghi
- Food Hygiene and Public Health Department, Faculty of Veterinary Medicine, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Ali Rezapour
- Department of Agriculture, Tabriz Branch, Islamic Azad University, Tabriz, Iran
| | - Ali Akbar Haghdoost
- Modeling in Health Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
| | - Habib MotieGhader
- Department of Basic sciences, Biotechnology Research Center, Tabriz Branch, Islamic Azad University, Tabriz, Iran.
- Department of Computer Engineering, Gowgan Educational Center, Tabriz Branch, Islamic Azad University, Tabriz, Iran.
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28
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O'Donovan SM, Imami A, Eby H, Henkel ND, Creeden JF, Asah S, Zhang X, Wu X, Alnafisah R, Taylor RT, Reigle J, Thorman A, Shamsaei B, Meller J, McCullumsmith RE. Identification of candidate repurposable drugs to combat COVID-19 using a signature-based approach. Sci Rep 2021; 11:4495. [PMID: 33627767 PMCID: PMC7904823 DOI: 10.1038/s41598-021-84044-9] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 01/21/2021] [Indexed: 02/08/2023] Open
Abstract
The COVID-19 pandemic caused by the novel SARS-CoV-2 is more contagious than other coronaviruses and has higher rates of mortality than influenza. Identification of effective therapeutics is a crucial tool to treat those infected with SARS-CoV-2 and limit the spread of this novel disease globally. We deployed a bioinformatics workflow to identify candidate drugs for the treatment of COVID-19. Using an "omics" repository, the Library of Integrated Network-Based Cellular Signatures (LINCS), we simultaneously probed transcriptomic signatures of putative COVID-19 drugs and publicly available SARS-CoV-2 infected cell lines to identify novel therapeutics. We identified a shortlist of 20 candidate drugs: 8 are already under trial for the treatment of COVID-19, the remaining 12 have antiviral properties and 6 have antiviral efficacy against coronaviruses specifically, in vitro. All candidate drugs are either FDA approved or are under investigation. Our candidate drug findings are discordant with (i.e., reverse) SARS-CoV-2 transcriptome signatures generated in vitro, and a subset are also identified in transcriptome signatures generated from COVID-19 patient samples, like the MEK inhibitor selumetinib. Overall, our findings provide additional support for drugs that are already being explored as therapeutic agents for the treatment of COVID-19 and identify promising novel targets that are worthy of further investigation.
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Affiliation(s)
- Sinead M O'Donovan
- Department of Neurosciences, University of Toledo College of Medicine and Life Sciences, Health Science Campus, Mail Stop #1007, 3000 Arlington Avenue, Toledo, OH, 43614-2598, USA
| | - Ali Imami
- Department of Neurosciences, University of Toledo College of Medicine and Life Sciences, Health Science Campus, Mail Stop #1007, 3000 Arlington Avenue, Toledo, OH, 43614-2598, USA
| | - Hunter Eby
- Department of Neurosciences, University of Toledo College of Medicine and Life Sciences, Health Science Campus, Mail Stop #1007, 3000 Arlington Avenue, Toledo, OH, 43614-2598, USA
| | - Nicholas D Henkel
- Department of Neurosciences, University of Toledo College of Medicine and Life Sciences, Health Science Campus, Mail Stop #1007, 3000 Arlington Avenue, Toledo, OH, 43614-2598, USA
| | - Justin Fortune Creeden
- Department of Neurosciences, University of Toledo College of Medicine and Life Sciences, Health Science Campus, Mail Stop #1007, 3000 Arlington Avenue, Toledo, OH, 43614-2598, USA
| | - Sophie Asah
- Department of Neurosciences, University of Toledo College of Medicine and Life Sciences, Health Science Campus, Mail Stop #1007, 3000 Arlington Avenue, Toledo, OH, 43614-2598, USA
| | - Xiaolu Zhang
- Department of Neurosciences, University of Toledo College of Medicine and Life Sciences, Health Science Campus, Mail Stop #1007, 3000 Arlington Avenue, Toledo, OH, 43614-2598, USA
| | - Xiaojun Wu
- Department of Neurosciences, University of Toledo College of Medicine and Life Sciences, Health Science Campus, Mail Stop #1007, 3000 Arlington Avenue, Toledo, OH, 43614-2598, USA
| | - Rawan Alnafisah
- Department of Neurosciences, University of Toledo College of Medicine and Life Sciences, Health Science Campus, Mail Stop #1007, 3000 Arlington Avenue, Toledo, OH, 43614-2598, USA
| | - R Travis Taylor
- Department of Medical Microbiology and Immunology, University of Toledo, Toledo, OH, USA
| | - James Reigle
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- Department of Biomedical Informatics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Alexander Thorman
- Department of Environmental Health, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Behrouz Shamsaei
- Department of Biomedical Informatics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Jarek Meller
- Department of Biomedical Informatics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Department of Cancer Biology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Department of Environmental Health, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Department of Electrical Engineering and Computing Systems, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Department of Informatics, Nicolaus Copernicus University, Torun, Poland
| | - Robert E McCullumsmith
- Department of Neurosciences, University of Toledo College of Medicine and Life Sciences, Health Science Campus, Mail Stop #1007, 3000 Arlington Avenue, Toledo, OH, 43614-2598, USA.
- Neurosciences Institute, Promedica, Toledo, OH, USA.
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29
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Gelemanović A, Vidović T, Stepanić V, Trajković K. Identification of 37 Heterogeneous Drug Candidates for Treatment of COVID-19 via a Rational Transcriptomics-Based Drug Repurposing Approach. Pharmaceuticals (Basel) 2021; 14:87. [PMID: 33504008 PMCID: PMC7912585 DOI: 10.3390/ph14020087] [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: 01/04/2021] [Revised: 01/19/2021] [Accepted: 01/21/2021] [Indexed: 02/07/2023] Open
Abstract
A year after the initial outbreak, the COVID-19 pandemic caused by SARS-CoV-2 virus remains a serious threat to global health, while current treatment options are insufficient to bring major improvements. The aim of this study is to identify repurposable drug candidates with a potential to reverse transcriptomic alterations in the host cells infected by SARS-CoV-2. We have developed a rational computational pipeline to filter publicly available transcriptomic datasets of SARS-CoV-2-infected biosamples based on their responsiveness to the virus, to generate a list of relevant differentially expressed genes, and to identify drug candidates for repurposing using LINCS connectivity map. Pathway enrichment analysis was performed to place the results into biological context. We identified 37 structurally heterogeneous drug candidates and revealed several biological processes as druggable pathways. These pathways include metabolic and biosynthetic processes, cellular developmental processes, immune response and signaling pathways, with steroid metabolic process being targeted by half of the drug candidates. The pipeline developed in this study integrates biological knowledge with rational study design and can be adapted for future more comprehensive studies. Our findings support further investigations of some drugs currently in clinical trials, such as itraconazole and imatinib, and suggest 31 previously unexplored drugs as treatment options for COVID-19.
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Affiliation(s)
- Andrea Gelemanović
- Mediterranean Institute for Life Sciences (MedILS), Šetalište Ivana Meštrovića 45, 21000 Split, Croatia;
| | - Tinka Vidović
- Mediterranean Institute for Life Sciences (MedILS), Šetalište Ivana Meštrovića 45, 21000 Split, Croatia;
| | - Višnja Stepanić
- Ruđer Bošković Institute, Bijenička Cesta 54, 10000 Zagreb, Croatia;
| | - Katarina Trajković
- Mediterranean Institute for Life Sciences (MedILS), Šetalište Ivana Meštrovića 45, 21000 Split, Croatia;
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Galindez G, Matschinske J, Rose TD, Sadegh S, Salgado-Albarrán M, Späth J, Baumbach J, Pauling JK. Lessons from the COVID-19 pandemic for advancing computational drug repurposing strategies. NATURE COMPUTATIONAL SCIENCE 2021; 1:33-41. [PMID: 38217166 DOI: 10.1038/s43588-020-00007-6] [Citation(s) in RCA: 68] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 12/01/2020] [Indexed: 12/15/2022]
Abstract
Responding quickly to unknown pathogens is crucial to stop uncontrolled spread of diseases that lead to epidemics, such as the novel coronavirus, and to keep protective measures at a level that causes as little social and economic harm as possible. This can be achieved through computational approaches that significantly speed up drug discovery. A powerful approach is to restrict the search to existing drugs through drug repurposing, which can vastly accelerate the usually long approval process. In this Review, we examine a representative set of currently used computational approaches to identify repurposable drugs for COVID-19, as well as their underlying data resources. Furthermore, we compare drug candidates predicted by computational methods to drugs being assessed by clinical trials. Finally, we discuss lessons learned from the reviewed research efforts, including how to successfully connect computational approaches with experimental studies, and propose a unified drug repurposing strategy for better preparedness in the case of future outbreaks.
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Affiliation(s)
- Gihanna Galindez
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany
| | - Julian Matschinske
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany
| | - Tim Daniel Rose
- LipiTUM, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany
| | - Sepideh Sadegh
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany
| | - Marisol Salgado-Albarrán
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany
- Natural Sciences Department, Universidad Autónoma Metropolitana-Cuajimalpa (UAM-C), Mexico City, Mexico
| | - Julian Späth
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany
| | - Jan Baumbach
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany
- Computational Biomedicine Lab, Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
| | - Josch Konstantin Pauling
- LipiTUM, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany.
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31
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Wang X, Xie P, Sun G, Deng Z, Zhao M, Bao S, Zhou Y. A systematic review and meta-analysis of the efficacy and safety of western medicine routine treatment combined with Chinese herbal medicine in the treatment of COVID-19. Medicine (Baltimore) 2020; 99:e21616. [PMID: 32769922 PMCID: PMC7593006 DOI: 10.1097/md.0000000000021616] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 07/08/2020] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND COVID-19 is a global public health emergency. At present, there is no highly effective medicine for the prevention and treatment of 2019-nCoV. Western medicine for COVID-19 is mainly based on symptomatic support therapy. Chinese herbal medicine has been used to prevent infectious diseases for thousands of years in China. Western medicine routine treatment combined with Chinese herbal medicine is an alternative clinical option but lacks evidence-based medical evidence. The systematic review protocol aims to formulate a research plan that can evaluate the efficacy and safety of western medicine routine treatment combined with Chinese herbal medicine for COVID-19. METHODS We will search the following eight databases: Cochrane Library, PubMed, Embase, Medline, CNKI, Wanfang, VIP, and CBM. The search time is up to the end of July 2020. Two authors will independently complete literature screening, data extraction, and risk of bias assessment. In case of disagreement, the third author will assist in the judgment. The primary outcome will be the clinical cure rate. The secondary outcome will be accounting symptoms, fever time, time of virus nucleic acid turning negative, check the condition by drawing blood, pneumonia absorption rate, patient hospitalization time, severe conversion rate and case fatality rate, adverse reactions, and adverse events. Revman 5.3 will be used for systematic reviews and meta-analysis. The report of the protocol will follow the PRISMA-P statement, and the report of the systematic review and meta-analysis will follow the PRISMA statement. RESULTS We will provide evidence-based medical evidence of the efficacy and safety of western medicine routine treatment combined with Chinese herbal medicine for COVID-19. The findings will be published in peer-reviewed journals. REGISTRATION DETAILS CRD42020190106.
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Affiliation(s)
- Xuemei Wang
- Department of Gynecology, Hospital of Chengdu University of Traditional Chinese Medicine, College of Clinical Medicine, Chengdu University of Traditional Chinese Medicine
| | - Ping Xie
- Department of Gynecology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, P. R. China
| | - Guojuan Sun
- Department of Gynecology, Hospital of Chengdu University of Traditional Chinese Medicine, College of Clinical Medicine, Chengdu University of Traditional Chinese Medicine
| | - Zhumei Deng
- Department of Gynecology, Hospital of Chengdu University of Traditional Chinese Medicine, College of Clinical Medicine, Chengdu University of Traditional Chinese Medicine
| | - Min Zhao
- Department of Gynecology, Hospital of Chengdu University of Traditional Chinese Medicine, College of Clinical Medicine, Chengdu University of Traditional Chinese Medicine
| | - Shuting Bao
- Department of Gynecology, Hospital of Chengdu University of Traditional Chinese Medicine, College of Clinical Medicine, Chengdu University of Traditional Chinese Medicine
| | - Yunxia Zhou
- Department of Gynecology, Hospital of Chengdu University of Traditional Chinese Medicine, College of Clinical Medicine, Chengdu University of Traditional Chinese Medicine
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