1
|
Oprea TI, Bologa C, Holmes J, Mathias S, Metzger VT, Waller A, Yang JJ, Leach AR, Jensen LJ, Kelleher KJ, Sheils TK, Mathé E, Avram S, Edwards JS. Overview of the Knowledge Management Center for Illuminating the Druggable Genome. Drug Discov Today 2024; 29:103882. [PMID: 38218214 PMCID: PMC10939799 DOI: 10.1016/j.drudis.2024.103882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 12/22/2023] [Accepted: 01/09/2024] [Indexed: 01/15/2024]
Abstract
The Knowledge Management Center (KMC) for the Illuminating the Druggable Genome (IDG) project aims to aggregate, update, and articulate protein-centric data knowledge for the entire human proteome, with emphasis on the understudied proteins from the three IDG protein families. KMC collates and analyzes data from over 70 resources to compile the Target Central Resource Database (TCRD), which is the web-based informatics platform (Pharos). These data include experimental, computational, and text-mined information on protein structures, compound interactions, and disease and phenotype associations. Based on this knowledge, proteins are classified into different Target Development Levels (TDLs) for identification of understudied targets. Additional work by the KMC focuses on enriching target knowledge and producing DrugCentral and other data visualization tools for expanding investigation of understudied targets.
Collapse
Affiliation(s)
- Tudor I Oprea
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Cristian Bologa
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Jayme Holmes
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Stephen Mathias
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Vincent T Metzger
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Anna Waller
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Jeremy J Yang
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Andrew R Leach
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, UK
| | - Lars Juhl Jensen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Keith J Kelleher
- National Center for Advancing Translational Sciences (NCATS), NIH, Bethesda, MD, USA
| | - Timothy K Sheils
- National Center for Advancing Translational Sciences (NCATS), NIH, Bethesda, MD, USA
| | - Ewy Mathé
- National Center for Advancing Translational Sciences (NCATS), NIH, Bethesda, MD, USA
| | - Sorin Avram
- Coriolan Dragulescu Institute of Chemistry, Timisoara, Romania
| | - Jeremy S Edwards
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA; Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, NM, USA.
| |
Collapse
|
2
|
Ebrahimi A, Roshani F. Systems biology approaches to identify driver genes and drug combinations for treating COVID-19. Sci Rep 2024; 14:2257. [PMID: 38278931 PMCID: PMC10817985 DOI: 10.1038/s41598-024-52484-8] [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: 10/10/2023] [Accepted: 01/19/2024] [Indexed: 01/28/2024] Open
Abstract
Corona virus 19 (Covid-19) has caused many problems in public health, economic, and even cultural and social fields since the beginning of the epidemic. However, in order to provide therapeutic solutions, many researches have been conducted and various omics data have been published. But there is still no early diagnosis method and comprehensive treatment solution. In this manuscript, by collecting important genes related to COVID-19 and using centrality and controllability analysis in PPI networks and signaling pathways related to the disease; hub and driver genes have been identified in the formation and progression of the disease. Next, by analyzing the expression data, the obtained genes have been evaluated. The results show that in addition to the significant difference in the expression of most of these genes, their expression correlation pattern is also different in the two groups of COVID-19 and control. Finally, based on the drug-gene interaction, drugs affecting the identified genes are presented in the form of a bipartite graph, which can be used as the potential drug combinations.
Collapse
Affiliation(s)
- Ali Ebrahimi
- Department of Physics, Alzahra University, Tehran, Iran
| | | |
Collapse
|
3
|
Zhang K, Chen F, Shen HY, Zhang PP, Gao H, Peng H, Luo YS, Cheng ZS. Regulatory variants of APOBEC3 genes potentially associate with COVID-19 severity in populations with African ancestry. Sci Rep 2023; 13:22435. [PMID: 38105291 PMCID: PMC10725877 DOI: 10.1038/s41598-023-49791-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 12/12/2023] [Indexed: 12/19/2023] Open
Abstract
Since November 2019, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused the worldwide pandemic of the coronavirus disease 2019 (COVID-19), the impact of which is huge to the lives of world populations. Many studies suggested that such situation will continue due to the endless mutations in SARS-CoV-2 genome that result in complexity of the efforts for the control of SARS-CoV-2, since the special enrichment of nucleotide substitution C>U in SARS-CoV-2 sequences were discovered mainly due to the editing by human host factors APOBEC3 genes. The observation of SARS-CoV-2 variants Beta (B.1.351) and Omicron (B.1.1.529) firstly spreading in South Africa promoted us to hypothesize that genetic variants of APOBEC3 special in African populations may be attributed to the higher mutation rate of SARS-CoV-2 variants in Africa. Current study was conducted to search for functional variants of APOBEC3 genes associate with COVID-19 hospitalization in African population. By integrating data from the 1000 Genomes Project, Genotype-Tissue Expression (GTEx), and Host Genetics Initiative (HGI) of COVID-19, we identified potential functional SNPs close to APOBEC3 genes that are associated with COVID-19 hospitalization in African but not with other populations. Our study provides new insights on the potential contribution of APOBEC3 genes on the evolution of SARS-CoV-2 mutations in African population, but further replication is needed to confirm our results.
Collapse
Affiliation(s)
- Ke Zhang
- The Key and Characteristic Laboratory of Modern Pathogenicity Biology, School of Basic Medical Sciences, Guizhou Medical University, Guiyang, 561113, China
| | - Fang Chen
- The Key and Characteristic Laboratory of Modern Pathogenicity Biology, School of Basic Medical Sciences, Guizhou Medical University, Guiyang, 561113, China
| | - Hu-Yan Shen
- The Key and Characteristic Laboratory of Modern Pathogenicity Biology, School of Basic Medical Sciences, Guizhou Medical University, Guiyang, 561113, China
| | - Ping-Ping Zhang
- The Key and Characteristic Laboratory of Modern Pathogenicity Biology, School of Basic Medical Sciences, Guizhou Medical University, Guiyang, 561113, China
| | - Han Gao
- The Department of Emergency ICU, The Affiliated Hospital of Guizhou Medical University, Guiyang, 550004, China
| | - Hong Peng
- The Department of Emergency ICU, The Affiliated Hospital of Guizhou Medical University, Guiyang, 550004, China
| | - Yu-Si Luo
- The Department of Emergency ICU, The Affiliated Hospital of Guizhou Medical University, Guiyang, 550004, China.
- The Department of Emergency, Liupanshui Hospital of The Affiliated Hospital of Guizhou Medical University, Liupanshui, 553000, China.
| | - Zhong-Shan Cheng
- Center for Applied Bioinformatics, St. Jude Children's Research Hospital, 262 Danny Thomas Hospital, MS1122, Memphis, TN, 38105, USA.
| |
Collapse
|
4
|
SALİHOĞLU R, SARAÇOĞLU F, SİBAİ M, ZENGİN T, ABAK MASUD B, KARASOY O, SÜZEK T. CompCorona: A web application for comparative transcriptome analyses of coronaviruses reveals SARS-CoV-2-specific host response. Turk J Biol 2023; 47:393-405. [PMID: 38681774 PMCID: PMC11045204 DOI: 10.55730/1300-0152.2673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 12/28/2023] [Accepted: 12/15/2023] [Indexed: 05/01/2024] Open
Abstract
Background/aim Understanding the mechanism of host transcriptomic response to infection by the SARS-CoV-2 virus is crucial, especially for patients suffering from long-term effects of COVID-19, such as long COVID or pericarditis inflammation, potentially linked to side effects of the SARS-CoV-2 spike proteins. We conducted comprehensive transcriptome and enrichment analyses on lung and peripheral blood mononuclear cells (PBMCs) infected with SARS-CoV-2, as well as on SARS-CoV and MERS-CoV, to uncover shared pathways and elucidate their common disease progression and viral replication mechanisms. Materials and methods We developed CompCorona, the first interactive online tool for visualizing gene response variance among the family Coronaviridae through 2D and 3D principal component analysis (PCA) and exploring systems biology variance using pathway plots. We also made preprocessed datasets of lungs and PBMCs infected by SARS-CoV-2, SARS-CoV, and MERS-CoV publicly available through CompCorona. Results One remarkable finding from the lung and PBMC datasets for infections by SARS-CoV-2, but not infections by other coronaviruses (CoVs), was the significant downregulation of the angiogenin (ANG) and vascular endothelial growth factor A (VEGFA) genes, both directly involved in epithelial and vascular endothelial cell dysfunction. Suppression of the TNF signaling pathway was also observed in cells infected by SARS-CoV-2, along with simultaneous activation of complement and coagulation cascades and pertussis pathways. The ribosome pathway was found to be universally suppressed across all three viruses. The CompCorona online tool enabled the comparative analysis of 9 preprocessed host transcriptome datasets of cells infected by CoVs, revealing the specific host response differences in cases of SARS-CoV-2 infection. This included identifying markers of epithelial dysfunction via interactive 2D and 3D PCA, Venn diagrams, and pathway plots. Conclusion Our findings suggest that infection by SARS-CoV-2 might induce pulmonary epithelial dysfunction, a phenomenon not observed in cells infected by other CoVs. The publicly available CompCorona tool, along with the preprocessed datasets of cells infected by various CoVs, constitutes a valuable resource for further research into CoV-associated syndromes.
Collapse
Affiliation(s)
- Rana SALİHOĞLU
- Department of Bioinformatics, University of Würzburg, Würzburg,
Germany
- Department of Bioinformatics, Graduate School of Science and Engineering, Muğla Sıtkı Koçman University, Muğla,
Turkiye
| | - Fatih SARAÇOĞLU
- Department of Computer Engineering, Faculty of Engineering, Muğla Sıtkı Koçman University, Muğla,
Turkiye
| | - Mustafa SİBAİ
- Josep Carreras Leukaemia Research Institute (IJC), Badalona,
Spain
| | - Talip ZENGİN
- Department of Bioinformatics, Graduate School of Science and Engineering, Muğla Sıtkı Koçman University, Muğla,
Turkiye
- Department of Molecular Biology and Genetics, Faculty of Science, Muğla Sıtkı Koçman University, Muğla,
Turkiye
| | - Başak ABAK MASUD
- Department of Bioinformatics, Graduate School of Science and Engineering, Muğla Sıtkı Koçman University, Muğla,
Turkiye
| | - Onur KARASOY
- Department of Bioinformatics, Graduate School of Science and Engineering, Muğla Sıtkı Koçman University, Muğla,
Turkiye
| | - Tuğba SÜZEK
- Department of Bioinformatics, Graduate School of Science and Engineering, Muğla Sıtkı Koçman University, Muğla,
Turkiye
- Department of Computer Engineering, Faculty of Engineering, Muğla Sıtkı Koçman University, Muğla,
Turkiye
| |
Collapse
|
5
|
Tan M, Xia J, Luo H, Meng G, Zhu Z. Applying the digital data and the bioinformatics tools in SARS-CoV-2 research. Comput Struct Biotechnol J 2023; 21:4697-4705. [PMID: 37841328 PMCID: PMC10568291 DOI: 10.1016/j.csbj.2023.09.044] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 09/29/2023] [Accepted: 09/29/2023] [Indexed: 10/17/2023] Open
Abstract
Bioinformatics has been playing a crucial role in the scientific progress to fight against the pandemic of the coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The advances in novel algorithms, mega data technology, artificial intelligence and deep learning assisted the development of novel bioinformatics tools to analyze daily increasing SARS-CoV-2 data in the past years. These tools were applied in genomic analyses, evolutionary tracking, epidemiological analyses, protein structure interpretation, studies in virus-host interaction and clinical performance. To promote the in-silico analysis in the future, we conducted a review which summarized the databases, web services and software applied in SARS-CoV-2 research. Those digital resources applied in SARS-CoV-2 research may also potentially contribute to the research in other coronavirus and non-coronavirus viruses.
Collapse
Affiliation(s)
- Meng Tan
- School of Life Sciences, Chongqing University, Chongqing, China
| | - Jiaxin Xia
- School of Life Sciences, Chongqing University, Chongqing, China
| | - Haitao Luo
- School of Life Sciences, Chongqing University, Chongqing, China
| | - Geng Meng
- College of Veterinary Medicine, China Agricultural University, Beijing, China
| | - Zhenglin Zhu
- School of Life Sciences, Chongqing University, Chongqing, China
| |
Collapse
|
6
|
Zhang F, Luna A, Tan T, Chen Y, Sander C, Guo T. COVIDpro: Database for Mining Protein Dysregulation in Patients with COVID-19. J Proteome Res 2023; 22:2847-2859. [PMID: 37555633 DOI: 10.1021/acs.jproteome.3c00092] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/10/2023]
Abstract
The ongoing pandemic of the coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 still has limited treatment options. Our understanding of the molecular dysregulations that occur in response to infection remains incomplete. We developed a web application COVIDpro (https://www.guomics.com/covidPro/) that includes proteomics data obtained from 41 original studies conducted in 32 hospitals worldwide, involving 3077 patients and covering 19 types of clinical specimens, predominantly plasma and serum. The data set encompasses 53 protein expression matrices, comprising a total of 5434 samples and 14,403 unique proteins. We identified a panel of proteins that exhibit significant dysregulation, enabling the classification of COVID-19 patients into severe and non-severe disease categories. The proteomic signatures achieved promising results in distinguishing severe cases, with a mean area under the curve of 0.87 and accuracy of 0.80 across five independent test sets. COVIDpro serves as a valuable resource for testing hypotheses and exploring potential targets for novel treatments in COVID-19 patients.
Collapse
Affiliation(s)
- Fangfei Zhang
- Fudan University, 220 Handan Road, Shanghai 200433, China
- Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province 310024, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang Province 310024, China
- Research Center for Industries of the Future, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030, China
| | - Augustin Luna
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts 02115, United States
- Broad Institute of MIT and Harvard, Cambridge, Cambridge, Massachusetts 02142, United States
| | - Tingting Tan
- Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province 310024, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang Province 310024, China
- Research Center for Industries of the Future, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030, China
| | - Yingdan Chen
- Westlake Omics (Hangzhou) Biotechnology Company Limited, Hangzhou, Zhejiang Province 310024, China
| | - Chris Sander
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts 02115, United States
- Broad Institute of MIT and Harvard, Cambridge, Cambridge, Massachusetts 02142, United States
| | - Tiannan Guo
- Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province 310024, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang Province 310024, China
- Research Center for Industries of the Future, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030, China
| |
Collapse
|
7
|
Gupta Y, Savytskyi OV, Coban M, Venugopal A, Pleqi V, Weber CA, Chitale R, Durvasula R, Hopkins C, Kempaiah P, Caulfield TR. Protein structure-based in-silico approaches to drug discovery: Guide to COVID-19 therapeutics. Mol Aspects Med 2023; 91:101151. [PMID: 36371228 PMCID: PMC9613808 DOI: 10.1016/j.mam.2022.101151] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 10/19/2022] [Accepted: 10/21/2022] [Indexed: 11/06/2022]
Abstract
With more than 5 million fatalities and close to 300 million reported cases, COVID-19 is the first documented pandemic due to a coronavirus that continues to be a major health challenge. Despite being rapid, uncontrollable, and highly infectious in its spread, it also created incentives for technology development and redefined public health needs and research agendas to fast-track innovations to be translated. Breakthroughs in computational biology peaked during the pandemic with renewed attention to making all cutting-edge technology deliver agents to combat the disease. The demand to develop effective treatments yielded surprising collaborations from previously segregated fields of science and technology. The long-standing pharmaceutical industry's aversion to repurposing existing drugs due to a lack of exponential financial gain was overrun by the health crisis and pressures created by front-line researchers and providers. Effective vaccine development even at an unprecedented pace took more than a year to develop and commence trials. Now the emergence of variants and waning protections during the booster shots is resulting in breakthrough infections that continue to strain health care systems. As of now, every protein of SARS-CoV-2 has been structurally characterized and related host pathways have been extensively mapped out. The research community has addressed the druggability of a multitude of possible targets. This has been made possible due to existing technology for virtual computer-assisted drug development as well as new tools and technologies such as artificial intelligence to deliver new leads. Here in this article, we are discussing advances in the drug discovery field related to target-based drug discovery and exploring the implications of known target-specific agents on COVID-19 therapeutic management. The current scenario calls for more personalized medicine efforts and stratifying patient populations early on for their need for different combinations of prognosis-specific therapeutics. We intend to highlight target hotspots and their potential agents, with the ultimate goal of using rational design of new therapeutics to not only end this pandemic but also uncover a generalizable platform for use in future pandemics.
Collapse
Affiliation(s)
- Yash Gupta
- Department of Medicine, Infectious Diseases, Mayo Clinic, Jacksonville, FL, USA
| | - Oleksandr V Savytskyi
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA; In Vivo Biosystems, Eugene, OR, USA
| | - Matt Coban
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA; Department of Cancer Biology, Mayo Clinic, Jacksonville, FL, USA
| | | | - Vasili Pleqi
- Department of Medicine, Infectious Diseases, Mayo Clinic, Jacksonville, FL, USA
| | - Caleb A Weber
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA
| | - Rohit Chitale
- Department of Medicine, Infectious Diseases, Mayo Clinic, Jacksonville, FL, USA; The Council on Strategic Risks, 1025 Connecticut Ave NW, Washington, DC, USA
| | - Ravi Durvasula
- Department of Medicine, Infectious Diseases, Mayo Clinic, Jacksonville, FL, USA
| | | | - Prakasha Kempaiah
- Department of Medicine, Infectious Diseases, Mayo Clinic, Jacksonville, FL, USA
| | - Thomas R Caulfield
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA; Department of QHS Computational Biology, Mayo Clinic, Jacksonville, FL, USA; Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, MN, USA; Department of Clinical Genomics, Mayo Clinic, Rochester, MN, USA; Department of Neurosurgery, Mayo Clinic, Jacksonville, FL, USA.
| |
Collapse
|
8
|
Santoni D, Ghosh N, Derelitto C, Saha I. Transcription Factor Driven Gene Regulation in COVID-19 Patients. Viruses 2023; 15:v15051188. [PMID: 37243274 DOI: 10.3390/v15051188] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 05/11/2023] [Accepted: 05/15/2023] [Indexed: 05/28/2023] Open
Abstract
SARS-CoV-2 and its many variants have caused a worldwide emergency. Host cells colonised by SARS-CoV-2 present a significantly different gene expression landscape. As expected, this is particularly true for genes that directly interact with virus proteins. Thus, understanding the role that transcription factors can play in driving differential regulation in patients affected by COVID-19 is a focal point to unveil virus infection. In this regard, we have identified 19 transcription factors which are predicted to target human proteins interacting with Spike glycoprotein of SARS-CoV-2. Transcriptomics RNA-Seq data derived from 13 human organs are used to analyse expression correlation between identified transcription factors and related target genes in both COVID-19 patients and healthy individuals. This resulted in the identification of transcription factors showing the most relevant impact in terms of most evident differential correlation between COVID-19 patients and healthy individuals. This analysis has also identified five organs such as the blood, heart, lung, nasopharynx and respiratory tract in which a major effect of differential regulation mediated by transcription factors is observed. These organs are also known to be affected by COVID-19, thereby providing consistency to our analysis. Furthermore, 31 key human genes differentially regulated by the transcription factors in the five organs are identified and the corresponding KEGG pathways and GO enrichment are also reported. Finally, the drugs targeting those 31 genes are also put forth. This in silico study explores the effects of transcription factors on human genes interacting with Spike glycoprotein of SARS-CoV-2 and intends to provide new insights to inhibit the virus infection.
Collapse
Affiliation(s)
- Daniele Santoni
- Institute for System Analysis and Computer Science "Antonio Ruberti", National Research Council of Italy, 00185 Rome, Italy
| | - Nimisha Ghosh
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, 02-097 Warsaw, Poland
- Department of Computer Science and Information Technology, Institute of Technical Education and Research, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar 751030, India
| | - Carlo Derelitto
- Institute for System Analysis and Computer Science "Antonio Ruberti", National Research Council of Italy, 00185 Rome, Italy
- Department of Biological, Geological and Environmental Sciences, Alma Mater Studiorum-University of Bologna, 40138 Bologna, Italy
| | - Indrajit Saha
- Department of Computer Science and Engineering, National Institute of Technical Teachers' Training and Research, Kolkata 700106, India
| |
Collapse
|
9
|
3D culture of the spinal cord with roots as an ex vivo model for comparative studies of motor and sensory nerve regeneration. Exp Neurol 2023; 362:114322. [PMID: 36652972 DOI: 10.1016/j.expneurol.2023.114322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 12/21/2022] [Accepted: 01/13/2023] [Indexed: 01/18/2023]
Abstract
Motor and sensory nerves exhibit tissue-specific structural and functional features. However, in vitro models designed to reflect tissue-specific differences between motor and sensory nerve regeneration have rarely been reported. Here, by embedding the spinal cord with roots (SCWR) in a 3D hydrogel environment, we compared the nerve regeneration processes between the ventral and dorsal roots. The 3D hydrogel environment induced an outward migration of neurons in the gray matter of the spinal cord, which allowed the long-term survival of motor neurons. Tuj1 immunofluorescence labeling confirmed the regeneration of neurites from both the ventral and dorsal roots. Next, we detected asymmetric ventral and dorsal root regeneration in response to nerve growth factor (NGF) and glial cell line-derived neurotrophic factor (GDNF), and we observed motor and sensory Schwann cell phenotypes in the regenerated ventral and dorsal roots, respectively. Moreover, based on the SCWR model, we identified a targeted effect of collagen VI on sensory nerve fasciculation and characterized the protein expression profiles correlating to motor/sensory-specific nerve regeneration. These results suggest that the SCWR model can serve as a valuable ex vivo model for comparative study of motor and sensory nerve regeneration and for pharmacodynamic evaluations.
Collapse
|
10
|
Zhu K, Chen Z, Xiao Y, Lai D, Wang X, Fang X, Shu Q. Multi-omics and immune cells' profiling of COVID-19 patients for ICU admission prediction: in silico analysis and an integrated machine learning-based approach in the framework of Predictive, Preventive, and Personalized Medicine. EPMA J 2023; 14:1-17. [PMID: 36845281 PMCID: PMC9942629 DOI: 10.1007/s13167-023-00317-5] [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: 08/31/2022] [Accepted: 02/01/2023] [Indexed: 02/23/2023]
Abstract
Background Intensive care unit admission (ICUA) triage has been urgent need for solving the shortage of ICU beds, during the coronavirus disease 2019 (COVID-19) surge. In silico analysis and integrated machine learning (ML) approach, based on multi-omics and immune cells (ICs) profiling, might provide solutions for this issue in the framework of predictive, preventive, and personalized medicine (PPPM). Methods Multi-omics was used to screen the synchronous differentially expressed protein-coding genes (SDEpcGs), and an integrated ML approach to develop and validate a nomogram for prediction of ICUA. Finally, the independent risk factor (IRF) with ICs profiling of the ICUA was identified. Results Colony-stimulating factor 1 receptor (CSF1R) and peptidase inhibitor 16 (PI16) were identified as SDEpcGs, and each fold change (FCij) of CSF1R and PI16 was selected to develop and validate a nomogram to predict ICUA. The area under curve (AUC) of the nomogram was 0.872 (95% confidence interval (CI): 0.707 to 0.950) on the training set, and 0.822 (95% CI: 0.659 to 0.917) on the testing set. CSF1R was identified as an IRF of ICUA, expressed in and positively correlated with monocytes which had a lower fraction in COVID-19 ICU patients. Conclusion The nomogram and monocytes could provide added value to ICUA prediction and targeted prevention, which are cost-effective platform for personalized medicine of COVID-19 patients. The log2fold change (log2FC) of the fraction of monocytes could be monitored simply and economically in primary care, and the nomogram offered an accurate prediction for secondary care in the framework of PPPM. Supplementary Information The online version contains supplementary material available at 10.1007/s13167-023-00317-5.
Collapse
Affiliation(s)
- Kun Zhu
- Department of Pathology, Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China
| | - Zhonghua Chen
- Department of Anesthesiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China ,Department of Anesthesiology, Shaoxing People’s Hospital (Shaoxing Hospital, Zhejiang University School of Medicine), Shaoxing, China
| | - Yi Xiao
- Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China
| | - Dengming Lai
- Department of Neonatal Surgery, Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China
| | - Xiaofeng Wang
- Department of Information Center, Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China
| | - Xiangming Fang
- Department of Anesthesiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Qiang Shu
- Department of Thoracic and Cardiovascular Surgery, Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China
| |
Collapse
|
11
|
Avram S, Wilson TB, Curpan R, Halip L, Borota A, Bora A, Bologa C, Holmes J, Knockel J, Yang J, Oprea T. DrugCentral 2023 extends human clinical data and integrates veterinary drugs. Nucleic Acids Res 2022; 51:D1276-D1287. [PMID: 36484092 PMCID: PMC9825566 DOI: 10.1093/nar/gkac1085] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 10/20/2022] [Accepted: 12/02/2022] [Indexed: 12/14/2022] Open
Abstract
DrugCentral monitors new drug approvals and standardizes drug information. The current update contains 285 drugs (131 for human use). New additions include: (i) the integration of veterinary drugs (154 for animal use only), (ii) the addition of 66 documented off-label uses and iii) the identification of adverse drug events from pharmacovigilance data for pediatric and geriatric patients. Additional enhancements include chemical substructure searching using SMILES and 'Target Cards' based on UniProt accession codes. Statistics of interests include the following: (i) 60% of the covered drugs are on-market drugs with expired patent and exclusivity coverage, 17% are off-market, and 23% are on-market drugs with active patents and exclusivity coverage; (ii) 59% of the drugs are oral, 33% are parenteral and 18% topical, at the level of the active ingredients; (iii) only 3% of all drugs are for animal use only; however, 61% of the veterinary drugs are also approved for human use; (iv) dogs, cats and horses are by far the most represented target species for veterinary drugs; (v) the physicochemical property profile of animal drugs is very similar to that of human drugs. Use cases include azaperone, the only sedative approved for swine, and ruxolitinib, a Janus kinase inhibitor.
Collapse
Affiliation(s)
| | | | - Ramona Curpan
- Department of Computational Chemistry, “Coriolan Dragulescu” Institute of Chemistry, 24 Mihai Viteazu Blvd, Timişoara, Timiş 300223, Romania
| | - Liliana Halip
- Department of Computational Chemistry, “Coriolan Dragulescu” Institute of Chemistry, 24 Mihai Viteazu Blvd, Timişoara, Timiş 300223, Romania
| | - Ana Borota
- Department of Computational Chemistry, “Coriolan Dragulescu” Institute of Chemistry, 24 Mihai Viteazu Blvd, Timişoara, Timiş 300223, Romania
| | - Alina Bora
- Department of Computational Chemistry, “Coriolan Dragulescu” Institute of Chemistry, 24 Mihai Viteazu Blvd, Timişoara, Timiş 300223, Romania
| | - Cristian G Bologa
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, 700 Camino de Salud NE, Albuquerque, NM 87106, USA
| | - Jayme Holmes
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, 700 Camino de Salud NE, Albuquerque, NM 87106, USA
| | - Jeffrey Knockel
- Department of Computer Science, University of New Mexico, 1901 Redondo S Dr, Albuquerque, NM 87106, USA
| | - Jeremy J Yang
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, 700 Camino de Salud NE, Albuquerque, NM 87106, USA
| | - Tudor I Oprea
- To whom correspondence should be addressed. Tel: +1 505 925 7529; Fax: +1 505 925 7625;
| |
Collapse
|
12
|
Ghosh N, Saha I, Plewczynski D. Unveiling the Biomarkers of Cancer and COVID-19 and Their Regulations in Different Organs by Integrating RNA-Seq Expression and Protein-Protein Interactions. ACS OMEGA 2022; 7:43589-43602. [PMID: 36506181 PMCID: PMC9730762 DOI: 10.1021/acsomega.2c04389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 10/13/2022] [Indexed: 06/17/2023]
Abstract
Cancer and COVID-19 have killed millions of people worldwide. COVID-19 is even more dangerous to people with comorbidities such as cancer. Thus, it is imperative to identify the key human genes or biomarkers that can be targeted to develop novel prognosis and therapeutic strategies. The transcriptomic data provided by the next-generation sequencing technique makes this identification very convenient. Hence, mRNA (messenger ribonucleic acid) expression data of 2265 cancer and 282 normal patients were considered, while for COVID-19 assessment, 784 and 425 COVID-19 and normal patients were taken, respectively. Initially, volcano plots were used to identify the up- and down-regulated genes for both cancer and COVID-19. Thereafter, protein-protein interaction (PPI) networks were prepared by combining all the up- and down-regulated genes for each of cancer and COVID-19. Subsequently, such networks were analyzed to identify the top 10 genes with the highest degree of connection to provide the biomarkers. Interestingly, these genes were all up-regulated for cancer, while they were down-regulated for COVID-19. This study had also identified common genes between cancer and COVID-19, all of which were up-regulated in both the diseases. This analysis revealed that FN1 was highly up-regulated in different organs for cancer, while EEF2 was dysregulated in most organs affected by COVID-19. Then, functional enrichment analysis was performed to identify significant biological processes. Finally, the drugs for cancer and COVID-19 biomarkers and the common genes between them were identified using the Enrichr online web tool. These drugs include lucanthone, etoposide, and methotrexate, targeting the biomarkers for cancer, while paclitaxel is an important drug for COVID-19.
Collapse
Affiliation(s)
- Nimisha Ghosh
- Faculty
of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw 02-097, Poland
- Department
of Computer Science and Information Technology, Institute of Technical
Education and Research, Siksha ‘O’
Anusandhan (Deemed to Be University), Bhubaneswar 751030 Odisha, India
| | - Indrajit Saha
- Department
of Computer Science and Engineering, National
Institute of Technical Teachers’ Training and Research, Kolkata 700106 West Bengal, India
| | - Dariusz Plewczynski
- Laboratory
of Functional and Structural Genomics, Centre of New Technologies, University of Warsaw, Warsaw 02-097, Poland
- Laboratory
of Bioinformatics and Computational Genomics, Faculty of Mathematics
and Information Science, Warsaw University
of Technology, Warsaw 00-662, Poland
| |
Collapse
|
13
|
Romeo I, Prandi IG, Giombini E, Gruber CEM, Pietrucci D, Borocci S, Abid N, Fava A, Beccari AR, Chillemi G, Talarico C. The Spike Mutants Website: A Worldwide Used Resource against SARS-CoV-2. Int J Mol Sci 2022; 23:13082. [PMID: 36361870 PMCID: PMC9654198 DOI: 10.3390/ijms232113082] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 10/24/2022] [Accepted: 10/24/2022] [Indexed: 07/22/2023] Open
Abstract
A large number of SARS-CoV-2 mutations in a short period of time has driven scientific research related to vaccines, new drugs, and antibodies to combat the new variants of the virus. Herein, we present a web portal containing the structural information, the tridimensional coordinates, and the molecular dynamics trajectories of the SARS-CoV-2 spike protein and its main variants. The Spike Mutants website can serve as a rapid online tool for investigating the impact of novel mutations on virus fitness. Taking into account the high variability of SARS-CoV-2, this application can help the scientific community when prioritizing molecules for experimental assays, thus, accelerating the identification of promising drug candidates for COVID-19 treatment. Below we describe the main features of the platform and illustrate the possible applications for speeding up the drug discovery process and hypothesize new effective strategies to overcome the recurrent mutations in SARS-CoV-2 genome.
Collapse
Affiliation(s)
- Isabella Romeo
- Dipartimento di Scienze della Salute, Università Magna Græcia di Catanzaro, Campus “S. Venuta”, Viale Europa, 88100 Catanzaro, Italy
- Net4Science Academic Spin-Off, Università Magna Graecia di Catanzaro, Campus “S. Venuta”, Viale Europa, 88100 Catanzaro, Italy
| | - Ingrid Guarnetti Prandi
- Department for Innovation in Biological, Agro-Food and Forest Systems (DIBAF), University of Tuscia, Via S. Camillo de Lellis s.n.c., 01100 Viterbo, Italy
| | - Emanuela Giombini
- Laboratory of Virology, INMI Lazzaro Spallanzani IRCCS, Via Portuense 292, 00149 Roma, Italy
| | | | - Daniele Pietrucci
- Department for Innovation in Biological, Agro-Food and Forest Systems (DIBAF), University of Tuscia, Via S. Camillo de Lellis s.n.c., 01100 Viterbo, Italy
- Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies (IBIOM), CNR, 70126 Bari, Italy
| | - Stefano Borocci
- Department for Innovation in Biological, Agro-Food and Forest Systems (DIBAF), University of Tuscia, Via S. Camillo de Lellis s.n.c., 01100 Viterbo, Italy
- Laboratory of Transmissible Diseases and Biological Active Substances LR99ES27, Faculty of Pharmacy, University of Monastir, Rue Ibn Sina, Monastir 5000, Tunisia
| | - Nabil Abid
- Laboratory of Transmissible Diseases and Biological Active Substances LR99ES27, Faculty of Pharmacy, University of Monastir, Rue Ibn Sina, Monastir 5000, Tunisia
- High Institute of Biotechnology of Monastir, Department of Molecular and Cellular Biology, University of Monastir, Monastir 5000, Tunisia
| | - Anna Fava
- Dompé Farmaceutici SpA, Via Tommaso De Amicis, 95, 80131 Napoli, Italy
| | - Andrea R. Beccari
- Dompé Farmaceutici SpA, Via Tommaso De Amicis, 95, 80131 Napoli, Italy
| | - Giovanni Chillemi
- Department for Innovation in Biological, Agro-Food and Forest Systems (DIBAF), University of Tuscia, Via S. Camillo de Lellis s.n.c., 01100 Viterbo, Italy
| | - Carmine Talarico
- Dompé Farmaceutici SpA, Via Tommaso De Amicis, 95, 80131 Napoli, Italy
| |
Collapse
|
14
|
Luo YS, Li W, Cai Y, Zhang J, Gui H, Zhang K, Cheng ZS. Genome-wide screening of sex-biased genetic variants potentially associated with COVID-19 hospitalization. Front Genet 2022; 13:1014191. [DOI: 10.3389/fgene.2022.1014191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 09/29/2022] [Indexed: 11/13/2022] Open
Abstract
Sex-biased difference in coronavirus disease 2019 (COVID-19) hospitalization has been observed as that male patients tend to be more likely to be hospitalized than female patients. However, due to the insufficient sample size and existed studies that more prioritized to sex-stratified COVID-19 genome-wide association study (GWAS), the searching for sex-biased genetic variants showing differential association signals between sexes with COVID-19 hospitalization was severely hindered. We hypothesized genetic variants would show potentially sex-biased genetic effects on COVID-19 hospitalization if they display significant differential association effect sizes between male and female COVID-19 patients. By integrating two COVID-19 GWASs, including hospitalized COVID-19 patients vs. general population separated into males (case = 1,917 and control = 221,174) and females (case = 1,343 and control = 262,886), we differentiated the association effect sizes of each common single nucleotide polymorphism (SNP) within the two GWASs. Twelve SNPs were suggested to show differential COVID-19 associations between sexes. Further investigation of genes (n = 58) close to these 12 SNPs resulted in the identification of 34 genes demonstrating sex-biased differential expression in at least one GTEx tissue. Finally, 5 SNPs are mapped to 8 genes, including rs1134004 (GADD45G), rs140657166 (TRIM29 and PVRL1), rs148143613 (KNDC1 and STK32C), rs2443615 (PGAP2 and TRIM21), and rs2924725 (CSMD1). The 8 genes display significantly differential gene expression in blood samples derived from COVID-19 patients compared to healthy controls. These genes are potential genetic factors contributing to sex differences in COVID-19 hospitalization and warranted for further functional studies.
Collapse
|
15
|
Zhang F, Luna A, Tan T, Chen Y, Sander C, Guo T. COVIDpro: Database for mining protein dysregulation in patients with COVID-19. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2022:2022.09.27.509819. [PMID: 36203550 PMCID: PMC9536031 DOI: 10.1101/2022.09.27.509819] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Background The ongoing pandemic of the coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) still has limited treatment options partially due to our incomplete understanding of the molecular dysregulations of the COVID-19 patients. We aimed to generate a repository and data analysis tools to examine the modulated proteins underlying COVID-19 patients for the discovery of potential therapeutic targets and diagnostic biomarkers. Methods We built a web server containing proteomic expression data from COVID-19 patients with a toolset for user-friendly data analysis and visualization. The web resource covers expert-curated proteomic data from COVID-19 patients published before May 2022. The data were collected from ProteomeXchange and from select publications via PubMed searches and aggregated into a comprehensive dataset. Protein expression by disease subgroups across projects was compared by examining differentially expressed proteins. We also visualize differentially expressed pathways and proteins. Moreover, circulating proteins that differentiated severe cases were nominated as predictive biomarkers. Findings We built and maintain a web server COVIDpro ( https://www.guomics.com/covidPro/ ) containing proteomics data generated by 41 original studies from 32 hospitals worldwide, with data from 3077 patients covering 19 types of clinical specimens, the majority from plasma and sera. 53 protein expression matrices were collected, for a total of 5434 samples and 14,403 unique proteins. Our analyses showed that the lipopolysaccharide-binding protein, as identified in the majority of the studies, was highly expressed in the blood samples of patients with severe disease. A panel of significantly dysregulated proteins was identified to separate patients with severe disease from non-severe disease. Classification of severe disease based on these proteomic signatures on five test sets reached a mean AUC of 0.87 and ACC of 0.80. Interpretation COVIDpro is an online database with an integrated analysis toolkit. It is a unique and valuable resource for testing hypotheses and identifying proteins or pathways that could be targeted by new treatments of COVID-19 patients. Funding National Key R&D Program of China: Key PDPM technologies (2021YFA1301602, 2021YFA1301601, 2021YFA1301603), Zhejiang Provincial Natural Science Foundation for Distinguished Young Scholars (LR19C050001), Hangzhou Agriculture and Society Advancement Program (20190101A04), National Natural Science Foundation of China (81972492) and National Science Fund for Young Scholars (21904107), National Resource for Network Biology (NRNB) from the National Institute of General Medical Sciences (NIGMS-P41 GM103504). Research in context Evidence before this study: Although an increasing number of therapies against COVID-19 are being developed, they are still insufficient, especially with the rise of new variants of concern. This is partially due to our incomplete understanding of the disease’s mechanisms. As data have been collected worldwide, several questions are now worth addressing via meta-analyses. Most COVID-19 drugs function by targeting or affecting proteins. Effectiveness and resistance to therapeutics can be effectively assessed via protein measurements. Empowered by mass spectrometry-based proteomics, protein expression has been characterized in a variety of patient specimens, including body fluids (e.g., serum, plasma, urea) and tissue (i.e., formalin-fixed and paraffin-embedded (FFPE)). We expert-curated proteomic expression data from COVID-19 patients published before May 2022, from the largest proteomic data repository ProteomeXhange as well as from literature search engines. Using this resource, a COVID-19 proteome meta-analysis could provide useful insights into the mechanisms of the disease and identify new potential drug targets.Added value of this study: We integrated many published datasets from patients with COVID-19 from 11 nations, with over 3000 patients and more than 5434 proteome measurements. We collected these datasets in an online database, and generated a toolbox to easily explore, analyze, and visualize the data. Next, we used the database and its associated toolbox to identify new proteins of diagnostic and therapeutic value for COVID-19 treatment. In particular, we identified a set of significantly dysregulated proteins for distinguishing severe from non-severe patients using serum samples.Implications of all the available evidence: COVIDpro will support the navigation and analysis of patterns of dysregulated proteins in various COVID-19 clinical specimens for identification and verification of protein biomarkers and potential therapeutic targets.
Collapse
|
16
|
Saha S, Chatterjee P, Halder AK, Nasipuri M, Basu S, Plewczynski D. ML-DTD: Machine Learning-Based Drug Target Discovery for the Potential Treatment of COVID-19. Vaccines (Basel) 2022; 10:1643. [PMID: 36298508 PMCID: PMC9607653 DOI: 10.3390/vaccines10101643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 09/11/2022] [Accepted: 09/14/2022] [Indexed: 11/05/2022] Open
Abstract
Recent research has highlighted that a large section of druggable protein targets in the Human interactome remains unexplored for various diseases. It might lead to the drug repurposing study and help in the in-silico prediction of new drug-human protein target interactions. The same applies to the current pandemic of COVID-19 disease in global health issues. It is highly desirable to identify potential human drug targets for COVID-19 using a machine learning approach since it saves time and labor compared to traditional experimental methods. Structure-based drug discovery where druggability is determined by molecular docking is only appropriate for the protein whose three-dimensional structures are available. With machine learning algorithms, differentiating relevant features for predicting targets and non-targets can be used for the proteins whose 3-D structures are unavailable. In this research, a Machine Learning-based Drug Target Discovery (ML-DTD) approach is proposed where a machine learning model is initially built up and tested on the curated dataset consisting of COVID-19 human drug targets and non-targets formed by using the Therapeutic Target Database (TTD) and human interactome using several classifiers like XGBBoost Classifier, AdaBoost Classifier, Logistic Regression, Support Vector Classification, Decision Tree Classifier, Random Forest Classifier, Naive Bayes Classifier, and K-Nearest Neighbour Classifier (KNN). In this method, protein features include Gene Set Enrichment Analysis (GSEA) ranking, properties derived from the protein sequence, and encoded protein network centrality-based measures. Among all these, XGBBoost, KNN, and Random Forest models are satisfactory and consistent. This model is further used to predict novel COVID-19 human drug targets, which are further validated by target pathway analysis, the emergence of allied repurposed drugs, and their subsequent docking study.
Collapse
Affiliation(s)
- Sovan Saha
- Department of Computer Science & Engineering, Institute of Engineering & Management, Salt Lake Electronics Complex, Kolkata 700091, India
| | - Piyali Chatterjee
- Department of Computer Science & Engineering, Netaji Subhash Engineering College, Techno City, Panchpota, Garia, Kolkata 700152, India
| | - Anup Kumar Halder
- Faculty of Mathematics and Information Sciences, Warsaw University of Technology, Koszykowa 75, 00-662 Warsaw, Poland
- Laboratory of Functional and Structural Genomics, Centre of New Technologies, University of Warsaw, Banacha 2c Street, 02-097 Warsaw, Poland
| | - Mita Nasipuri
- Department of Computer Science & Engineering, Jadavpur University, 188, Raja S.C. Mallick Road, Kolkata 700032, India
| | - Subhadip Basu
- Department of Computer Science & Engineering, Jadavpur University, 188, Raja S.C. Mallick Road, Kolkata 700032, India
| | - Dariusz Plewczynski
- Faculty of Mathematics and Information Sciences, Warsaw University of Technology, Koszykowa 75, 00-662 Warsaw, Poland
- Laboratory of Functional and Structural Genomics, Centre of New Technologies, University of Warsaw, Banacha 2c Street, 02-097 Warsaw, Poland
| |
Collapse
|
17
|
Transcriptomics, Cheminformatics, and Systems Pharmacology Strategies Unveil the Potential Bioactives to Combat COVID-19. Molecules 2022; 27:molecules27185955. [PMID: 36144690 PMCID: PMC9503185 DOI: 10.3390/molecules27185955] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 09/08/2022] [Accepted: 09/10/2022] [Indexed: 11/17/2022] Open
Abstract
Coronavirus disease (COVID-19) is a viral disease caused by the SARS-CoV-2 virus and is becoming a global threat again because of the higher transmission rate and lack of proper therapeutics as well as the rapid mutations in the genetic pattern of SARS-CoV-2. Despite vaccinations, the prevalence and recurrence of this infection are still on the rise, which urges the identification of potential global therapeutics for a complete cure. Plant-based alternative medicine is becoming popular worldwide because of its higher efficiency and minimal side effects. Yet, identifying the potential medicinal plants and formulating a plant-based medicine is still a bottleneck. Hence, in this study, the systems pharmacology, transcriptomics, and cheminformatics approaches were employed to uncover the multi-targeted mechanisms and to screen the potential phytocompounds from significant medicinal plants to treat COVID-19. These approaches have identified 30 unique COVID-19 human immune genes targeted by the 25 phytocompounds present in four selected ethnobotanical plants. Differential and co-expression profiling and pathway enrichment analyses delineate the molecular signaling and immune functional regulations of the COVID-19 unique genes. In addition, the credibility of these compounds was analyzed by the pharmacological features. The current holistic finding is the first to explore whether the identified potential bioactives could reform into a drug candidate to treat COVID-19. Furthermore, the molecular docking analysis was employed to identify the important bioactive compounds; thus, an ultimately significant medicinal plant was also determined. However, further laboratory evaluation and clinical validation are required to determine the efficiency of a therapeutic formulation against COVID-19.
Collapse
|
18
|
Bińkowski J, Taryma-Leśniak O, Łuczkowska K, Niedzwiedź A, Lechowicz K, Strapagiel D, Jarczak J, Davalos V, Pujol A, Esteller M, Kotfis K, Machaliński B, Parczewski M, Wojdacz TK. Epigenetic activation of antiviral sensors and effectors of interferon response pathways during SARS-CoV-2 infection. Biomed Pharmacother 2022; 153:113396. [PMID: 36076479 PMCID: PMC9271528 DOI: 10.1016/j.biopha.2022.113396] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 07/01/2022] [Accepted: 07/07/2022] [Indexed: 12/15/2022] Open
Abstract
Recent studies have shown that methylation changes identified in blood cells of COVID-19 patients have a potential to be used as biomarkers of SARS-CoV-2 infection outcomes. However, different studies have reported different subsets of epigenetic lesions that stratify patients according to the severity of infection symptoms, and more importantly, the significance of those epigenetic changes in the pathology of the infection is still not clear. We used methylomics and transcriptomics data from the largest so far cohort of COVID-19 patients from four geographically distant populations, to identify casual interactions of blood cells’ methylome in pathology of the COVID-19 disease. We identified a subset of methylation changes that is uniformly present in all COVID-19 patients regardless of symptoms. Those changes are not present in patients suffering from upper respiratory tract infections with symptoms similar to COVID-19. Most importantly, the identified epigenetic changes affect the expression of genes involved in interferon response pathways and the expression of those genes differs between patients admitted to intensive care units and only hospitalized. In conclusion, the DNA methylation changes involved in pathophysiology of SARS-CoV-2 infection, which are specific to COVID-19 patients, can not only be utilized as biomarkers in the disease management but also present a potential treatment target.
Collapse
|
19
|
Liu XH, Cheng T, Liu BY, Chi J, Shu T, Wang T. Structures of the SARS-CoV-2 spike glycoprotein and applications for novel drug development. Front Pharmacol 2022; 13:955648. [PMID: 36016554 PMCID: PMC9395726 DOI: 10.3389/fphar.2022.955648] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Accepted: 07/13/2022] [Indexed: 12/14/2022] Open
Abstract
COVID-19 caused by SARS-CoV-2 has raised a health crisis worldwide. The high morbidity and mortality associated with COVID-19 and the lack of effective drugs or vaccines for SARS-CoV-2 emphasize the urgent need for standard treatment and prophylaxis of COVID-19. The receptor-binding domain (RBD) of the glycosylated spike protein (S protein) is capable of binding to human angiotensin-converting enzyme 2 (hACE2) and initiating membrane fusion and virus entry. Hence, it is rational to inhibit the RBD activity of the S protein by blocking the RBD interaction with hACE2, which makes the glycosylated S protein a potential target for designing and developing antiviral agents. In this study, the molecular features of the S protein of SARS-CoV-2 are highlighted, such as the structures, functions, and interactions of the S protein and ACE2. Additionally, computational tools developed for the treatment of COVID-19 are provided, for example, algorithms, databases, and relevant programs. Finally, recent advances in the novel development of antivirals against the S protein are summarized, including screening of natural products, drug repurposing and rational design. This study is expected to provide novel insights for the efficient discovery of promising drug candidates against the S protein and contribute to the development of broad-spectrum anti-coronavirus drugs to fight against SARS-CoV-2.
Collapse
|
20
|
Rigden DJ, Fernández XM. The 2022 Nucleic Acids Research database issue and the online molecular biology database collection. Nucleic Acids Res 2022; 50:D1-D10. [PMID: 34986604 PMCID: PMC8728296 DOI: 10.1093/nar/gkab1195] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
The 2022 Nucleic Acids Research Database Issue contains 185 papers, including 87 papers reporting on new databases and 85 updates from resources previously published in the Issue. Thirteen additional manuscripts provide updates on databases most recently published elsewhere. Seven new databases focus specifically on COVID-19 and SARS-CoV-2, including SCoV2-MD, the first of the Issue's Breakthrough Articles. Major nucleic acid databases reporting updates include MODOMICS, JASPAR and miRTarBase. The AlphaFold Protein Structure Database, described in the second Breakthrough Article, is the stand-out in the protein section, where the Human Proteoform Atlas and GproteinDb are other notable new arrivals. Updates from DisProt, FuzDB and ELM comprehensively cover disordered proteins. Under the metabolism and signalling section Reactome, ConsensusPathDB, HMDB and CAZy are major returning resources. In microbial and viral genomes taxonomy and systematics are well covered by LPSN, TYGS and GTDB. Genomics resources include Ensembl, Ensembl Genomes and UCSC Genome Browser. Major returning pharmacology resource names include the IUPHAR/BPS guide and the Therapeutic Target Database. New plant databases include PlantGSAD for gene lists and qPTMplants for post-translational modifications. The entire Database Issue is freely available online on the Nucleic Acids Research website (https://academic.oup.com/nar). Our latest update to the NAR online Molecular Biology Database Collection brings the total number of entries to 1645. Following last year's major cleanup, we have updated 317 entries, listing 89 new resources and trimming 80 discontinued URLs. The current release is available at http://www.oxfordjournals.org/nar/database/c/.
Collapse
Affiliation(s)
- Daniel J Rigden
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Crown Street, Liverpool L69 7ZB, UK
| | | |
Collapse
|