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Wu YK, Liu CD, Liu C, Wu J, Xie ZG. Machine learning and weighted gene co-expression network analysis identify a three-gene signature to diagnose rheumatoid arthritis. Front Immunol 2024; 15:1387311. [PMID: 38711508 PMCID: PMC11070572 DOI: 10.3389/fimmu.2024.1387311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Accepted: 04/08/2024] [Indexed: 05/08/2024] Open
Abstract
Background Rheumatoid arthritis (RA) is a systemic immune-related disease characterized by synovial inflammation and destruction of joint cartilage. The pathogenesis of RA remains unclear, and diagnostic markers with high sensitivity and specificity are needed urgently. This study aims to identify potential biomarkers in the synovium for diagnosing RA and to investigate their association with immune infiltration. Methods We downloaded four datasets containing 51 RA and 36 healthy synovium samples from the Gene Expression Omnibus database. Differentially expressed genes were identified using R. Then, various enrichment analyses were conducted. Subsequently, weighted gene co-expression network analysis (WGCNA), random forest (RF), support vector machine-recursive feature elimination (SVM-RFE), and least absolute shrinkage and selection operator (LASSO) were used to identify the hub genes for RA diagnosis. Receiver operating characteristic curves and nomogram models were used to validate the specificity and sensitivity of hub genes. Additionally, we analyzed the infiltration levels of 28 immune cells in the expression profile and their relationship with the hub genes using single-sample gene set enrichment analysis. Results Three hub genes, namely, ribonucleotide reductase regulatory subunit M2 (RRM2), DLG-associated protein 5 (DLGAP5), and kinesin family member 11 (KIF11), were identified through WGCNA, LASSO, SVM-RFE, and RF algorithms. These hub genes correlated strongly with T cells, natural killer cells, and macrophage cells as indicated by immune cell infiltration analysis. Conclusion RRM2, DLGAP5, and KIF11 could serve as potential diagnostic indicators and treatment targets for RA. The infiltration of immune cells offers additional insights into the underlying mechanisms involved in the progression of RA.
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Affiliation(s)
- Ying-Kai Wu
- Department of Orthopaedic, The Second Affiliated Hospital of Soochow University, Jiangsu, China
- Department of Orthopaedics, Ningyang County First People’s Hospital, Tai an, China
| | - Cai-De Liu
- Department of General Practice, Affiliated Hospital of Weifang Medical University, Wei Fang, China
| | - Chao Liu
- Gynecology and Obstetrics, Ningyang County Maternal and Child Health Hospital, Tai an, China
| | - Jun Wu
- Medical Cosmetology and Plastic Surgery Center, LinYi People’s Hospital, Lin Yi, China
| | - Zong-Gang Xie
- Department of Orthopaedic, The Second Affiliated Hospital of Soochow University, Jiangsu, China
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Xu J, Abdulsalam Khaleel R, Zaidan HK, Faisal Mutee A, Fahmi Fawy K, Gehlot A, Abbas AH, Arias Gonzáles JL, Amin AH, Ruiz-Balvin MC, Imannezhad S, Bahrami A, Akhavan-Sigari R. Discovery of common molecular signatures and drug repurposing for COVID-19/Asthma comorbidity: ACE2 and multi-partite networks. Cell Cycle 2024:1-30. [PMID: 38640424 DOI: 10.1080/15384101.2024.2340859] [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: 06/27/2023] [Accepted: 04/04/2024] [Indexed: 04/21/2024] Open
Abstract
Angiotensin-converting enzyme 2 (ACE2) is identified as the functional receptor for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of the ongoing global coronavirus disease-2019 (COVID-19) pandemic. This study aimed to elucidate potential therapeutic avenues by scrutinizing approved drugs through the identification of the genetic signature associated with SARS-CoV-2 infection in individuals with asthma. This exploration was conducted through an integrated analysis, encompassing interaction networks between the ACE2 receptor and common host (co-host) factors implicated in COVID-19/asthma comorbidity. The comprehensive analysis involved the identification of common differentially expressed genes (cDEGs) and hub-cDEGs, functional annotations, interaction networks, gene set variation analysis (GSVA), gene set enrichment analysis (GSEA), and module construction. Interaction networks were used to identify overlapping disease modules and potential drug targets. Computational biology and molecular docking analyzes were utilized to discern functional drug modules. Subsequently, the impact of the identified drugs on the expression of hub-cDEGs was experimentally validated using a mouse model. A total of 153 cDEGs or co-host factors associated with ACE2 were identified in the COVID-19 and asthma comorbidity. Among these, seven significant cDEGs and proteins - namely, HRAS, IFNG, JUN, CDH1, TLR4, ICAM1, and SCD-were recognized as pivotal host factors linked to ACE2. Regulatory network analysis of hub-cDEGs revealed eight top-ranked transcription factors (TFs) proteins and nine microRNAs as key regulatory factors operating at the transcriptional and post-transcriptional levels, respectively. Molecular docking simulations led to the proposal of 10 top-ranked repurposable drug molecules (Rapamycin, Ivermectin, Everolimus, Quercetin, Estradiol, Entrectinib, Nilotinib, Conivaptan, Radotinib, and Venetoclax) as potential treatment options for COVID-19 in individuals with comorbid asthma. Validation analysis demonstrated that Rapamycin effectively inhibited ICAM1 expression in the HDM-stimulated mice group (p < 0.01). This study unveils the common pathogenesis and genetic signature underlying asthma and SARS-CoV-2 infection, delineated by the interaction networks of ACE2-related host factors. These findings provide valuable insights for the design and discovery of drugs aimed at more effective therapeutics within the context of lung disease comorbidities.
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Affiliation(s)
- Jiajun Xu
- College of Veterinary & Life Sciences, the University of Glasgow, Glasgow, UK
| | | | | | | | - Khaled Fahmi Fawy
- Department of Chemistry, Faculty of Science, King Khalid University, Abha, Saudi Arabia
| | - Anita Gehlot
- Uttaranchal Institute of Technology, Uttaranchal University, Dehradun, India
| | | | - José Luis Arias Gonzáles
- Department of Social Sciences, Faculty of Social Studies, University of British Columbia, Vancouver, Canada
| | - Ali H Amin
- Zoology Department, Faculty of Science, Mansoura University, Mansoura, Egypt
| | | | - Shima Imannezhad
- Department of Pediatrics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Abolfazl Bahrami
- Biomedical Center for Systems Biology Science Munich, Ludwig-Maximilians-University, Munich, Germany
- Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
| | - Reza Akhavan-Sigari
- Department of Neurosurgery, University Medical Center Tuebingen, Tuebingen, Germany
- Department of Health Care Management and Clinical Research, Collegium Humanum, Warsaw, Poland
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Song P, Yakufujiang Y, Zhou J, Gu S, Wang W, Huo Z. Identification of important genes related to anoikis in acute myocardial infarction. J Cell Mol Med 2024; 28:e18264. [PMID: 38526027 PMCID: PMC10962123 DOI: 10.1111/jcmm.18264] [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: 09/20/2023] [Revised: 03/05/2024] [Accepted: 03/11/2024] [Indexed: 03/26/2024] Open
Abstract
Acute myocardial infarction (AMI) increasingly precipitates severe heart failure, with diagnoses now extending to progressively younger demographics. The focus of this study was to pinpoint critical genes linked to both AMI and anoikis, thereby unveiling potential novel biomarkers for AMI detection and intervention. Differential analysis was performed to identify significant differences in expression, and gene functionality was explored. Weighted gene coexpression network analysis (WGCNA) was used to construct gene coexpression networks. Immunoinfiltration analysis quantified immune cell abundance. Protein-protein interaction (PPI) analysis identified the proteins that interact with theanoikis. MCODE identified key functional modules. Drug enrichment analysis identified relevant compounds explored in the DsigDB. Through WGCNA, 13 key genes associated with anoikis and differentially expressed genes were identified. GO and KEGG pathway enrichment revealed the regulation of apoptotic signalling pathways and negative regulation of anoikis. PPI network analysis was also conducted, and 10 hub genes, such as IL1B, ZAP70, LCK, FASLG, CD4, LRP1, CDH2, MERTK, APOE and VTN were identified. IL1B were correlated with macrophages, mast cells, neutrophils and Tcells in MI, and the most common predicted medications were roxithromycin, NSC267099 and alsterpaullone. This study identified key genes associated with AMI and anoikis, highlighting their role in immune infiltration, diagnosis and medication prediction. These findings provide valuable insights into potential biomarkers and therapeutic targets for AMI.
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Affiliation(s)
- Puwei Song
- Department of Thoracic‐Cardiovascular Surgery, Shanghai Tongji Hospital, School of MedicineTongji UniversityShanghaiChina
| | - Yasen Yakufujiang
- Department of Thoracic‐Cardiovascular Surgery, Shanghai Tongji Hospital, School of MedicineTongji UniversityShanghaiChina
| | - Jianghui Zhou
- Department of Thoracic‐Cardiovascular Surgery, Shanghai Tongji Hospital, School of MedicineTongji UniversityShanghaiChina
| | - Shaorui Gu
- Department of Thoracic‐Cardiovascular Surgery, Shanghai Tongji Hospital, School of MedicineTongji UniversityShanghaiChina
| | - Wenli Wang
- Department of Thoracic‐Cardiovascular Surgery, Shanghai Tongji Hospital, School of MedicineTongji UniversityShanghaiChina
| | - Zhengyuan Huo
- Department of Thoracic‐Cardiovascular Surgery, Shanghai Tongji Hospital, School of MedicineTongji UniversityShanghaiChina
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Ma Y, Zhou Y, Jiang D, Dai W, Li J, Deng C, Chen C, Zheng G, Zhang Y, Qiu F, Sun H, Xing S, Han H, Qu J, Wu N, Yao Y, Su J. Integration of human organoids single-cell transcriptomic profiles and human genetics repurposes critical cell type-specific drug targets for severe COVID-19. Cell Prolif 2024; 57:e13558. [PMID: 37807299 PMCID: PMC10905359 DOI: 10.1111/cpr.13558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 08/31/2023] [Accepted: 09/18/2023] [Indexed: 10/10/2023] Open
Abstract
Human organoids recapitulate the cell type diversity and function of their primary organs holding tremendous potentials for basic and translational research. Advances in single-cell RNA sequencing (scRNA-seq) technology and genome-wide association study (GWAS) have accelerated the biological and therapeutic interpretation of trait-relevant cell types or states. Here, we constructed a computational framework to integrate atlas-level organoid scRNA-seq data, GWAS summary statistics, expression quantitative trait loci, and gene-drug interaction data for distinguishing critical cell populations and drug targets relevant to coronavirus disease 2019 (COVID-19) severity. We found that 39 cell types across eight kinds of organoids were significantly associated with COVID-19 outcomes. Notably, subset of lung mesenchymal stem cells increased proximity with fibroblasts predisposed to repair COVID-19-damaged lung tissue. Brain endothelial cell subset exhibited significant associations with severe COVID-19, and this cell subset showed a notable increase in cell-to-cell interactions with other brain cell types, including microglia. We repurposed 33 druggable genes, including IFNAR2, TYK2, and VIPR2, and their interacting drugs for COVID-19 in a cell-type-specific manner. Overall, our results showcase that host genetic determinants have cellular-specific contribution to COVID-19 severity, and identification of cell type-specific drug targets may facilitate to develop effective therapeutics for treating severe COVID-19 and its complications.
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Affiliation(s)
- Yunlong Ma
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Department of Biomedical Informatics, Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, China
- Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Zhejiang, China
| | - Yijun Zhou
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Department of Biomedical Informatics, Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, China
| | - Dingping Jiang
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Zhejiang, China
| | - Wei Dai
- Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, China
| | - Jingjing Li
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Department of Biomedical Informatics, Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, China
| | - Chunyu Deng
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Cheng Chen
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Department of Biomedical Informatics, Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, China
| | - Gongwei Zheng
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Department of Biomedical Informatics, Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, China
| | - Yaru Zhang
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Department of Biomedical Informatics, Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, China
- Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Zhejiang, China
| | - Fei Qiu
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Department of Biomedical Informatics, Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, China
| | - Haojun Sun
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Department of Biomedical Informatics, Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, China
| | - Shilai Xing
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Department of Biomedical Informatics, Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, China
| | - Haijun Han
- School of Medicine, Hangzhou City University, Hangzhou, China
| | - Jia Qu
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
| | - Nan Wu
- Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Key Laboratory of Big Data for Spinal Deformities, Department of Orthopedic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Yinghao Yao
- Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Zhejiang, China
| | - Jianzhong Su
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Department of Biomedical Informatics, Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, China
- Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Zhejiang, China
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Mao F, Wang E, Fu L, Fan W, Zhou J, Yan G, Liu T, Li Y. Identification of pyroptosis-related gene signature in nonalcoholic steatohepatitis. Sci Rep 2024; 14:3175. [PMID: 38326642 PMCID: PMC10850360 DOI: 10.1038/s41598-024-53599-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 02/02/2024] [Indexed: 02/09/2024] Open
Abstract
Non-alcoholic fatty liver disease (NAFLD) has emerged as one of the major causes of liver-related morbidity and mortality globally. It ranges from simple steatosis to non-alcoholic steatohepatitis (NASH) characterized by ballooning and hepatic inflammation. In the past few years, pyroptosis has been shown as a type of programmed cell death that triggers inflammation and plays a role in the development of NASH. However, the roles of pyroptosis-related genes (PRGs) in NASH remained unclear. In this study, we studied the expression level of pyroptosis-related genes (PRGs) in NASH and healthy controls, developed a diagnostic model of NASH based on PRGs and explored the pathological mechanisms associated with pyroptosis. We further compared immune status between NASH and healthy controls, analyzed immune status in different subtypes of NASH. We identified altogether twenty PRGs that were differentially expressed between NASH and normal liver tissues. Then, a novel diagnostic model consisting of seven PRGs including CASP3, ELANE, GZMA, CASP4, CASP9, IL6 and TP63 for NASH was constructed with an area under the ROC curve (AUC) of 0.978 (CI 0.965-0.99). Obvious variations in immune status between healthy controls and NASH cases were detected. Subsequently, the consensus clustering method based on differentially expressed PRGs was constructed to divide all NASH cases into two distinct pyroptosis subtypes with different immune and biological characteristics. Pyroptosis-related genes may play an important role in NASH and can provide new insights into the diagnosis and underlying mechanisms of NASH.
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Affiliation(s)
- Fei Mao
- Ministry of Education Key Laboratory of Metabolism and Molecular Medicine, Department of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - E Wang
- Department of Laboratory Animal Science, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025, China
| | - Li Fu
- Department of Laboratory Animal Science, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025, China
| | - Wenhua Fan
- Department of Laboratory Animal Science, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025, China
| | - Jing Zhou
- Department of Laboratory Animal Science, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025, China
| | - Guofeng Yan
- Department of Laboratory Animal Science, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025, China
| | - Tiemin Liu
- Human Phenome Institute, Fudan University, Shanghai, 200032, China.
- Key Laboratory of Genetic Engineering, School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
- Shanghai Key Laboratory of Metabolic Remodeling and Health, Institute of Metabolism and Integrative Biology, Fudan University, Shanghai, 200438, China.
| | - Yao Li
- Department of Laboratory Animal Science, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025, China.
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Qian W, Yang Z. Identification of cell-type-specific genes in multimodal single-cell data using deep neural network algorithm. Comput Biol Med 2023; 166:107498. [PMID: 37738895 DOI: 10.1016/j.compbiomed.2023.107498] [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: 03/25/2023] [Revised: 08/15/2023] [Accepted: 09/15/2023] [Indexed: 09/24/2023]
Abstract
The emergence of single-cell RNA sequencing (scRNA-seq) technology makes it possible to measure DNA, RNA, and protein in a single cell. Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq) is a powerful multimodal single-cell research innovation, allowing researchers to capture RNA and surface protein expression on the same cells. Currently, identification of cell-type-specific genes in CITE-seq data is still challenging. In this study, we obtained a set of CITE-seq datasets from Kaggle database, which included the sequencing dataset of seven cell types during bone marrow stem cell differentiation. We used Student's t-test to analyze these transcription RNAs and pick out 133 significantly differentially expressed genes (DEGs) among all cell types. Functional enrichment revealed that these DEGs were strongly associated with blood-related diseases, providing important insights into the cellular heterogeneity within bone marrow stem cells. The relation between RNA and protein levels was performed by deep neural network (DNN) model and achieved a high prediction score of 0.867. Based on their coefficients in the DNN model, three genes (LGALS1, CENPV, TRIM24) were identified as cell-type-specific genes in erythrocyte progenitor. Our works provide a novel perspective regarding the differentiation of stem cells in the bone marrow and provide valuable insights for further research in this field.
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Affiliation(s)
- Weiye Qian
- School of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou, PR China
| | - Zhiyuan Yang
- School of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou, PR China.
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7
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Huang T, Jiang N, Song Y, Pan H, Du A, Yu B, Li X, He J, Yuan K, Wang Z. Bioinformatics and system biology approach to identify the influences of SARS-CoV-2 on metabolic unhealthy obese patients. Front Mol Biosci 2023; 10:1274463. [PMID: 37877121 PMCID: PMC10591333 DOI: 10.3389/fmolb.2023.1274463] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 09/25/2023] [Indexed: 10/26/2023] Open
Abstract
Introduction: The severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) has posed a significant challenge to individuals' health. Increasing evidence shows that patients with metabolic unhealthy obesity (MUO) and COVID-19 have severer complications and higher mortality rate. However, the molecular mechanisms underlying the association between MUO and COVID-19 are poorly understood. Methods: We sought to reveal the relationship between MUO and COVID-19 using bioinformatics and systems biology analysis approaches. Here, two datasets (GSE196822 and GSE152991) were employed to extract differentially expressed genes (DEGs) to identify common hub genes, shared pathways, transcriptional regulatory networks, gene-disease relationship and candidate drugs. Results: Based on the identified 65 common DEGs, the complement-related pathways and neutrophil degranulation-related functions are found to be mainly affected. The hub genes, which included SPI1, CD163, C1QB, SIGLEC1, C1QA, ITGAM, CD14, FCGR1A, VSIG4 and C1QC, were identified. From the interaction network analysis, 65 transcription factors (TFs) were found to be the regulatory signals. Some infections, inflammation and liver diseases were found to be most coordinated with the hub genes. Importantly, Paricalcitol, 3,3',4,4',5-Pentachlorobiphenyl, PD 98059, Medroxyprogesterone acetate, Dexamethasone and Tretinoin HL60 UP have shown possibility as therapeutic agents against COVID-19 and MUO. Conclusion: This study provides new clues and references to treat both COVID-19 and MUO.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Kefei Yuan
- Division of Liver Surgery, Department of General Surgery and Laboratory of Liver Surgery, and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Zhen Wang
- Division of Liver Surgery, Department of General Surgery and Laboratory of Liver Surgery, and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
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Tian Y, Wang Z, Liang F, Wang Y. Identifying Immune Cell Infiltration and Hub Genes During the Myocardial Remodeling Process After Myocardial Infarction. J Inflamm Res 2023; 16:2893-2906. [PMID: 37456781 PMCID: PMC10349602 DOI: 10.2147/jir.s416914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 06/28/2023] [Indexed: 07/18/2023] Open
Abstract
Purpose Myocardial remodeling after myocardial infarction (MI) is a complex repair process following myocardial injury, characterized by the infiltration of multiple types of immune cells. However, the underlying molecular mechanism of myocardial remodeling after MI remains obscure. This study aimed to identify the hub differential expression genes (DEGs) of myocardial remodeling after MI and determine the distribution of immune cells infiltrating the pathology. Methods We downloaded GSE132143, GSE151834, and GSE176092 data from the GEO database. The GSE132143 dataset was used to identify DEGs, perform functional annotation, and screen hub genes based on protein-protein interaction (PPI) analysis. The GSE151834 dataset was used to validate the expression of hub genes. CIBERSORTx analysis was performed to explore the immune microenvironment in myocardial remodeling after MI. After conducting a literature review, we selected P3H3 to confirm the expression by utilizing immunohistochemistry and qRT-PCR. Finally, the snRNA-seq data in dataset GSE176092 was used for clarifying the expression of these hub genes in various cell clusters. Results We found 975 DEGs in myocardial remodeling after MI. Four hub genes (P3H3, COL15A1, COL16A1, COL27A1) were identified and were verified in the GSE151834 dataset. According to immune infiltration analysis, CD4+ naive T cells, regulatory T cells, monocytes, M2 macrophages, and neutrophils were involved in the pathological process of myocardial remodeling after MI. Additionally, in vitro experiments verified that P3h3 expression was significantly elevated in myocardial remodeling after MI. The snRNA-seq data analyzed that P3h3, Col15a1, Col16a1, and Col27a1 were highly expressed in fibroblasts of post-MI. Conclusion This study identified four hub genes P3H3, COL15A1, COL16A1, and COL27A1, particularly P3H3, as potential targets for targeted therapy in MI patients.
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Affiliation(s)
- Yuan Tian
- Department of Cardiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, People’s Republic of China
| | - Zilin Wang
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, People’s Republic of China
| | - Feng Liang
- Heart Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, People’s Republic of China
| | - Yi Wang
- Department of Cardiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, People’s Republic of China
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Jin B, Cheng X, Fei G, Sang S, Zhong C. Identification of diagnostic biomarkers in Alzheimer's disease by integrated bioinformatic analysis and machine learning strategies. Front Aging Neurosci 2023; 15:1169620. [PMID: 37434738 PMCID: PMC10331604 DOI: 10.3389/fnagi.2023.1169620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Accepted: 06/08/2023] [Indexed: 07/13/2023] Open
Abstract
Background Alzheimer's disease (AD) is the most prevalent form of dementia, and is becoming one of the most burdening and lethal diseases. More useful biomarkers for diagnosing AD and reflecting the disease progression are in need and of significance. Methods The integrated bioinformatic analysis combined with machine-learning strategies was applied for exploring crucial functional pathways and identifying diagnostic biomarkers of AD. Four datasets (GSE5281, GSE131617, GSE48350, and GSE84422) with samples of AD frontal cortex are integrated as experimental datasets, and another two datasets (GSE33000 and GSE44772) with samples of AD frontal cortex were used to perform validation analyses. Functional Correlation enrichment analyses were conducted based on Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and the Reactome database to reveal AD-associated biological functions and key pathways. Four models were employed to screen the potential diagnostic biomarkers, including one bioinformatic analysis of Weighted gene co-expression network analysis (WGCNA)and three machine-learning algorithms: Least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF) analysis. The correlation analysis was performed to explore the correlation between the identified biomarkers with CDR scores and Braak staging. Results The pathways of the immune response and oxidative stress were identified as playing a crucial role during AD. Thioredoxin interacting protein (TXNIP), early growth response 1 (EGR1), and insulin-like growth factor binding protein 5 (IGFBP5) were screened as diagnostic markers of AD. The diagnostic efficacy of TXNIP, EGR1, and IGFBP5 was validated with corresponding AUCs of 0.857, 0.888, and 0.856 in dataset GSE33000, 0.867, 0.909, and 0.841 in dataset GSE44770. And the AUCs of the combination of these three biomarkers as a diagnostic tool for AD were 0.954 and 0.938 in the two verification datasets. Conclusion The pathways of immune response and oxidative stress can play a crucial role in the pathogenesis of AD. TXNIP, EGR1, and IGFBP5 are useful biomarkers for diagnosing AD and their mRNA level may reflect the development of the disease by correlation with the CDR scores and Breaking staging.
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Affiliation(s)
- Boru Jin
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China
- Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China
| | - Xiaoqin Cheng
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China
- Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China
| | - Guoqiang Fei
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China
- Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China
| | - Shaoming Sang
- Shanghai Raising Pharmaceutical Technology Co., Ltd.Shanghai, China
| | - Chunjiu Zhong
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China
- Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China
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Zhou H, Xu M, Hu P, Li Y, Ren C, Li M, Pan Y, Wang S, Liu X. Identifying hub genes and common biological pathways between COVID-19 and benign prostatic hyperplasia by machine learning algorithms. Front Immunol 2023; 14:1172724. [PMID: 37426635 PMCID: PMC10328422 DOI: 10.3389/fimmu.2023.1172724] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 06/07/2023] [Indexed: 07/11/2023] Open
Abstract
Background COVID-19, a serious respiratory disease that has the potential to affect numerous organs, is a serious threat to the health of people around the world. The objective of this article is to investigate the potential biological targets and mechanisms by which SARS-CoV-2 affects benign prostatic hyperplasia (BPH) and related symptoms. Methods We downloaded the COVID-19 datasets (GSE157103 and GSE166253) and the BPH datasets (GSE7307 and GSE132714) from the Gene Expression Omnibus (GEO) database. In GSE157103 and GSE7307, differentially expressed genes (DEGs) were found using the "Limma" package, and the intersection was utilized to obtain common DEGs. Further analyses followed, including those using Protein-Protein Interaction (PPI), Gene Ontology (GO) function enrichment analysis, and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Potential hub genes were screened using three machine learning methods, and they were later verified using GSE132714 and GSE166253. The CIBERSORT analysis and the identification of transcription factors, miRNAs, and drugs as candidates were among the subsequent analyses. Results We identified 97 common DEGs from GSE157103 and GSE7307. According to the GO and KEGG analyses, the primary gene enrichment pathways were immune-related pathways. Machine learning methods were used to identify five hub genes (BIRC5, DNAJC4, DTL, LILRB2, and NDC80). They had good diagnostic properties in the training sets and were validated in the validation sets. According to CIBERSORT analysis, hub genes were closely related to CD4 memory activated of T cells, T cells regulatory and NK cells activated. The top 10 drug candidates (lucanthone, phytoestrogens, etoposide, dasatinib, piroxicam, pyrvinium, rapamycin, niclosamide, genistein, and testosterone) will also be evaluated by the P value, which is expected to be helpful for the treatment of COVID-19-infected patients with BPH. Conclusion Our findings reveal common signaling pathways, possible biological targets, and promising small molecule drugs for BPH and COVID-19. This is crucial to understand the potential common pathogenic and susceptibility pathways between them.
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Affiliation(s)
- Hang Zhou
- Department of Urology, Tianjin Medical University General Hospital, Tianjin, China
| | - Mingming Xu
- Department of Urology, Tianjin Medical University General Hospital, Tianjin, China
| | - Ping Hu
- Department of Orthopedics, Tianjin Medical University General Hospital, Tianjin, China
| | - Yuezheng Li
- Department of Urology, Tianjin Medical University General Hospital, Tianjin, China
| | - Congzhe Ren
- Department of Urology, Tianjin Medical University General Hospital, Tianjin, China
| | - Muwei Li
- Department of Urology, Tianjin Medical University General Hospital, Tianjin, China
| | - Yang Pan
- Department of Urology, Tianjin Medical University General Hospital, Tianjin, China
| | - Shangren Wang
- Department of Urology, Tianjin Medical University General Hospital, Tianjin, China
| | - Xiaoqiang Liu
- Department of Urology, Tianjin Medical University General Hospital, Tianjin, China
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11
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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.
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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.
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12
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He H, Zhou Y, Liu L, Cui J, Pei Y, Cao J, Hao X, Guo L, Wang H, Liu H. Bioinformatics analysis reveals lipid metabolism may play an important role in the SiO 2-stimulated rat model. Cell Signal 2023:110716. [PMID: 37224986 DOI: 10.1016/j.cellsig.2023.110716] [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: 03/13/2023] [Revised: 04/16/2023] [Accepted: 05/12/2023] [Indexed: 05/26/2023]
Abstract
Silicosis is a progressive and irreversible common occupational disease caused by long-term inhalation of a large amount of free silica dust. Its pathogenesis is complex, and the existing prevention and treatment methods can not effectively improve silicosis injury. To uncover potential differential genes in silicosis, SiO2-stimulated rats and their control original transcriptomic data sets GSE49144, GSE32147 and GSE30178 were downloaded for further bioinformatics analysis. We used R packages to extract and standardize transcriptome profiles, then screened differential genes, and enriched GO and KEGG pathways through clusterProfiler packages. In addition, we investigated the role of lipid metabolism in the progression of silicosis by qRT-PCR validation and transfection with si-CD36. A total of 426 differential genes were identified in this study. Based on GO and KEGG enrichment analysis, it was found that lipid and atherosclerosis were significantly enriched. qRT-PCR was used to detect the relative expression level of differential genes in this signaling pathway of silicosis rat models. mRNA levels of Abcg1, Il1b, Sod2, Cyba, Cd14, Cxcl2, Ccl3, Cxcl1, Ccl2 and CD36 increased, mRNA levels of Ccl5, Cybb and Il18 decreased. In addition, at the cellular level, SiO2-stimulated lead to lipid metabolism disorder in NR8383, and silencing CD36 inhibited SiO2-induced lipid metabolism disorder. These results indicate that lipid metabolism plays an important role in the progression of silicosis, and the genes and pathways reported in this study may provide new ideas for the pathogenesis of silicosis.
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Affiliation(s)
- Hailan He
- School of Public Health, North China University of Science and Technology, Tangshan, Hebei 063210, China
| | - Yuhui Zhou
- School of Public Health, North China University of Science and Technology, Tangshan, Hebei 063210, China
| | - Lekai Liu
- School of Public Health, North China University of Science and Technology, Tangshan, Hebei 063210, China
| | - Jie Cui
- School of Public Health, North China University of Science and Technology, Tangshan, Hebei 063210, China
| | - Yongchao Pei
- School of Clinical Medicine, North China University of Science and Technology, Tangshan, Hebei 063210, China
| | - Jiahui Cao
- School of Public Health, North China University of Science and Technology, Tangshan, Hebei 063210, China
| | - Xiaohui Hao
- School of Public Health, North China University of Science and Technology, Tangshan, Hebei 063210, China; Hebei Key Laboratory of Organ Fibrosis, North China University of Science and Technology, Tangshan, Hebei 063210, China
| | - Lingli Guo
- School of Public Health, North China University of Science and Technology, Tangshan, Hebei 063210, China; Hebei Key Laboratory of Organ Fibrosis, North China University of Science and Technology, Tangshan, Hebei 063210, China
| | - Hongli Wang
- School of Public Health, North China University of Science and Technology, Tangshan, Hebei 063210, China; Hebei Key Laboratory of Organ Fibrosis, North China University of Science and Technology, Tangshan, Hebei 063210, China.
| | - Heliang Liu
- School of Public Health, North China University of Science and Technology, Tangshan, Hebei 063210, China; Hebei Key Laboratory of Organ Fibrosis, North China University of Science and Technology, Tangshan, Hebei 063210, China.
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Noor F, Ashfaq UA, Bakar A, ul Haq W, Allemailem KS, Alharbi BF, Al-Megrin WAI, Tahir ul Qamar M. Discovering common pathogenic processes between COVID-19 and HFRS by integrating RNA-seq differential expression analysis with machine learning. Front Microbiol 2023; 14:1175844. [PMID: 37234545 PMCID: PMC10208410 DOI: 10.3389/fmicb.2023.1175844] [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: 02/28/2023] [Accepted: 03/29/2023] [Indexed: 05/28/2023] Open
Abstract
Zoonotic virus spillover in human hosts including outbreaks of Hantavirus and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) imposes a serious impact on the quality of life of patients. Recent studies provide a shred of evidence that patients with Hantavirus-caused hemorrhagic fever with renal syndrome (HFRS) are at risk of contracting SARS-CoV-2. Both RNA viruses shared a higher degree of clinical features similarity including dry cough, high fever, shortness of breath, and certain reported cases with multiple organ failure. However, there is currently no validated treatment option to tackle this global concern. This study is attributed to the identification of common genes and perturbed pathways by combining differential expression analysis with bioinformatics and machine learning approaches. Initially, the transcriptomic data of hantavirus-infected peripheral blood mononuclear cells (PBMCs) and SARS-CoV-2 infected PBMCs were analyzed through differential gene expression analysis for identification of common differentially expressed genes (DEGs). The functional annotation by enrichment analysis of common genes demonstrated immune and inflammatory response biological processes enriched by DEGs. The protein-protein interaction (PPI) network of DEGs was then constructed and six genes named RAD51, ALDH1A1, UBA52, CUL3, GADD45B, and CDKN1A were identified as the commonly dysregulated hub genes among HFRS and COVID-19. Later, the classification performance of these hub genes were evaluated using Random Forest (RF), Poisson Linear Discriminant Analysis (PLDA), Voom-based Nearest Shrunken Centroids (voomNSC), and Support Vector Machine (SVM) classifiers which demonstrated accuracy >70%, suggesting the biomarker potential of the hub genes. To our knowledge, this is the first study that unveiled biological processes and pathways commonly dysregulated in HFRS and COVID-19, which could be in the next future used for the design of personalized treatment to prevent the linked attacks of COVID-19 and HFRS.
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Affiliation(s)
- Fatima Noor
- Integrative Omics and Molecular Modeling Laboratory, Department of Bioinformatics and Biotechnology, Government College University, Faisalabad, Pakistan
| | - Usman Ali Ashfaq
- Integrative Omics and Molecular Modeling Laboratory, Department of Bioinformatics and Biotechnology, Government College University, Faisalabad, Pakistan
| | - Abu Bakar
- Centre of Agricultural Biochemistry and Biotechnology (CABB), University of Agriculture, Faisalabad, Pakistan
| | - Waqar ul Haq
- Centre of Agricultural Biochemistry and Biotechnology (CABB), University of Agriculture, Faisalabad, Pakistan
| | - Khaled S. Allemailem
- Department of Medical Laboratories, College of Applied Medical Sciences, Qassim University, Buraydah, Saudi Arabia
| | - Basmah F. Alharbi
- Department of Basic Health Science, College of Applied Medical Sciences, Qassim University, Buraydah, Saudi Arabia
| | - Wafa Abdullah I. Al-Megrin
- Department of Biology, College of Science, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Muhammad Tahir ul Qamar
- Integrative Omics and Molecular Modeling Laboratory, Department of Bioinformatics and Biotechnology, Government College University, Faisalabad, Pakistan
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14
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Yang S, Yi L, Xia X, Chen X, Hou X, Zhang L, Yang F, Liao J, Han Z, Fu Y. Transcriptome comparative analysis of amygdala-hippocampus in depression: A rat model induced by chronic unpredictable mild stress (CUMS). J Affect Disord 2023; 334:258-270. [PMID: 37105469 DOI: 10.1016/j.jad.2023.04.074] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 04/11/2023] [Accepted: 04/16/2023] [Indexed: 04/29/2023]
Abstract
BACKGROUND Depression is a common and complex mental disease, and its pathogenesis involves several brain regions. Abnormalities in the amygdala-hippocampal neural circuits have been shown to be involved in depression. However, the underlying molecular mechanisms remain unclear. METHODS A rat model was used to determine the transcriptome changes in the amygdala-hippocampal neural network under chronic unpredictable mild stress (CUMS). Depression-related modules in this neural network were identified using weighted gene co-expression network analysis (WGCNA). Difference and enrichment analyses were used to determine differential gene expression in the two brain regions. RESULTS The modules in the amygdala and hippocampus associated with depression-like behavior contained 363 and 225 genes, respectively. Forty-two differentially expressed genes were identified in the amygdala candidate module and 37 in the hippocampus. Enrichment analysis showed that candidate genes in the amygdala were associated with neuronal myelination and candidate genes in the hippocampus were associated with synaptic transmission. Finally, based on module hub gene statistics, differential gene expression, and protein-protein interaction networks, 11 central genes were found in the amygdala candidate module, and one central gene was found in the hippocampal module. LIMITATIONS Our study was based on a rat CUMS model. Further evidence is needed to prove that our results are applicable to patients with depression. CONCLUSION This study identified critical modules and central genes involved in the amygdala-hippocampal circuit in the context of depression, and may provide further understanding of the pathogenesis of depression and help identify potential targets for antidepressant therapy.
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Affiliation(s)
- Shu Yang
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Li Yi
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Xiaodi Xia
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Xiaolu Chen
- The First Branch, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Xiao Hou
- Department of Clinical Medicine, Chongqing Medical and Pharmaceutical College, Chongqing 401331, China
| | - Longjie Zhang
- Department of Pharmacy, School of Pharmacy, Chongqing Medical University, Chongqing 400016, China
| | - Fang Yang
- Department of pharmacology, School of Pharmacy, Chongqing Medical University, Chongqing 400016, China
| | - Jiaxin Liao
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Zhijie Han
- Department of Bioinformatics, School of Basic Medicine, Chongqing Medical University, Chongqing 400016, China
| | - Yixiao Fu
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China.
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15
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Ullah MA, Alam S, Moin AT, Ahamed T, Shohael AM. Risk factors and actionable molecular signatures in COVID-19-associated lung adenocarcinoma and lung squamous cell carcinoma patients. Comput Biol Med 2023; 158:106855. [PMID: 37040675 PMCID: PMC10072980 DOI: 10.1016/j.compbiomed.2023.106855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 02/05/2023] [Accepted: 03/30/2023] [Indexed: 04/08/2023]
Abstract
The molecular mechanism of COVID-19's pathogenic effect on lung cancer patients is yet unknown. In this study, we used differential gene expression pattern analysis to try to figure out the possible disease mechanism of COVID-19 and its associated risk factors in patients with the two most common types of non-small-cell lung cancer, lung adenocarcinoma and lung squamous cell carcinoma. We also used network-based approaches to identify potential diagnostic and molecular targets for COVID-19-infected lung cancer patients. Our study showed that lung cancer and COVID-19 patients share 36 genes that are expressed differently and in common. Most of these genes are expressed in lung tissues and are mostly involved in the pathogenesis of different respiratory tract diseases. Additionally, we also found that COVID-19 may affect the expression of several cancer-associated genes in lung cancer patients, such as the oncogenes JUN, TNC, and POU2AF1. Moreover, we also reported that COVID-19 may predispose lung cancer patients to other diseases like acute liver failure and respiratory distress syndrome. Also, our findings in concert with published literature suggest that molecular signatures like hsa-mir-93-5p, CCNB2, IRF1, CD163, and different immune cell-based approaches could help both diagnose and treat this group of patients. Overall, the scientific results of this research will aid in the formulation of suitable management strategies as well as the development of diagnostic and therapeutic methods for COVID-19-infected lung cancer patients.
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Affiliation(s)
- Md Asad Ullah
- Department of Biotechnology and Genetic Engineering, Faculty of Biological Sciences, Jahangirnagar University, Dhaka, Bangladesh
| | - Sayka Alam
- Department of Biotechnology and Genetic Engineering, Faculty of Biological Sciences, Jahangirnagar University, Dhaka, Bangladesh
| | - Abu Tayab Moin
- Department of Genetic Engineering and Biotechnology, Faculty of Biological Sciences, University of Chittagong, Chattogram, Bangladesh
| | - Tanvir Ahamed
- Department of Biotechnology and Genetic Engineering, Faculty of Biological Sciences, Jahangirnagar University, Dhaka, Bangladesh
| | - Abdullah Mohammad Shohael
- Department of Biotechnology and Genetic Engineering, Faculty of Biological Sciences, Jahangirnagar University, Dhaka, Bangladesh.
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16
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Lin Y, Li Y, Chen H, Meng J, Li J, Chu J, Zheng R, Wang H, Pan P, Su J, Jiang J, Ye L, Liang H, An S. Weighted gene co-expression network analysis revealed T cell differentiation associated with the age-related phenotypes in COVID-19 patients. BMC Med Genomics 2023; 16:59. [PMID: 36966292 PMCID: PMC10039774 DOI: 10.1186/s12920-023-01490-2] [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: 10/19/2022] [Accepted: 03/15/2023] [Indexed: 03/27/2023] Open
Abstract
The risk of severe condition caused by Corona Virus Disease 2019 (COVID-19) increases with age. However, the underlying mechanisms have not been clearly understood. The dataset GSE157103 was used to perform weighted gene co-expression network analysis on 100 COVID-19 patients in our analysis. Through weighted gene co-expression network analysis, we identified a key module which was significantly related with age. This age-related module could predict Intensive Care Unit status and mechanical-ventilation usage, and enriched with positive regulation of T cell receptor signaling pathway biological progress. Moreover, 10 hub genes were identified as crucial gene of the age-related module. Protein-protein interaction network and transcription factors-gene interactions were established. Lastly, independent data sets and RT-qPCR were used to validate the key module and hub genes. Our conclusion revealed that key genes were associated with the age-related phenotypes in COVID-19 patients, and it would be beneficial for clinical doctors to develop reasonable therapeutic strategies in elderly COVID-19 patients.
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Affiliation(s)
- Yao Lin
- Medical Laboratory Centre, Life Sciences Institute, Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Yueqi Li
- Department of Biochemistry and Molecular Biology, School of Basic Medicine, Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Hubin Chen
- Medical Laboratory Centre, Life Sciences Institute, Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Jun Meng
- Medical Laboratory Centre, Life Sciences Institute, Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Jingyi Li
- Biosafety Level 3 Laboratory and Guangxi Collaborative Innovation Centre for Biomedicine, Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Jiemei Chu
- Medical Laboratory Centre, Life Sciences Institute, Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Ruili Zheng
- Biosafety Level 3 Laboratory and Guangxi Collaborative Innovation Centre for Biomedicine, Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Hailong Wang
- Biosafety Level 3 Laboratory and Guangxi Collaborative Innovation Centre for Biomedicine, Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Peijiang Pan
- Biosafety Level 3 Laboratory and Guangxi Collaborative Innovation Centre for Biomedicine, Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Jinming Su
- Biosafety Level 3 Laboratory and Guangxi Collaborative Innovation Centre for Biomedicine, Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Junjun Jiang
- Medical Laboratory Centre, Life Sciences Institute, Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Li Ye
- Biosafety Level 3 Laboratory and Guangxi Collaborative Innovation Centre for Biomedicine, Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Hao Liang
- Medical Laboratory Centre, Life Sciences Institute, Guangxi Medical University, Nanning, 530021, Guangxi, China
- Biosafety Level 3 Laboratory and Guangxi Collaborative Innovation Centre for Biomedicine, Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Sanqi An
- Medical Laboratory Centre, Life Sciences Institute, Guangxi Medical University, Nanning, 530021, Guangxi, China.
- Department of Biochemistry and Molecular Biology, School of Basic Medicine, Guangxi Medical University, Nanning, 530021, Guangxi, China.
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Sarker B, Rahaman MM, Islam MA, Alamin MH, Husain MM, Ferdousi F, Ahsan MA, Mollah MNH. Identification of host genomic biomarkers from multiple transcriptomics datasets for diagnosis and therapies of SARS-CoV-2 infections. PLoS One 2023; 18:e0281981. [PMID: 36913345 PMCID: PMC10010564 DOI: 10.1371/journal.pone.0281981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 02/05/2023] [Indexed: 03/14/2023] Open
Abstract
The pandemic of COVID-19 is a severe threat to human life and the global economy. Despite the success of vaccination efforts in reducing the spread of the virus, the situation remains largely uncontrolled due to the random mutation in the RNA sequence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which demands different variants of effective drugs. Disease-causing gene-mediated proteins are usually used as receptors to explore effective drug molecules. In this study, we analyzed two different RNA-Seq and one microarray gene expression profile datasets by integrating EdgeR, LIMMA, weighted gene co-expression network and robust rank aggregation approaches, which revealed SARS-CoV-2 infection causing eight hub-genes (HubGs) including HubGs; REL, AURKA, AURKB, FBXL3, OAS1, STAT4, MMP2 and IL6 as the host genomic biomarkers. Gene Ontology and pathway enrichment analyses of HubGs significantly enriched some crucial biological processes, molecular functions, cellular components and signaling pathways that are associated with the mechanisms of SARS-CoV-2 infections. Regulatory network analysis identified top-ranked 5 TFs (SRF, PBX1, MEIS1, ESR1 and MYC) and 5 miRNAs (hsa-miR-106b-5p, hsa-miR-20b-5p, hsa-miR-93-5p, hsa-miR-106a-5p and hsa-miR-20a-5p) as the key transcriptional and post-transcriptional regulators of HubGs. Then, we conducted a molecular docking analysis to determine potential drug candidates that could interact with HubGs-mediated receptors. This analysis resulted in the identification of top-ranked ten drug agents, including Nilotinib, Tegobuvir, Digoxin, Proscillaridin, Olysio, Simeprevir, Hesperidin, Oleanolic Acid, Naltrindole and Danoprevir. Finally, we investigated the binding stability of the top-ranked three drug molecules Nilotinib, Tegobuvir and Proscillaridin with the three top-ranked proposed receptors (AURKA, AURKB, OAS1) by using 100 ns MD-based MM-PBSA simulations and observed their stable performance. Therefore, the findings of this study might be useful resources for diagnosis and therapies of SARS-CoV-2 infections.
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Affiliation(s)
- Bandhan Sarker
- Faculty of Science, Department of Statistics, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj, Bangladesh
- Department of Statistics, Bioinformatics Laboratory (Dry), University of Rajshahi, Rajshahi, Bangladesh
| | - Md. Matiur Rahaman
- Faculty of Science, Department of Statistics, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj, Bangladesh
| | - Md. Ariful Islam
- Department of Statistics, Bioinformatics Laboratory (Dry), University of Rajshahi, Rajshahi, Bangladesh
| | - Muhammad Habibulla Alamin
- Faculty of Science, Department of Statistics, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj, Bangladesh
| | - Md. Maidul Husain
- Faculty of Science, Department of Statistics, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj, Bangladesh
| | - Farzana Ferdousi
- Faculty of Science, Department of Statistics, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj, Bangladesh
| | - Md. Asif Ahsan
- Department of Statistics, Bioinformatics Laboratory (Dry), University of Rajshahi, Rajshahi, Bangladesh
- Liangzhu Laboratory, Zhejiang University Medical Center, Zhejiang University, Hangzhou, Zhejiang, China
| | - Md. Nurul Haque Mollah
- Department of Statistics, Bioinformatics Laboratory (Dry), University of Rajshahi, Rajshahi, Bangladesh
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Cassiano LMG, Cavalcante-Silva V, Oliveira MS, Prado BVO, Cardoso CG, Salim ACM, Franco GR, D’Almeida V, Francisco SC, Coimbra RS. Vitamin B12 attenuates leukocyte inflammatory signature in COVID-19 via methyl-dependent changes in epigenetic markings. Front Immunol 2023; 14:1048790. [PMID: 36993968 PMCID: PMC10040807 DOI: 10.3389/fimmu.2023.1048790] [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: 09/21/2022] [Accepted: 02/27/2023] [Indexed: 03/16/2023] Open
Abstract
COVID-19 induces chromatin remodeling in host immune cells, and it had previously been shown that vitamin B12 downregulates some inflammatory genes via methyl-dependent epigenetic mechanisms. In this work, whole blood cultures from moderate or severe COVID-19 patients were used to assess the potential of B12 as adjuvant drug. The vitamin normalized the expression of a panel of inflammatory genes still dysregulated in the leukocytes despite glucocorticoid therapy during hospitalization. B12 also increased the flux of the sulfur amino acid pathway, that regulates the bioavailability of methyl. Accordingly, B12-induced downregulation of CCL3 strongly and negatively correlated with the hypermethylation of CpGs in its regulatory regions. Transcriptome analysis revealed that B12 attenuates the effects of COVID-19 on most inflammation-related pathways affected by the disease. As far as we are aware, this is the first study to demonstrate that pharmacological modulation of epigenetic markings in leukocytes favorably regulates central components of COVID-19 physiopathology.
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Affiliation(s)
- Larissa M. G. Cassiano
- Neurogenômica, Imunopatologia, Instituto René Rachou, Fiocruz, Belo Horizonte, Brazil
- Departamento de Bioquímica e Imunologia, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Vanessa Cavalcante-Silva
- Departamento de Psicobiologia, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, Brazil
| | - Marina S. Oliveira
- Neurogenômica, Imunopatologia, Instituto René Rachou, Fiocruz, Belo Horizonte, Brazil
| | | | | | - Anna C. M. Salim
- Plataforma de Sequenciamento NGS (Next Generation Sequencing), Instituto René Rachou, Fiocruz, Belo Horizonte, Brazil
| | - Gloria R. Franco
- Departamento de Bioquímica e Imunologia, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Vânia D’Almeida
- Departamento de Psicobiologia, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, Brazil
| | | | - Roney S. Coimbra
- Neurogenômica, Imunopatologia, Instituto René Rachou, Fiocruz, Belo Horizonte, Brazil
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19
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Hossain MA, Asa TA, Auwul MR, Aktaruzzaman M, Rahman MM, Moni MA. The pathogenetic influence of smoking on SARS-CoV-2 infection: Integrative transcriptome and regulomics analysis of lung epithelial cells. Comput Biol Med 2023; 159:106885. [PMID: 37084641 PMCID: PMC10065815 DOI: 10.1016/j.compbiomed.2023.106885] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Revised: 02/26/2023] [Accepted: 03/30/2023] [Indexed: 04/03/2023]
Abstract
Corona virus disease (COVID-19) has been emerged as pandemic infectious disease. The recent epidemiological data suggest that the smokers are more vulnerable to infection with COVID-19; however, the influence of smoking (SMK) on the COVID-19 infected patients and the mortality is not known yet. In this study, we aimed to discern the influence of SMK on COVID-19 infected patients utilizing the transcriptomics data of COVID-19 infected lung epithelial cells and transcriptomics data smoking matched with controls from lung epithelial cells. The bioinformatics based analysis revealed the molecular insights into the level of transcriptional changes and pathways which are important to identify the impact of smoking on COVID-19 infection and prevalence. We compared differentially expressed genes (DEGs) between COVID-19 and SMK and 59 DEGs were identified as consistently dysregulated at transcriptomics levels. The correlation network analyses were constructed for these common genes using WGCNA R package to see the relationship among these genes. Integration of DEGs with network analysis (protein-protein interaction) showed the presence of 9 hub proteins as key so called "candidate hub proteins" overlapped between COVID-19 patients and SMK. The Gene Ontology and pathways analysis demonstrated the enrichment of inflammatory pathway such as IL-17 signaling pathway, Interleukin-6 signaling, TNF signaling pathway and MAPK1/MAPK3 signaling pathways that might be the therapeutic targets in COVID-19 for smoking persons. The identified genes, pathways, hubs genes, and their regulators might be considered for establishment of key genes and drug targets for SMK and COVID-19.
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20
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A systematic review of artificial intelligence-based COVID-19 modeling on multimodal genetic information. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2023; 179:1-9. [PMID: 36809830 PMCID: PMC9938959 DOI: 10.1016/j.pbiomolbio.2023.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 02/07/2023] [Accepted: 02/12/2023] [Indexed: 02/21/2023]
Abstract
This study systematically reviews the Artificial Intelligence (AI) methods developed to resolve the critical process of COVID-19 gene data analysis, including diagnosis, prognosis, biomarker discovery, drug responsiveness, and vaccine efficacy. This systematic review follows the guidelines of Preferred Reporting for Systematic Reviews and Meta-Analyses (PRISMA). We searched PubMed, Embase, Web of Science, and Scopus databases to identify the relevant articles from January 2020 to June 2022. It includes the published studies of AI-based COVID-19 gene modeling extracted through relevant keyword searches in academic databases. This study included 48 articles discussing AI-based genetic studies for several objectives. Ten articles confer about the COVID-19 gene modeling with computational tools, and five articles evaluated ML-based diagnosis with observed accuracy of 97% on SARS-CoV-2 classification. Gene-based prognosis study reviewed three articles and found host biomarkers detecting COVID-19 progression with 90% accuracy. Twelve manuscripts reviewed the prediction models with various genome analysis studies, nine articles examined the gene-based in silico drug discovery, and another nine investigated the AI-based vaccine development models. This study compiled the novel coronavirus gene biomarkers and targeted drugs identified through ML approaches from published clinical studies. This review provided sufficient evidence to delineate the potential of AI in analyzing complex gene information for COVID-19 modeling on multiple aspects like diagnosis, drug discovery, and disease dynamics. AI models entrenched a substantial positive impact by enhancing the efficiency of the healthcare system during the COVID-19 pandemic.
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21
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Xiao Y, Yan Y, Chang L, Ji H, Sun H, Song S, Feng K, Nuermaimaiti A, Lu Z, Wang L. CDK4/6 inhibitor palbociclib promotes SARS-CoV-2 cell entry by down-regulating SKP2 dependent ACE2 degradation. Antiviral Res 2023; 212:105558. [PMID: 36806814 PMCID: PMC9938000 DOI: 10.1016/j.antiviral.2023.105558] [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: 09/28/2022] [Revised: 02/09/2023] [Accepted: 02/17/2023] [Indexed: 02/19/2023]
Abstract
Coronavirus disease 2019 (COVID-19) outbreak has become a global pandemic. CDK4/6 inhibitor palbociclib was reported to be one of the top-scored repurposed drugs to treat COVID-19. As the receptor for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) entry, expression level of angiotensin-converting enzyme 2 (ACE2) is closely related to SARS-CoV-2 infection. In this study, we demonstrated that palbociclib and other methods could arrest cells in G0/G1 phase and up-regulate ACE2 mRNA and protein levels without altering its subcellular localization. Palbociclib inhibited ubiquitin-proteasome and lysosomal degradation of ACE2 through down-regulating S-phase kinase-associated protein 2 (SKP2). In addition, increased ACE2 expression induced by palbociclib and other cell cycle arresting compounds facilitated pseudotyped SARS-CoV-2 infection. This study suggested that ACE2 expression was down-regulated in proliferating cells. Cell cycle arresting compounds could increase ACE2 expression and facilitate SARS-CoV-2 cell entry, which may not be suitable therapeutic agents for the treatment of SARS-CoV-2 infection.
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Affiliation(s)
- Yingzi Xiao
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital / National Center of Gerontology, Beijing, PR China; National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, PR China; Beijing Engineering Research Center of Laboratory Medicine, Beijing, PR China
| | - Ying Yan
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital / National Center of Gerontology, Beijing, PR China; Beijing Engineering Research Center of Laboratory Medicine, Beijing, PR China
| | - Le Chang
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital / National Center of Gerontology, Beijing, PR China; Beijing Engineering Research Center of Laboratory Medicine, Beijing, PR China
| | - Huimin Ji
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital / National Center of Gerontology, Beijing, PR China; Beijing Engineering Research Center of Laboratory Medicine, Beijing, PR China
| | - Huizhen Sun
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital / National Center of Gerontology, Beijing, PR China; National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, PR China; Beijing Engineering Research Center of Laboratory Medicine, Beijing, PR China
| | - Shi Song
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital / National Center of Gerontology, Beijing, PR China; National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, PR China; Beijing Engineering Research Center of Laboratory Medicine, Beijing, PR China
| | - Kaihao Feng
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital / National Center of Gerontology, Beijing, PR China; National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, PR China; Beijing Engineering Research Center of Laboratory Medicine, Beijing, PR China
| | - Abudulimutailipu Nuermaimaiti
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital / National Center of Gerontology, Beijing, PR China; National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, PR China; Beijing Engineering Research Center of Laboratory Medicine, Beijing, PR China
| | - Zhuoqun Lu
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital / National Center of Gerontology, Beijing, PR China; Beijing Engineering Research Center of Laboratory Medicine, Beijing, PR China
| | - Lunan Wang
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital / National Center of Gerontology, Beijing, PR China; National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, PR China; Beijing Engineering Research Center of Laboratory Medicine, Beijing, PR China.
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22
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Hasankhani A, Bahrami A, Tavakoli-Far B, Iranshahi S, Ghaemi F, Akbarizadeh MR, Amin AH, Abedi Kiasari B, Mohammadzadeh Shabestari A. The role of peroxisome proliferator-activated receptors in the modulation of hyperinflammation induced by SARS-CoV-2 infection: A perspective for COVID-19 therapy. Front Immunol 2023; 14:1127358. [PMID: 36875108 PMCID: PMC9981974 DOI: 10.3389/fimmu.2023.1127358] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 02/08/2023] [Indexed: 02/19/2023] Open
Abstract
Coronavirus disease 2019 (COVID-19) is a severe respiratory disease caused by infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that affects the lower and upper respiratory tract in humans. SARS-CoV-2 infection is associated with the induction of a cascade of uncontrolled inflammatory responses in the host, ultimately leading to hyperinflammation or cytokine storm. Indeed, cytokine storm is a hallmark of SARS-CoV-2 immunopathogenesis, directly related to the severity of the disease and mortality in COVID-19 patients. Considering the lack of any definitive treatment for COVID-19, targeting key inflammatory factors to regulate the inflammatory response in COVID-19 patients could be a fundamental step to developing effective therapeutic strategies against SARS-CoV-2 infection. Currently, in addition to well-defined metabolic actions, especially lipid metabolism and glucose utilization, there is growing evidence of a central role of the ligand-dependent nuclear receptors and peroxisome proliferator-activated receptors (PPARs) including PPARα, PPARβ/δ, and PPARγ in the control of inflammatory signals in various human inflammatory diseases. This makes them attractive targets for developing therapeutic approaches to control/suppress the hyperinflammatory response in patients with severe COVID-19. In this review, we (1) investigate the anti-inflammatory mechanisms mediated by PPARs and their ligands during SARS-CoV-2 infection, and (2) on the basis of the recent literature, highlight the importance of PPAR subtypes for the development of promising therapeutic approaches against the cytokine storm in severe COVID-19 patients.
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Affiliation(s)
- Aliakbar Hasankhani
- Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
| | - Abolfazl Bahrami
- Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
- Faculty of Agricultural Sciences and Engineering, University of Tehran, Karaj, Iran
| | - Bahareh Tavakoli-Far
- Dietary Supplements and Probiotic Research Center, Alborz University of Medical Sciences, Karaj, Iran
- Department of Physiology and Pharmacology, School of Medicine, Alborz University of Medical Sciences, Karaj, Iran
| | - Setare Iranshahi
- School of Pharmacy, Shahid Beheshty University of Medical Sciences, Tehran, Iran
| | - Farnaz Ghaemi
- Department of Biochemistry, Faculty of Advanced Sciences and Technology, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Majid Reza Akbarizadeh
- Department of Pediatric, School of Medicine, Amir al momenin Hospital, Zabol University of Medical Sciences, Zabol, Iran
| | - Ali H. Amin
- Zoology Department, Faculty of Science, Mansoura University, Mansoura, Egypt
| | - Bahman Abedi Kiasari
- Virology Department, Faculty of Veterinary Medicine, University of Tehran, Tehran, Iran
| | - Alireza Mohammadzadeh Shabestari
- Department of Dental Surgery, Mashhad University of Medical Sciences, Mashhad, Iran
- Khorasan Covid-19 Scientific Committee, Mashhad, Iran
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23
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Blood leukocyte transcriptional modules and differentially expressed genes associated with disease severity and age in COVID-19 patients. Sci Rep 2023; 13:898. [PMID: 36650374 PMCID: PMC9844197 DOI: 10.1038/s41598-023-28227-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 01/16/2023] [Indexed: 01/18/2023] Open
Abstract
Since the molecular mechanisms determining COVID-19 severity are not yet well understood, there is a demand for biomarkers derived from comparative transcriptome analyses of mild and severe cases, combined with patients' clinico-demographic and laboratory data. Here the transcriptomic response of human leukocytes to SARS-CoV-2 infection was investigated by focusing on the differences between mild and severe cases and between age subgroups (younger and older adults). Three transcriptional modules correlated with these traits were functionally characterized, as well as 23 differentially expressed genes (DEGs) associated to disease severity. One module, correlated with severe cases and older patients, had an overrepresentation of genes involved in innate immune response and in neutrophil activation, whereas two other modules, correlated with disease severity and younger patients, harbored genes involved in the innate immune response to viral infections, and in the regulation of this response. This transcriptomic mechanism could be related to the better outcome observed in younger COVID-19 patients. The DEGs, all hyper-expressed in the group of severe cases, were mostly involved in neutrophil activation and in the p53 pathway, therefore related to inflammation and lymphopenia. These biomarkers may be useful for getting a better stratification of risk factors in COVID-19.
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24
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Shi J, Li G, Yuan X, Wang Y, Gong M, Li C, Ge X, Lu S. Exploration and verification of COVID-19-related hub genes in liver physiological and pathological regeneration. Front Bioeng Biotechnol 2023; 11:1135997. [PMID: 36911196 PMCID: PMC9997844 DOI: 10.3389/fbioe.2023.1135997] [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: 01/02/2023] [Accepted: 02/13/2023] [Indexed: 02/25/2023] Open
Abstract
Objectives An acute injury is often accompanied by tissue regeneration. In this process, epithelial cells show a tendency of cell proliferation under the induction of injury stress, inflammatory factors, and other factors, accompanied by a temporary decline of cellular function. Regulating this regenerative process and avoiding chronic injury is a concern of regenerative medicine. The severe coronavirus disease 2019 (COVID-19) has posed a significant threat to people's health caused by the coronavirus. Acute liver failure (ALF) is a clinical syndrome resulting from rapid liver dysfunction with a fatal outcome. We hope to analyze the two diseases together to find a way for acute failure treatment. Methods COVID-19 dataset (GSE180226) and ALF dataset (GSE38941) were downloaded from the Gene Expression Omnibus (GEO) database, and the "Deseq2" package and "limma" package were used to identify differentially expressed genes (DEGs). Common DEGs were used for hub genes exploration, Protein-Protein Interaction (PPI) network construction, Gene Ontology (GO) functional enrichment, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. The real-time reverse transcriptase-polymerase chain reaction (RT-qPCR) was used to verify the role of hub genes in liver regeneration during in vitro expansion of liver cells and a CCl4-induced ALF mice model. Results: The common gene analysis of the COVID-19 and ALF databases revealed 15 hub genes from 418 common DEGs. These hub genes, including CDC20, were related to cell proliferation and mitosis regulation, reflecting the consistent tissue regeneration change after the injury. Furthermore, hub genes were verified in vitro expansion of liver cells and in vivo ALF model. On this basis, the potential therapeutic small molecule of ALF was found by targeting the hub gene CDC20. Conclusion We have identified hub genes for epithelial cell regeneration under acute injury conditions and explored a new small molecule Apcin for liver function maintenance and ALF treatment. These findings may provide new approaches and ideas for treating COVID-19 patients with ALF.
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Affiliation(s)
- Jihang Shi
- Medical School of Chinese People's Liberation Army (PLA), Beijing, China.,Faculty of Hepato-Pancreato-Biliary Surgery, Chinese PLA General Hospital, Beijing, China.,Institute of Hepatobiliary Surgery of Chinese PLA, Beijing, China
| | - Guangya Li
- MOE Key Laboratory of Cell Proliferation and Differentiation, College of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China.,Peking University-Tsinghua University-National Institute of Biological Science Joint Graduate Program, College of Life Science, Peking University, Beijing, China
| | - Xiandun Yuan
- Department of Rheumatology and Immunology, Peking University Third Hospital, Beijing, China
| | - Yafei Wang
- Medical School of Chinese People's Liberation Army (PLA), Beijing, China.,Faculty of Hepato-Pancreato-Biliary Surgery, Chinese PLA General Hospital, Beijing, China.,Institute of Hepatobiliary Surgery of Chinese PLA, Beijing, China
| | - Ming Gong
- Medical School of Chinese People's Liberation Army (PLA), Beijing, China.,Faculty of Hepato-Pancreato-Biliary Surgery, Chinese PLA General Hospital, Beijing, China.,Institute of Hepatobiliary Surgery of Chinese PLA, Beijing, China
| | - Chonghui Li
- Faculty of Hepato-Pancreato-Biliary Surgery, Chinese PLA General Hospital, Beijing, China.,Institute of Hepatobiliary Surgery of Chinese PLA, Beijing, China
| | - Xinlan Ge
- Faculty of Hepato-Pancreato-Biliary Surgery, Chinese PLA General Hospital, Beijing, China.,Institute of Hepatobiliary Surgery of Chinese PLA, Beijing, China
| | - Shichun Lu
- Faculty of Hepato-Pancreato-Biliary Surgery, Chinese PLA General Hospital, Beijing, China.,Institute of Hepatobiliary Surgery of Chinese PLA, Beijing, China
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25
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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.
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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
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26
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Identification of Transcriptome Biomarkers for Severe COVID-19 with Machine Learning Methods. Biomolecules 2022; 12:biom12121735. [PMID: 36551164 PMCID: PMC9775121 DOI: 10.3390/biom12121735] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 11/18/2022] [Accepted: 11/18/2022] [Indexed: 11/24/2022] Open
Abstract
The rapid spread of COVID-19 has become a major concern for people's lives and health all around the world. COVID-19 patients in various phases and severity require individualized treatment given that different patients may develop different symptoms. We employed machine learning methods to discover biomarkers that may accurately classify COVID-19 in various disease states and severities in this study. The blood gene expression profiles from 50 COVID-19 patients without intensive care, 50 COVID-19 patients with intensive care, 10 non-COVID-19 individuals without intensive care, and 16 non-COVID-19 individuals with intensive care were analyzed. Boruta was first used to remove irrelevant gene features in the expression profiles, and then, the minimum redundancy maximum relevance was applied to sort the remaining features. The generated feature-ranked list was fed into the incremental feature selection method to discover the essential genes and build powerful classifiers. The molecular mechanism of some biomarker genes was addressed using recent studies, and biological functions enriched by essential genes were examined. Our findings imply that genes including UBE2C, PCLAF, CDK1, CCNB1, MND1, APOBEC3G, TRAF3IP3, CD48, and GZMA play key roles in defining the different states and severity of COVID-19. Thus, a new point of reference is provided for understanding the disease's etiology and facilitating a precise therapy.
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27
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Lu L, Liu LP, Gui R, Dong H, Su YR, Zhou XH, Liu FX. Discovering common pathogenetic processes between COVID-19 and sepsis by bioinformatics and system biology approach. Front Immunol 2022; 13:975848. [PMID: 36119022 PMCID: PMC9471316 DOI: 10.3389/fimmu.2022.975848] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 08/04/2022] [Indexed: 11/17/2022] Open
Abstract
Corona Virus Disease 2019 (COVID-19), an acute respiratory infectious disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has spread rapidly worldwide, resulting in a pandemic with a high mortality rate. In clinical practice, we have noted that many critically ill or critically ill patients with COVID-19 present with typical sepsis-related clinical manifestations, including multiple organ dysfunction syndrome, coagulopathy, and septic shock. In addition, it has been demonstrated that severe COVID-19 has some pathological similarities with sepsis, such as cytokine storm, hypercoagulable state after blood balance is disrupted and neutrophil dysfunction. Considering the parallels between COVID-19 and non-SARS-CoV-2 induced sepsis (hereafter referred to as sepsis), the aim of this study was to analyze the underlying molecular mechanisms between these two diseases by bioinformatics and a systems biology approach, providing new insights into the pathogenesis of COVID-19 and the development of new treatments. Specifically, the gene expression profiles of COVID-19 and sepsis patients were obtained from the Gene Expression Omnibus (GEO) database and compared to extract common differentially expressed genes (DEGs). Subsequently, common DEGs were used to investigate the genetic links between COVID-19 and sepsis. Based on enrichment analysis of common DEGs, many pathways closely related to inflammatory response were observed, such as Cytokine-cytokine receptor interaction pathway and NF-kappa B signaling pathway. In addition, protein-protein interaction networks and gene regulatory networks of common DEGs were constructed, and the analysis results showed that ITGAM may be a potential key biomarker base on regulatory analysis. Furthermore, a disease diagnostic model and risk prediction nomogram for COVID-19 were constructed using machine learning methods. Finally, potential therapeutic agents, including progesterone and emetine, were screened through drug-protein interaction networks and molecular docking simulations. We hope to provide new strategies for future research and treatment related to COVID-19 by elucidating the pathogenesis and genetic mechanisms between COVID-19 and sepsis.
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Affiliation(s)
- Lu Lu
- Department of Blood Transfusion, The Third Xiangya Hospital of Central South University, Changsha, China
| | - Le-Ping Liu
- Department of Blood Transfusion, The Third Xiangya Hospital of Central South University, Changsha, China
- Department of Pediatrics, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Rong Gui
- Department of Blood Transfusion, The Third Xiangya Hospital of Central South University, Changsha, China
| | - Hang Dong
- Department of Blood Transfusion, The Third Xiangya Hospital of Central South University, Changsha, China
| | - Yan-Rong Su
- Department of Laboratory Medicine, The Third Xiangya Hospital of Central South University, Changsha, China
| | - Xiong-Hui Zhou
- Department of Blood Transfusion, The Third Xiangya Hospital of Central South University, Changsha, China
| | - Feng-Xia Liu
- Department of Blood Transfusion, The Third Xiangya Hospital of Central South University, Changsha, China
- *Correspondence: Feng-Xia Liu,
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28
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Mosharaf MP, Kibria MK, Hossen MB, Islam MA, Reza MS, Mahumud RA, Alam K, Gow J, Mollah MNH. Meta-Data Analysis to Explore the Hub of the Hub-Genes That Influence SARS-CoV-2 Infections Highlighting Their Pathogenetic Processes and Drugs Repurposing. Vaccines (Basel) 2022; 10:vaccines10081248. [PMID: 36016137 PMCID: PMC9415433 DOI: 10.3390/vaccines10081248] [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: 06/28/2022] [Revised: 07/27/2022] [Accepted: 07/30/2022] [Indexed: 01/09/2023] Open
Abstract
The pandemic of SARS-CoV-2 infections is a severe threat to human life and the world economic condition. Although vaccination has reduced the outspread, but still the situation is not under control because of the instability of RNA sequence patterns of SARS-CoV-2, which requires effective drugs. Several studies have suggested that the SARS-CoV-2 infection causing hub differentially expressed genes (Hub-DEGs). However, we observed that there was not any common hub gene (Hub-DEGs) in our analyses. Therefore, it may be difficult to take a common treatment plan against SARS-CoV-2 infections globally. The goal of this study was to examine if more representative Hub-DEGs from published studies by means of hub of Hub-DEGs (hHub-DEGs) and associated potential candidate drugs. In this study, we reviewed 41 articles on transcriptomic data analysis of SARS-CoV-2 and found 370 unique hub genes or studied genes in total. Then, we selected 14 more representative Hub-DEGs (AKT1, APP, CXCL8, EGFR, IL6, INS, JUN, MAPK1, STAT3, TNF, TP53, UBA52, UBC, VEGFA) as hHub-DEGs by their protein-protein interaction analysis. Their associated biological functional processes, transcriptional, and post-transcriptional regulatory factors. Then we detected hHub-DEGs guided top-ranked nine candidate drug agents (Digoxin, Avermectin, Simeprevir, Nelfinavir Mesylate, Proscillaridin, Linifanib, Withaferin, Amuvatinib, Atazanavir) by molecular docking and cross-validation for treatment of SARS-CoV-2 infections. Therefore, the findings of this study could be useful in formulating a common treatment plan against SARS-CoV-2 infections globally.
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Affiliation(s)
- Md. Parvez Mosharaf
- Bioinformatics Lab, Department of Statistics, University of Rajshahi, Rajshahi 6205, Bangladesh; (M.P.M.); (M.K.K.); (M.B.H.); (M.A.I.); (M.S.R.)
- School of Business, Faculty of Business, Education, Law and Arts, University of Southern Queensland, Toowoomba, QLD 4350, Australia; (K.A.); (J.G.)
| | - Md. Kaderi Kibria
- Bioinformatics Lab, Department of Statistics, University of Rajshahi, Rajshahi 6205, Bangladesh; (M.P.M.); (M.K.K.); (M.B.H.); (M.A.I.); (M.S.R.)
| | - Md. Bayazid Hossen
- Bioinformatics Lab, Department of Statistics, University of Rajshahi, Rajshahi 6205, Bangladesh; (M.P.M.); (M.K.K.); (M.B.H.); (M.A.I.); (M.S.R.)
| | - Md. Ariful Islam
- Bioinformatics Lab, Department of Statistics, University of Rajshahi, Rajshahi 6205, Bangladesh; (M.P.M.); (M.K.K.); (M.B.H.); (M.A.I.); (M.S.R.)
| | - Md. Selim Reza
- Bioinformatics Lab, Department of Statistics, University of Rajshahi, Rajshahi 6205, Bangladesh; (M.P.M.); (M.K.K.); (M.B.H.); (M.A.I.); (M.S.R.)
| | - Rashidul Alam Mahumud
- NHMRC Clinical Trials Centre, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2006, Australia;
| | - Khorshed Alam
- School of Business, Faculty of Business, Education, Law and Arts, University of Southern Queensland, Toowoomba, QLD 4350, Australia; (K.A.); (J.G.)
| | - Jeff Gow
- School of Business, Faculty of Business, Education, Law and Arts, University of Southern Queensland, Toowoomba, QLD 4350, Australia; (K.A.); (J.G.)
- School of Accounting, Economics and Finance, University of KwaZulu Natal, Durban 4001, South Africa
| | - Md. Nurul Haque Mollah
- Bioinformatics Lab, Department of Statistics, University of Rajshahi, Rajshahi 6205, Bangladesh; (M.P.M.); (M.K.K.); (M.B.H.); (M.A.I.); (M.S.R.)
- Correspondence:
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Iqbal N, Kumar P. Integrated COVID-19 Predictor: Differential expression analysis to reveal potential biomarkers and prediction of coronavirus using RNA-Seq profile data. Comput Biol Med 2022; 147:105684. [PMID: 35687925 PMCID: PMC9162937 DOI: 10.1016/j.compbiomed.2022.105684] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Revised: 05/27/2022] [Accepted: 05/30/2022] [Indexed: 02/01/2023]
Abstract
Background The world has been battling the continuous COVID-19 pandemic spread by the SARS-CoV-2 virus for last two years. The issue of viral disease prediction is constantly a matter of interest in virology and the study of disease transmission over the long years. Objective In this study, we aimed to implement genome association studies using RNA-Seq of COVID-19 and reveal highly expressed gene biomarkers and prediction based on the machine learning model of COVID-19 analysis to combat this pandemic. Method We collected RNA-Seq gene count data for both healthy (Control) and non-healthy (Treated) COVID-19 cases. In this experiment, a sequence of bioinformatics strategies and statistical techniques, such as fold-change and adjusted p-value, were processed to identify differentially expressed genes (DEGs). We filtered biomarker sets of high DEGs, moderate DEGs, and low DEGs using DESeq2, Limma Trend, and Limma Voom methods based on intersection and union operations and applied machine learning techniques to predict COVID-19. Result Through experimental analysis, 67 potential biomarkers were extracted, comprising 49 up-regulated and 18 down-regulated genes, using statistical techniques and a set-theory consensus strategy. We trained the machine learning models on 12 different biomarker sets and found that the SVM model performed better than the other classifiers with 99.07% classification accuracy for moderate DEGs. Conclusion Our study revealed that identified differentially expressed genes of the moderate DEGs biomarker set, |log2FC| ≥ 2 with adjusted p-value < 0.05, work significantly as input features to implement a machine learning model using a kernel-based SVM technique to predict COVID-19.
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Yang D, Li H, Chen Y, Ren W, Dong M, Li C, Jiao Q. Immunomodulatory mechanisms of abatacept: A therapeutic strategy for COVID-19. Front Med (Lausanne) 2022; 9:951115. [PMID: 35957855 PMCID: PMC9357915 DOI: 10.3389/fmed.2022.951115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 07/04/2022] [Indexed: 11/13/2022] Open
Abstract
Coronavirus disease 2019 (COVID-19) caused by coronavirus-2 (SARS-CoV-2) infection has rapidly spread throughout the world and become a major threat to human beings. Cytokine storm is a major cause of death in severe patients. Abatacept can suppress cytokines used as antirheumatic drugs in clinical applications. This study analyzed the molecular mechanisms of abatacept treatment for COVID-19. Differentially expressed genes (DEGs) were identified by analyzing expression profiling of abatacept treatment for rheumatoid arthritis (RA) patients and SARS-CoV-2 infection patients. We found that 59 DEGs were upregulated in COVID-19 patients and downregulated following abatacept treatment. Gene set enrichment analysis (GSEA) and Gene Ontology (GO) analysis showed that immune and inflammatory responses were potential regulatory mechanisms. Moreover, we verified 8 targeting genes and identified 15 potential drug candidates for the treatment of COVID-19. Our study illustrated that abatacept could be a promising property for preventing severe COVID-19, and we predicted alternative potential drugs for the treatment of SARS-CoV-2 infection.
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Affiliation(s)
- Dinglong Yang
- Second Clinical Medical College, Shanxi Medical University, Taiyuan, China
| | - Hetong Li
- Second Clinical Medical College, Shanxi Medical University, Taiyuan, China
| | - Yujing Chen
- School of Public Health, Xi'an Jiaotong University, Xi'an, China
| | - Weiping Ren
- Department of Orthopedics, The Second Hospital of Shanxi Medical University, Taiyuan, China
| | - Mingjie Dong
- Second Clinical Medical College, Shanxi Medical University, Taiyuan, China
| | - Chunjiang Li
- Department of Orthopedics, The Second Hospital of Shanxi Medical University, Taiyuan, China
| | - Qiang Jiao
- Department of Orthopedics, The Second Hospital of Shanxi Medical University, Taiyuan, China
- *Correspondence: Qiang Jiao
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Al-Mustanjid M, Mahmud SMH, Akter F, Rahman MS, Hossen MS, Rahman MH, Moni MA. Systems biology models to identify the influence of SARS-CoV-2 infections to the progression of human autoimmune diseases. INFORMATICS IN MEDICINE UNLOCKED 2022; 32:101003. [PMID: 35818398 PMCID: PMC9259025 DOI: 10.1016/j.imu.2022.101003] [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: 02/17/2022] [Revised: 06/25/2022] [Accepted: 06/25/2022] [Indexed: 11/20/2022] Open
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been circulating since 2019, and its global dominance is rising. Evidences suggest the respiratory illness SARS-CoV-2 has a sensitive affect on causing organ damage and other complications to the patients with autoimmune diseases (AD), posing a significant risk factor. The genetic interrelationships and molecular appearances between SARS-CoV-2 and AD are yet unknown. We carried out the transcriptomic analytical framework to delve into the SARS-CoV-2 impacts on AD progression. We analyzed both gene expression microarray and RNA-Seq datasets from SARS-CoV-2 and AD affected tissues. With neighborhood-based benchmarks and multilevel network topology, we obtained dysfunctional signaling and ontological pathways, gene disease (diseasesome) association network and protein-protein interaction network (PPIN), uncovered essential shared infection recurrence connectivities with biological insights underlying between SARS-CoV-2 and AD. We found a total of 77, 21, 9, 54 common DEGs for SARS-CoV-2 and inflammatory bowel disorder (IBD), SARS-CoV-2 and rheumatoid arthritis (RA), SARS-CoV-2 and systemic lupus erythematosus (SLE) and SARS-CoV-2 and type 1 diabetes (T1D). The enclosure of these common DEGs with bimolecular networks revealed 10 hub proteins (FYN, VEGFA, CTNNB1, KDR, STAT1, B2M, CD3G, ITGAV, TGFB3). Drugs such as amlodipine besylate, vorinostat, methylprednisolone, and disulfiram have been identified as a common ground between SARS-CoV-2 and AD from drug repurposing investigation which will stimulate the optimal selection of medications in the battle against this ongoing pandemic triggered by COVID-19.
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Affiliation(s)
- Md Al-Mustanjid
- Department of Software Engineering, Faculty of Science and Information Technology, Daffodil International University, Dhaka-1207, Bangladesh
| | - S M Hasan Mahmud
- Department of Computer Science, American International University-Bangladesh, Dhaka, 1229, Bangladesh
| | - Farzana Akter
- Department of Software Engineering, Faculty of Science and Information Technology, Daffodil International University, Dhaka-1207, Bangladesh
| | - Md Shazzadur Rahman
- Department of Computer Science & Engineering, Faculty of Science and Information Technology, Daffodil International University, Dhaka-1207, Bangladesh
| | - Md Sajid Hossen
- Department of Software Engineering, Faculty of Science and Information Technology, Daffodil International University, Dhaka-1207, Bangladesh
| | - Md Habibur Rahman
- Department of Computer Science and Engineering, Islamic University, Kushtia-7003, Bangladesh
| | - Mohammad Ali Moni
- Department of Computer Science and Engineering, Pabna Science & Technology University, Pabna, 6600, Bangladesh
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Ming T, Dong M, Song X, Li X, Kong Q, Fang Q, Wang J, Wu X, Xia Z. Integrated Analysis of Gene Co-Expression Network and Prediction Model Indicates Immune-Related Roles of the Identified Biomarkers in Sepsis and Sepsis-Induced Acute Respiratory Distress Syndrome. Front Immunol 2022; 13:897390. [PMID: 35844622 PMCID: PMC9281548 DOI: 10.3389/fimmu.2022.897390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 06/03/2022] [Indexed: 11/13/2022] Open
Abstract
Sepsis is a series of clinical syndromes caused by immunological response to severe infection. As the most important and common complication of sepsis, acute respiratory distress syndrome (ARDS) is associated with poor outcomes and high medical expenses. However, well-described studies of analysis-based researches, especially related bioinformatics analysis on revealing specific targets and underlying molecular mechanisms of sepsis and sepsis-induced ARDS (sepsis/se-ARDS), still remain limited and delayed despite the era of data-driven medicine. In this report, weight gene co-expression network based on data from a public database was constructed to identify the key modules and screen the hub genes. Functional annotation by enrichment analysis of the modular genes also demonstrated the key biological processes and signaling pathway; among which, extensive immune-involved enrichment was remarkably associated with sepsis/se-ARDS. Based on the differential expression analysis, least absolute shrink and selection operator, and multivariable logistic regression analysis of the screened hub genes, SIGLEC9, TSPO, CKS1B and PTTG3P were identified as the candidate biomarkers for the further analysis. Accordingly, a four-gene-based model for diagnostic prediction assessment was established and then developed by sepsis/se-ARDS risk nomogram, whose efficiency was verified by calibration curves and decision curve analyses. In addition, various machine learning algorithms were also applied to develop extra models based on the four genes. Receiver operating characteristic curve analysis proved the great diagnostic and predictive performance of these models, and the multivariable logistic regression of the model was still found to be the best as further verified again by the internal test, training, and external validation cohorts. During the development of sepsis/se-ARDS, the expressions of the identified biomarkers including SIGLEC9, TSPO, CKS1B and PTTG3P were all regulated remarkably and generally exhibited notable correlations with the stages of sepsis/se-ARDS. Moreover, the expression levels of these four genes were substantially correlated during sepsis/se-ARDS. Analysis of immune infiltration showed that multiple immune cells, neutrophils and monocytes in particular, might be closely involved in the process of sepsis/se-ARDS. Besides, SIGLEC9, TSPO, CKS1B and PTTG3P were considerably correlated with the infiltration of various immune cells including neutrophils and monocytes during sepsis/se-ARDS. The discovery of relevant gene co-expression network and immune signatures might provide novel insights into the pathophysiology of sepsis/se-ARDS.
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Affiliation(s)
- Tingqian Ming
- Department of Anesthesiology, Renmin Hospital, Wuhan University, Wuhan, China
| | - Mingyou Dong
- College of Medical Laboratory Science, Youjiang Medical College for Nationalities, Baise, China
| | - Xuemin Song
- Department of Anesthesiology and Critical Care Medicine, Zhongnan Hospital, Wuhan University, Wuhan, China
| | - Xingqiao Li
- School of Computer, Wuhan University, Wuhan, China
| | - Qian Kong
- Department of Anesthesiology, Renmin Hospital, Wuhan University, Wuhan, China
| | - Qing Fang
- Department of Anesthesiology and Critical Care Medicine, Zhongnan Hospital, Wuhan University, Wuhan, China
| | - Jie Wang
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital, Wuhan University, Wuhan, China
| | - Xiaojing Wu
- Department of Anesthesiology, Renmin Hospital, Wuhan University, Wuhan, China
- *Correspondence: Zhongyuan Xia, ; Xiaojing Wu,
| | - Zhongyuan Xia
- Department of Anesthesiology, Renmin Hospital, Wuhan University, Wuhan, China
- *Correspondence: Zhongyuan Xia, ; Xiaojing Wu,
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Zhang F, Yu C, Xu W, Li X, Feng J, Shi H, Yang J, Sun Q, Cao X, Zhang L, Peng M. Identification of critical genes and molecular pathways in COVID-19 myocarditis and constructing gene regulatory networks by bioinformatic analysis. PLoS One 2022; 17:e0269386. [PMID: 35749386 PMCID: PMC9231758 DOI: 10.1371/journal.pone.0269386] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 05/19/2022] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND There is growing evidence of a strong relationship between COVID-19 and myocarditis. However, there are few bioinformatics-based analyses of critical genes and the mechanisms related to COVID-19 Myocarditis. This study aimed to identify critical genes related to COVID-19 Myocarditis by bioinformatic methods, explore the biological mechanisms and gene regulatory networks, and probe related drugs. METHODS The gene expression data of GSE150392 and GSE167028 were obtained from the Gene Expression Omnibus (GEO), including cardiomyocytes derived from human induced pluripotent stem cells infected with SARS-CoV-2 in vitro and GSE150392 from patients with myocarditis infected with SARS-CoV-2 and the GSE167028 gene expression dataset. Differentially expressed genes (DEGs) (adjusted P-Value <0.01 and |Log2 Fold Change| ≥2) in GSE150392 were assessed by NetworkAnalyst 3.0. Meanwhile, significant modular genes in GSE167028 were identified by weighted gene correlation network analysis (WGCNA) and overlapped with DEGs to obtain common genes. Functional enrichment analyses were performed by using the "clusterProfiler" package in the R software, and protein-protein interaction (PPI) networks were constructed on the STRING website (https://cn.string-db.org/). Critical genes were identified by the CytoHubba plugin of Cytoscape by 5 algorithms. Transcription factor-gene (TF-gene) and Transcription factor-microRibonucleic acid (TF-miRNA) coregulatory networks construction were performed by NetworkAnalyst 3.0 and displayed in Cytoscape. Finally, Drug Signatures Database (DSigDB) was used to probe drugs associated with COVID-19 Myocarditis. RESULTS Totally 850 DEGs (including 449 up-regulated and 401 down-regulated genes) and 159 significant genes in turquoise modules were identified from GSE150392 and GSE167028, respectively. Functional enrichment analysis indicated that common genes were mainly enriched in biological processes such as cell cycle and ubiquitin-protein hydrolysis. 6 genes (CDK1, KIF20A, PBK, KIF2C, CDC20, UBE2C) were identified as critical genes. TF-gene interactions and TF-miRNA coregulatory network were constructed successfully. A total of 10 drugs, (such as Etoposide, Methotrexate, Troglitazone, etc) were considered as target drugs for COVID-19 Myocarditis. CONCLUSIONS Through bioinformatics method analysis, this study provides a new perspective to explore the pathogenesis, gene regulatory networks and provide drug compounds as a reference for COVID-19 Myocarditis. It is worth highlighting that critical genes (CDK1, KIF20A, PBK, KIF2C, CDC20, UBE2C) may be potential biomarkers and treatment targets of COVID-19 Myocarditis for future study.
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Affiliation(s)
- Fengjun Zhang
- College of Acupuncture and Massage, Shandong University of Traditional Chinese Medicine, Jinan, China
- Department of Traditional Chinese Medicine, Shandong Provincial Hospital affiliated to Shandong First Medical University, Jinan, China
| | - Cheng Yu
- Department of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine Affiliated Hospital, Jinan, 250014, Shandong, China
| | - Wenchang Xu
- College of Acupuncture and Massage, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Xiao Li
- Department of Cardiology, Shandong University of Traditional Chinese Medicine Affiliated Hospital, Jinan, 250014, Shandong, China
| | - Junchen Feng
- College of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Hongshuo Shi
- College of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Jingrong Yang
- College of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Qinhua Sun
- First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Xianyi Cao
- First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Lin Zhang
- Department of Clinical Pharmacy, Shaoxing People’s Hospital, Shaoxing Hospital, Zhejiang University School of Medicine, Shaoxing, China
| | - Min Peng
- Department of Traditional Chinese Medicine, Shandong Provincial Hospital affiliated to Shandong First Medical University, Jinan, China
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Sagulkoo P, Chuntakaruk H, Rungrotmongkol T, Suratanee A, Plaimas K. Multi-Level Biological Network Analysis and Drug Repurposing Based on Leukocyte Transcriptomics in Severe COVID-19: In Silico Systems Biology to Precision Medicine. J Pers Med 2022; 12:jpm12071030. [PMID: 35887528 PMCID: PMC9319133 DOI: 10.3390/jpm12071030] [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: 05/24/2022] [Revised: 06/16/2022] [Accepted: 06/20/2022] [Indexed: 01/08/2023] Open
Abstract
The coronavirus disease 2019 (COVID-19) pandemic causes many morbidity and mortality cases. Despite several developed vaccines and antiviral therapies, some patients experience severe conditions that need intensive care units (ICU); therefore, precision medicine is necessary to predict and treat these patients using novel biomarkers and targeted drugs. In this study, we proposed a multi-level biological network analysis framework to identify key genes via protein–protein interaction (PPI) network analysis as well as survival analysis based on differentially expressed genes (DEGs) in leukocyte transcriptomic profiles, discover novel biomarkers using microRNAs (miRNA) from regulatory network analysis, and provide candidate drugs targeting the key genes using drug–gene interaction network and structural analysis. The results show that upregulated DEGs were mainly enriched in cell division, cell cycle, and innate immune signaling pathways. Downregulated DEGs were primarily concentrated in the cellular response to stress, lysosome, glycosaminoglycan catabolic process, and mature B cell differentiation. Regulatory network analysis revealed that hsa-miR-6792-5p, hsa-let-7b-5p, hsa-miR-34a-5p, hsa-miR-92a-3p, and hsa-miR-146a-5p were predicted biomarkers. CDC25A, GUSB, MYBL2, and SDAD1 were identified as key genes in severe COVID-19. In addition, drug repurposing from drug–gene and drug–protein database searching and molecular docking showed that camptothecin and doxorubicin were candidate drugs interacting with the key genes. In conclusion, multi-level systems biology analysis plays an important role in precision medicine by finding novel biomarkers and targeted drugs based on key gene identification.
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Affiliation(s)
- Pakorn Sagulkoo
- Program in Bioinformatics and Computational Biology, Graduate School, Chulalongkorn University, Bangkok 10330, Thailand; (P.S.); (H.C.); (T.R.)
- Center of Biomedical Informatics, Department of Family Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai 50200, Thailand
| | - Hathaichanok Chuntakaruk
- Program in Bioinformatics and Computational Biology, Graduate School, Chulalongkorn University, Bangkok 10330, Thailand; (P.S.); (H.C.); (T.R.)
- Center of Excellence in Biocatalyst and Sustainable Biotechnology Research Unit, Department of Biochemistry, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand
| | - Thanyada Rungrotmongkol
- Program in Bioinformatics and Computational Biology, Graduate School, Chulalongkorn University, Bangkok 10330, Thailand; (P.S.); (H.C.); (T.R.)
- Center of Excellence in Biocatalyst and Sustainable Biotechnology Research Unit, Department of Biochemistry, Faculty of Science, Chulalongkorn University, Bangkok 10330, 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
- Program in Bioinformatics and Computational Biology, Graduate School, Chulalongkorn University, Bangkok 10330, Thailand; (P.S.); (H.C.); (T.R.)
- 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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Zhang G, Cui X, Zhang L, Liu G, Zhu X, Shangguan J, Zhang W, Zheng Y, Zhang H, Tang J, Zhang J. Uncovering the genetic links of SARS-CoV-2 infections on heart failure co-morbidity by a systems biology approach. ESC Heart Fail 2022; 9:2937-2954. [PMID: 35727093 PMCID: PMC9349450 DOI: 10.1002/ehf2.14003] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 04/24/2022] [Accepted: 05/19/2022] [Indexed: 01/08/2023] Open
Abstract
Aims The co‐morbidities contribute to the inferior prognosis of COVID‐19 patients. Recent reports suggested that the higher co‐morbidity rate between COVID‐19 and heart failure (HF) leads to increased mortality. However, the common pathogenic mechanism between them remained elusive. Here, we aimed to reveal underlying molecule mechanisms and genetic correlation between COVID‐19 and HF, providing a new perspective on current clinical management for patients with co‐morbidity. Methods The gene expression profiles of HF (GSE26887) and COVID‐19 (GSE147507) were retrieved from the GEO database. After identifying the common differentially expressed genes (|log2FC| > 1 and adjusted P < 0.05), integrated analyses were performed, namely, enrichment analyses, protein–protein interaction network, module construction, critical gene identification, and functional co‐expression analysis. The performance of critical genes was validation combining hierarchical clustering, correlation, and principal component analysis in external datasets (GSE164805 and GSE9128). Potential transcription factors and miRNAs were obtained from the JASPER and RegNetwork repository used to construct co‐regulatory networks. The candidate drug compounds in potential genetic link targets were further identified using the DSigDB database. Results The alteration of 12 genes was identified as a shared transcriptional signature, with the role of immune inflammatory pathway, especially Toll‐like receptor, NF‐kappa B, chemokine, and interleukin‐related pathways that primarily emphasized in response to SARS‐CoV‐2 complicated with HF. Top 10 critical genes (TLR4, TLR2, CXCL8, IL10, STAT3, IL1B, TLR1, TP53, CCL20, and CXCL10) were identified from protein–protein interaction with topological algorithms. The unhealthy microbiota status and gut–heart axis in co‐morbidity were identified as potential disease roads in bridging pathogenic mechanism, and lipopolysaccharide acts as a potential marker for monitoring HF during COVID‐19. For transcriptional and post‐transcriptional levels, regulation networks tightly coupling with both disorders were constructed, and significant regulator signatures with high interaction degree, especially FOXC1, STAT3, NF‐κB1, miR‐181, and miR‐520, were detected to regulate common differentially expressed genes. According to genetic links targets, glutathione‐based antioxidant strategy combined with muramyl dipeptide‐based microbe‐derived immunostimulatory therapies was identified as promising anti‐COVID‐19 and anti‐HF therapeutics. Conclusions This study identified shared transcriptomic and corresponding regulatory signatures as emerging therapeutic targets and detected a set of pharmacologic agents targeting genetic links. Our findings provided new insights for underlying pathogenic mechanisms between COVID‐19 and HF.
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Affiliation(s)
- Ge Zhang
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory of Cardiac Injury and Repair of Henan Province, Zhengzhou, China.,Henan Province Clinical Research Center for Cardiovascular Diseases, Zhengzhou, China
| | - Xiaolin Cui
- Christchurch Regenerative Medicine and Tissue Engineering (CReaTE) Group, Department of Orthopaedic Surgery and Musculoskeletal Medicine, University of Otago, Christchurch, Canterbury, New Zealand
| | - Li Zhang
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory of Cardiac Injury and Repair of Henan Province, Zhengzhou, China.,Henan Province Clinical Research Center for Cardiovascular Diseases, Zhengzhou, China
| | - Gangqiong Liu
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory of Cardiac Injury and Repair of Henan Province, Zhengzhou, China.,Henan Province Clinical Research Center for Cardiovascular Diseases, Zhengzhou, China
| | - Xiaodan Zhu
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory of Cardiac Injury and Repair of Henan Province, Zhengzhou, China.,Henan Province Clinical Research Center for Cardiovascular Diseases, Zhengzhou, China
| | - Jiahong Shangguan
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory of Cardiac Injury and Repair of Henan Province, Zhengzhou, China.,Henan Province Clinical Research Center for Cardiovascular Diseases, Zhengzhou, China
| | - Wenjing Zhang
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory of Cardiac Injury and Repair of Henan Province, Zhengzhou, China.,Henan Province Clinical Research Center for Cardiovascular Diseases, Zhengzhou, China
| | - Yingying Zheng
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory of Cardiac Injury and Repair of Henan Province, Zhengzhou, China.,Henan Province Clinical Research Center for Cardiovascular Diseases, Zhengzhou, China
| | - Hui Zhang
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory of Cardiac Injury and Repair of Henan Province, Zhengzhou, China.,Henan Province Clinical Research Center for Cardiovascular Diseases, Zhengzhou, China
| | - Junnan Tang
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory of Cardiac Injury and Repair of Henan Province, Zhengzhou, China.,Henan Province Clinical Research Center for Cardiovascular Diseases, Zhengzhou, China
| | - Jinying Zhang
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory of Cardiac Injury and Repair of Henan Province, Zhengzhou, China.,Henan Province Clinical Research Center for Cardiovascular Diseases, Zhengzhou, China
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SARS CoV-2 (Delta Variant) Infection Kinetics and Immunopathogenesis in Domestic Cats. Viruses 2022; 14:v14061207. [PMID: 35746678 PMCID: PMC9230585 DOI: 10.3390/v14061207] [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: 05/06/2022] [Revised: 05/27/2022] [Accepted: 05/29/2022] [Indexed: 02/04/2023] Open
Abstract
Continued emergence of SARS-CoV-2 variants highlights the critical need for adaptable and translational animal models for acute COVID-19. Limitations to current animal models for SARS CoV-2 (e.g., transgenic mice, non-human primates, ferrets) include subclinical to mild lower respiratory disease, divergence from clinical COVID-19 disease course, and/or the need for host genetic modifications to permit infection. We therefore established a feline model to study COVID-19 disease progression and utilized this model to evaluate infection kinetics and immunopathology of the rapidly circulating Delta variant (B.1.617.2) of SARS-CoV-2. In this study, specific-pathogen-free domestic cats (n = 24) were inoculated intranasally and/or intratracheally with SARS CoV-2 (B.1.617.2). Infected cats developed severe clinical respiratory disease and pulmonary lesions at 4- and 12-days post-infection (dpi), even at 1/10 the dose of previously studied wild-type SARS-CoV-2. Infectious virus was isolated from nasal secretions of delta-variant infected cats in high amounts at multiple timepoints, and viral antigen was co-localized in ACE2-expressing cells of the lungs (pneumocytes, vascular endothelium, peribronchial glandular epithelium) and strongly associated with severe pulmonary inflammation and vasculitis that were more pronounced than in wild-type SARS-CoV-2 infection. RNA sequencing of infected feline lung tissues identified upregulation of multiple gene pathways associated with cytokine receptor interactions, chemokine signaling, and viral protein–cytokine interactions during acute infection with SARS-CoV-2. Weighted correlation network analysis (WGCNA) of differentially expressed genes identified several distinct clusters of dysregulated hub genes that are significantly correlated with both clinical signs and lesions during acute infection. Collectively, the results of these studies help to delineate the role of domestic cats in disease transmission and response to variant emergence, establish a flexible translational model to develop strategies to prevent the spread of SARS-CoV-2, and identify potential targets for downstream therapeutic development.
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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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Chen T, Polak P, Uryasev S. Classification and severity progression measure of COVID-19 patients using pairs of multi-omic factors. J Appl Stat 2022; 50:2473-2503. [PMID: 37529561 PMCID: PMC10388828 DOI: 10.1080/02664763.2022.2064975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Accepted: 04/06/2022] [Indexed: 10/18/2022]
Abstract
Early detection and effective treatment of severe COVID-19 patients remain two major challenges during the current pandemic. Analysis of molecular changes in blood samples of severe patients is one of the promising approaches to this problem. From thousands of proteomic, metabolomic, lipidomic, and transcriptomic biomarkers selected in other research, we identify several pairs of biomarkers that after additional nonlinear spline transformation are highly effective in classifying and predicting severe COVID-19 cases. The performance of these pairs is evaluated in-sample, in a cross-validation exercise, and in an out-of-sample analysis on two independent datasets. We further improve our classifier by identifying complementary pairs using hierarchical clustering. In a result, we achieve 96-98% AUC on the validation data. Our findings can help medical experts to identify small groups of biomarkers that after nonlinear transformation can be used to construct a cost-effective test for patient screening and prediction of severity progression.
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Affiliation(s)
- Teng Chen
- Department of Applied Math & Statistics, Stony Brook University, Stony Brook, NY, USA
| | - Paweł Polak
- Department of Applied Math & Statistics, Stony Brook University, Stony Brook, NY, USA
| | - Stanislav Uryasev
- Department of Applied Math & Statistics, Stony Brook University, Stony Brook, NY, USA
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You H, Zhao Q, Dong M. The Key Genes Underlying Pathophysiology Correlation Between the Acute Myocardial Infarction and COVID-19. Int J Gen Med 2022; 15:2479-2490. [PMID: 35282650 PMCID: PMC8904943 DOI: 10.2147/ijgm.s354885] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 02/23/2022] [Indexed: 11/30/2022] Open
Abstract
Introduction Accumulating evidences disclose that COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has a marked effect on acute myocardial infarction (AMI). Nevertheless, the underlying pathophysiology correlation between the AMI and COVID-19 remains vague. Materials and Methods Bioinformatics analyses of the altered transcriptional profiling of peripheral blood mononuclear cells (PBMCs) in patients with AMI and COVID-19 were implemented, including identification of differentially expressed genes and common genes between AMI and COVID-19, protein–protein interactions, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses, TF-genes and miRNA coregulatory networks, to explore their biological functions and potential roles in the pathogenesis of COVID-19-related AMI. Conclusion Our bioinformatic analyses of gene expression profiling of PBMCs in patients with AMI and COVID-19 provide us with a unique view regarding underlying pathophysiology correlation between the two vital diseases.
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Affiliation(s)
- Hongjun You
- Department of Cardiovascular Medicine, Shaanxi Provincial People’s Hospital, Xi’an, 710068, Shaanxi, People’s Republic of China
| | - Qianqian Zhao
- Department of Clinical Immunology, The First Affiliated Hospital, Air Force Military Medical University, Xi’an, 710032, Shaanxi, People’s Republic of China
| | - Mengya Dong
- Department of Cardiovascular Medicine, Shaanxi Provincial People’s Hospital, Xi’an, 710068, Shaanxi, People’s Republic of China
- Correspondence: Mengya Dong, Department of Cardiovascular Medicine, Shaanxi Provincial People’s Hospital, 256 West Youyi Road, Xi’an, Shaanxi, 710068, People’s Republic of China, Tel +86–15802943974, Email
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Ma Y, Qiu F, Deng C, Li J, Huang Y, Wu Z, Zhou Y, Zhang Y, Xiong Y, Yao Y, Zhong Y, Qu J, Su J. Integrating single-cell sequencing data with GWAS summary statistics reveals CD16+monocytes and memory CD8+T cells involved in severe COVID-19. Genome Med 2022; 14:16. [PMID: 35172892 PMCID: PMC8851814 DOI: 10.1186/s13073-022-01021-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 02/06/2022] [Indexed: 02/08/2023] Open
Abstract
Background Understanding the host genetic architecture and viral immunity contributes to the development of effective vaccines and therapeutics for controlling the COVID-19 pandemic. Alterations of immune responses in peripheral blood mononuclear cells play a crucial role in the detrimental progression of COVID-19. However, the effects of host genetic factors on immune responses for severe COVID-19 remain largely unknown. Methods We constructed a computational framework to characterize the host genetics that influence immune cell subpopulations for severe COVID-19 by integrating GWAS summary statistics (N = 969,689 samples) with four independent scRNA-seq datasets containing healthy controls and patients with mild, moderate, and severe symptom (N = 606,534 cells). We collected 10 predefined gene sets including inflammatory and cytokine genes to calculate cell state score for evaluating the immunological features of individual immune cells. Results We found that 34 risk genes were significantly associated with severe COVID-19, and the number of highly expressed genes increased with the severity of COVID-19. Three cell subtypes that are CD16+monocytes, megakaryocytes, and memory CD8+T cells were significantly enriched by COVID-19-related genetic association signals. Notably, three causal risk genes of CCR1, CXCR6, and ABO were highly expressed in these three cell types, respectively. CCR1+CD16+monocytes and ABO+ megakaryocytes with significantly up-regulated genes, including S100A12, S100A8, S100A9, and IFITM1, confer higher risk to the dysregulated immune response among severe patients. CXCR6+ memory CD8+ T cells exhibit a notable polyfunctionality including elevation of proliferation, migration, and chemotaxis. Moreover, we observed an increase in cell-cell interactions of both CCR1+ CD16+monocytes and CXCR6+ memory CD8+T cells in severe patients compared to normal controls among both PBMCs and lung tissues. The enhanced interactions of CXCR6+ memory CD8+T cells with epithelial cells facilitate the recruitment of this specific population of T cells to airways, promoting CD8+T cell-mediated immunity against COVID-19 infection. Conclusions We uncover a major genetics-modulated immunological shift between mild and severe infection, including an elevated expression of genetics-risk genes, increase in inflammatory cytokines, and of functional immune cell subsets aggravating disease severity, which provides novel insights into parsing the host genetic determinants that influence peripheral immune cells in severe COVID-19. Supplementary Information The online version contains supplementary material available at 10.1186/s13073-022-01021-1.
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Affiliation(s)
- Yunlong Ma
- Institute of Biomedical Big Data, School of Ophthalmology & Optometry and Eye Hospital, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou, 325027, China
| | - Fei Qiu
- Institute of Biomedical Big Data, School of Ophthalmology & Optometry and Eye Hospital, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou, 325027, China
| | - Chunyu Deng
- Institute of Biomedical Big Data, School of Ophthalmology & Optometry and Eye Hospital, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou, 325027, China
| | - Jingjing Li
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, the First Affiliated Hospital, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang University School of Medicine, Zhejiang, 310003, Hangzhou, China
| | - Yukuan Huang
- Institute of Biomedical Big Data, School of Ophthalmology & Optometry and Eye Hospital, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou, 325027, China
| | - Zeyi Wu
- Institute of Biomedical Big Data, School of Ophthalmology & Optometry and Eye Hospital, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou, 325027, China
| | - Yijun Zhou
- Institute of Biomedical Big Data, School of Ophthalmology & Optometry and Eye Hospital, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou, 325027, China
| | - Yaru Zhang
- Institute of Biomedical Big Data, School of Ophthalmology & Optometry and Eye Hospital, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou, 325027, China
| | - Yichun Xiong
- Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, 325011, China
| | - Yinghao Yao
- Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, 325011, China
| | - Yigang Zhong
- Department of Cardiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jia Qu
- Institute of Biomedical Big Data, School of Ophthalmology & Optometry and Eye Hospital, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou, 325027, China
| | - Jianzhong Su
- Institute of Biomedical Big Data, School of Ophthalmology & Optometry and Eye Hospital, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou, 325027, China. .,Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, 325011, China.
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Zhang Y, Guo X, Li C, Kou Z, Lin L, Yao M, Pang B, Zhang X, Duan Q, Tian X, Xing Y, Jiang X. Transcriptome Analysis of Peripheral Blood Mononuclear Cells in SARS-CoV-2 Naïve and Recovered Individuals Vaccinated With Inactivated Vaccine. Front Cell Infect Microbiol 2022; 11:821828. [PMID: 35186784 PMCID: PMC8851474 DOI: 10.3389/fcimb.2021.821828] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 12/30/2021] [Indexed: 12/13/2022] Open
Abstract
The urgent approval of the use of the inactivated COVID-19 vaccine is essential to reduce the threat and burden of the epidemic on global public health, however, our current understanding of the host immune response to inactivated vaccine remains limited. Herein, we performed serum IgG antibody detection and transcriptomics analysis on 20 SARS-CoV-2 naïve individuals who received multiple doses of inactivated vaccine and 5 SARS-CoV-2 recovered individuals who received single dose of inactivated vaccine. Our research revealed the important role of many innate immune pathways after vaccination, identified a significant correlation with the third dose of booster vaccine and proteasome-related genes, and found that SARS-CoV-2 recovered individuals can produces a strong immune response to a single dose of inactivated vaccine. These results help us understand the reaction mechanism of the host's molecular immune system to the inactivated vaccine, and provide a basis for the choice of vaccination strategy.
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Affiliation(s)
- Yuwei Zhang
- Infectious Disease Prevention and Control Section, Shandong Center for Disease Control and Prevention, Jinan, China
| | - Xingyu Guo
- Infectious Disease Prevention and Control Section, School of Public Health and Health Management, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Cunbao Li
- Infectious Disease Prevention and Control Section, Lanshan Center for Disease Control and Prevention, Linyi, China
| | - Zengqiang Kou
- Infectious Disease Prevention and Control Section, Shandong Center for Disease Control and Prevention, Jinan, China
| | - Lanfang Lin
- Infectious Disease Prevention and Control Section, Lanshan Center for Disease Control and Prevention, Linyi, China
| | - Mingxiao Yao
- Infectious Disease Prevention and Control Section, Shandong Center for Disease Control and Prevention, Jinan, China
| | - Bo Pang
- Infectious Disease Prevention and Control Section, Shandong Center for Disease Control and Prevention, Jinan, China
| | - Xiaomei Zhang
- Infectious Disease Prevention and Control Section, Shandong Center for Disease Control and Prevention, Jinan, China
| | - Qing Duan
- Infectious Disease Prevention and Control Section, Shandong Center for Disease Control and Prevention, Jinan, China
| | - Xueying Tian
- Infectious Disease Prevention and Control Section, Shandong Center for Disease Control and Prevention, Jinan, China
| | - Yufang Xing
- Infectious Disease Prevention and Control Section, Shandong Center for Disease Control and Prevention, Jinan, China
| | - Xiaolin Jiang
- Ministry of Research and Education, Shandong Center for Disease Control and Prevention, Jinan, China
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Feng S, Song F, Guo W, Tan J, Zhang X, Qiao F, Guo J, Zhang L, Jia X. Potential Genes Associated with COVID-19 and Comorbidity. Int J Med Sci 2022; 19:402-415. [PMID: 35165525 PMCID: PMC8795808 DOI: 10.7150/ijms.67815] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Accepted: 01/05/2022] [Indexed: 11/10/2022] Open
Abstract
Hypertension, diabetes mellitus, and coronary artery disease are common comorbidities and dangerous factors for infection and serious COVID-19. Polymorphisms in genes associated with comorbidities may help observe susceptibility and disease severity variation. However, specific genetic factors and the extent to which they can explain variation in susceptibility of severity are unclear. Therefore, we evaluated candidate genes associated with COVID-19 and hypertension, diabetes mellitus, and coronary artery disease. In particular, we performed searches against OMIM, NCBI, and other databases, protein-protein interaction network construction, and GO and KEGG pathway enrichment analyses. Results showed that the associated overlapping genes were TLR4, NLRP3, MBL2, IL6, IL1RN, IL1B, CX3CR1, CCR5, AGT, ACE, and F2. GO and KEGG analyses yielded 302 GO terms (q < 0.05) and 29 signaling pathways (q < 0.05), respectively, mainly including coronavirus disease-COVID-19 and cytokine-cytokine receptor interaction. IL6 and AGT were central in the PPI, with 8 and 5 connections, respectively. In this study, we identified 11 genes associated with both COVID-19 and three comorbidities that may contribute to infection and disease severity. The key genes IL6 and AGT are involved in regulating immune response, cytokine activity, and viral infection. Therefore, RAAS inhibitors, AGT antisense nucleotides, cytokine inhibitors, vitamin D, fenofibrate, and vaccines regulating non-immune and immune factors could be potential strategies to prevent and cure COVID-19. The study provides a basis for further investigation of genes and pathways with predictive value for the risk of infection and prognosis and could help guide drug and vaccine development to improve treatment efficacy and the development of personalised treatments, especially for COVID-19 individuals with common comorbidities.
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Affiliation(s)
- Shanshan Feng
- College of Medical Technology, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
- Non-coding RNA and Drug Discovery Key Laboratory of Sichuan Province, Chengdu Medical College, Chengdu, Sichuan, China
| | - Fuqiang Song
- Department of medical Laboratory, The General Hospital of Western Theater Command, Chengdu, China
| | | | - Jishan Tan
- Department of medical Laboratory, The General Hospital of Western Theater Command, Chengdu, China
| | - Xianqin Zhang
- School of Basic Medical Sciences, Chengdu Medical College, Chengdu, Sichuan, China
| | - Fengling Qiao
- College of Medical Technology, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Jinlin Guo
- Key Laboratory of Systematic Research of Distinctive Chinese Medicine Resources in Southwest China, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Chongqing Key Laboratory of Sichuan-Chongqing Co-construction for Diagnosis and Treatment of Infectious Diseases Integrated Traditional Chinese and Western Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Lin Zhang
- Department of Pharmacy, Shaoxing People's Hospital, Shaoxing Hospital, Zhejiang University School of Medicine, Shaoxing, China
| | - Xu Jia
- Non-coding RNA and Drug Discovery Key Laboratory of Sichuan Province, Chengdu Medical College, Chengdu, Sichuan, China
- School of Basic Medical Sciences, Chengdu Medical College, Chengdu, Sichuan, China
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Yerukala Sathipati S, Shukla SK, Ho SY. Tracking the amino acid changes of spike proteins across diverse host species of severe acute respiratory syndrome coronavirus 2. iScience 2022; 25:103560. [PMID: 34877480 PMCID: PMC8638202 DOI: 10.1016/j.isci.2021.103560] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 11/02/2021] [Accepted: 11/30/2021] [Indexed: 12/14/2022] Open
Abstract
Knowledge of the host-specific properties of the spike protein is of crucial importance to understand the adaptability of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) to infect multiple species and alter transmissibility, particularly in humans. Here, we propose a spike protein predictor SPIKES incorporating with an inheritable bi-objective combinatorial genetic algorithm to identify the biochemical properties of spike proteins and determine their specificity to human hosts. SPIKES identified 20 informative physicochemical properties of the spike protein, including information measures for alpha helix and relative mutability, and amino acid and dipeptide compositions, which have shown compositional difference at the amino acid sequence level between human and diverse animal coronaviruses. We suggest that alterations of these amino acids between human and animal coronaviruses may provide insights into the development and transmission of SARS-CoV-2 in human and other species and support the discovery of targeted antiviral therapies. Differences exist in the amino acids within the S protein of diverse host species CoVs We developed SPIKES to identify informative properties of S protein SARS-CoV-2 variants have amino acid changes that alter infection and transmission The SPIKES identified changes in S protein properties from animal to human host CoVs
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Affiliation(s)
- Srinivasulu Yerukala Sathipati
- Center for Precision Medicine Research, Marshfield Clinic Research Institute, Marshfield, WI 54449, USA
- Corresponding author
| | - Sanjay K. Shukla
- Center for Precision Medicine Research, Marshfield Clinic Research Institute, Marshfield, WI 54449, USA
| | - Shinn-Ying Ho
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
- Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
- Center for intelligent Drug Systems and Smart Bio-Devices (IDSB), National Yang Ming Chiao Tung University, Hsinchu, Taiwan
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Hasankhani A, Bahrami A, Sheybani N, Aria B, Hemati B, Fatehi F, Ghaem Maghami Farahani H, Javanmard G, Rezaee M, Kastelic JP, Barkema HW. Differential Co-Expression Network Analysis Reveals Key Hub-High Traffic Genes as Potential Therapeutic Targets for COVID-19 Pandemic. Front Immunol 2022; 12:789317. [PMID: 34975885 PMCID: PMC8714803 DOI: 10.3389/fimmu.2021.789317] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Accepted: 11/26/2021] [Indexed: 01/08/2023] Open
Abstract
Background The recent emergence of COVID-19, rapid worldwide spread, and incomplete knowledge of molecular mechanisms underlying SARS-CoV-2 infection have limited development of therapeutic strategies. Our objective was to systematically investigate molecular regulatory mechanisms of COVID-19, using a combination of high throughput RNA-sequencing-based transcriptomics and systems biology approaches. Methods RNA-Seq data from peripheral blood mononuclear cells (PBMCs) of healthy persons, mild and severe 17 COVID-19 patients were analyzed to generate a gene expression matrix. Weighted gene co-expression network analysis (WGCNA) was used to identify co-expression modules in healthy samples as a reference set. For differential co-expression network analysis, module preservation and module-trait relationships approaches were used to identify key modules. Then, protein-protein interaction (PPI) networks, based on co-expressed hub genes, were constructed to identify hub genes/TFs with the highest information transfer (hub-high traffic genes) within candidate modules. Results Based on differential co-expression network analysis, connectivity patterns and network density, 72% (15 of 21) of modules identified in healthy samples were altered by SARS-CoV-2 infection. Therefore, SARS-CoV-2 caused systemic perturbations in host biological gene networks. In functional enrichment analysis, among 15 non-preserved modules and two significant highly-correlated modules (identified by MTRs), 9 modules were directly related to the host immune response and COVID-19 immunopathogenesis. Intriguingly, systemic investigation of SARS-CoV-2 infection identified signaling pathways and key genes/proteins associated with COVID-19's main hallmarks, e.g., cytokine storm, respiratory distress syndrome (ARDS), acute lung injury (ALI), lymphopenia, coagulation disorders, thrombosis, and pregnancy complications, as well as comorbidities associated with COVID-19, e.g., asthma, diabetic complications, cardiovascular diseases (CVDs), liver disorders and acute kidney injury (AKI). Topological analysis with betweenness centrality (BC) identified 290 hub-high traffic genes, central in both co-expression and PPI networks. We also identified several transcriptional regulatory factors, including NFKB1, HIF1A, AHR, and TP53, with important immunoregulatory roles in SARS-CoV-2 infection. Moreover, several hub-high traffic genes, including IL6, IL1B, IL10, TNF, SOCS1, SOCS3, ICAM1, PTEN, RHOA, GDI2, SUMO1, CASP1, IRAK3, HSPA5, ADRB2, PRF1, GZMB, OASL, CCL5, HSP90AA1, HSPD1, IFNG, MAPK1, RAB5A, and TNFRSF1A had the highest rates of information transfer in 9 candidate modules and central roles in COVID-19 immunopathogenesis. Conclusion This study provides comprehensive information on molecular mechanisms of SARS-CoV-2-host interactions and identifies several hub-high traffic genes as promising therapeutic targets for the COVID-19 pandemic.
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Affiliation(s)
- Aliakbar Hasankhani
- Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
| | - Abolfazl Bahrami
- Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.,Biomedical Center for Systems Biology Science Munich, Ludwig-Maximilians-University, Munich, Germany
| | - Negin Sheybani
- Department of Animal and Poultry Science, College of Aburaihan, University of Tehran, Tehran, Iran
| | - Behzad Aria
- Department of Physical Education and Sports Science, School of Psychology and Educational Sciences, Yazd University, Yazd, Iran
| | - Behzad Hemati
- Biotechnology Research Center, Karaj Branch, Islamic Azad University, Karaj, Iran
| | - Farhang Fatehi
- Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
| | | | - Ghazaleh Javanmard
- Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
| | - Mahsa Rezaee
- Department of Medical Mycology, School of Medical Science, Tarbiat Modares University, Tehran, Iran
| | - John P Kastelic
- Department of Production Animal Health, Faculty of Veterinary Medicine, University of Calgary, Calgary, AB, Canada
| | - Herman W Barkema
- Department of Production Animal Health, Faculty of Veterinary Medicine, University of Calgary, Calgary, AB, Canada
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Unal U, Comertpay B, Demirtas TY, Gov E. Drug repurposing for rheumatoid arthritis: Identification of new drug candidates via bioinformatics and text mining analysis. Autoimmunity 2022; 55:147-156. [PMID: 35048767 DOI: 10.1080/08916934.2022.2027922] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Rheumatoid arthritis (RA) is an autoimmune disease that results in the destruction of tissue by attacks on the patient by his or her own immune system. Current treatment strategies are not sufficient to overcome RA. In the present study, various transcriptomic data from synovial fluids, synovial fluid-derived macrophages, and blood samples from patients with RA were analysed using bioinformatics approaches to identify tissue-specific repurposing drug candidates for RA. Differentially expressed genes (DEGs) were identified by integrating datasets for each tissue and comparing diseased to healthy samples. Tissue-specific protein-protein interaction (PPI) networks were generated and topologically prominent proteins were selected. Transcription-regulating biomolecules for each tissue type were determined from protein-DNA interaction data. Common DEGs and reporter biomolecules were used to identify drug candidates for repurposing using the hypergeometric test. As a result of bioinformatic analyses, 19 drugs were identified as repurposing candidates for RA, and text mining analyses supported our findings. We hypothesize that the FDA-approved drugs momelotinib, ibrutinib, and sodium butyrate may be promising candidates for RA. In addition, CHEMBL306380, Compound 19a (CHEMBL3116050), ME-344, XL-019, TG100801, JNJ-26483327, and NV-128 were identified as novel repurposing candidates for the treatment of RA. Preclinical and further validation of these drugs may provide new treatment options for RA.
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Affiliation(s)
- Ulku Unal
- Department of Bioengineering, Adana Alparslan Türkeş Science and Technology University, Adana, Turkey
| | - Betul Comertpay
- Department of Bioengineering, Adana Alparslan Türkeş Science and Technology University, Adana, Turkey
| | - Talip Yasir Demirtas
- Department of Bioengineering, Adana Alparslan Türkeş Science and Technology University, Adana, Turkey
| | - Esra Gov
- Department of Bioengineering, Adana Alparslan Türkeş Science and Technology University, Adana, Turkey
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Systems Biology and Bioinformatics approach to Identify blood based signatures molecules and drug targets of patient with COVID-19. INFORMATICS IN MEDICINE UNLOCKED 2022; 28:100840. [PMID: 34981034 PMCID: PMC8716147 DOI: 10.1016/j.imu.2021.100840] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 12/27/2021] [Indexed: 01/08/2023] Open
Abstract
Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection results in the development of a highly contagious respiratory ailment known as new coronavirus disease (COVID-19). Despite the fact that the prevalence of COVID-19 continues to rise, it is still unclear how people become infected with SARS-CoV-2 and how patients with COVID-19 become so unwell. Detecting biomarkers for COVID-19 using peripheral blood mononuclear cells (PBMCs) may aid in drug development and treatment. This research aimed to find blood cell transcripts that represent levels of gene expression associated with COVID-19 progression. Through the development of a bioinformatics pipeline, two RNA-Seq transcriptomic datasets and one microarray dataset were studied and discovered 102 significant differentially expressed genes (DEGs) that were shared by three datasets derived from PBMCs. To identify the roles of these DEGs, we discovered disease-gene association networks and signaling pathways, as well as we performed gene ontology (GO) studies and identified hub protein. Identified significant gene ontology and molecular pathways improved our understanding of the pathophysiology of COVID-19, and our identified blood-based hub proteins TPX2, DLGAP5, NCAPG, CCNB1, KIF11, HJURP, AURKB, BUB1B, TTK, and TOP2A could be used for the development of therapeutic intervention. In COVID-19 subjects, we discovered effective putative connections between pathological processes in the transcripts blood cells, suggesting that blood cells could be used to diagnose and monitor the disease’s initiation and progression as well as developing drug therapeutics.
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Demirtas TY, Rahman MR, Yurtsever MC, Gov E. Forecasting Gastric Cancer Diagnosis, Prognosis, and Drug Repurposing with Novel Gene Expression Signatures. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2022; 26:64-74. [PMID: 34910889 DOI: 10.1089/omi.2021.0195] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Gastric cancer (GC) is a prevalent disease worldwide with high mortality and poor treatment success. Early diagnosis of GC and forecasting of its prognosis with the use of biomarkers are directly relevant to achieve both personalized/precision medicine and innovation in cancer therapeutics. Gene expression signatures offer one of the promising avenues of research in this regard, as well as guiding drug repurposing analyses in cancers. Using publicly accessible gene expression datasets from the Gene Expression Omnibus and The Cancer Genome Atlas (TCGA), we report here original findings on co-expressed gene modules that are differentially expressed between 133 GC samples and 46 normal tissues, and thus hold potential for novel diagnostic candidates for GC. Furthermore, we found two co-expressed gene modules were significantly associated with poor survival outcomes revealed by survival analysis of the RNA-Seq TCGA datasets. We identified STAT6 (signal transducer and activator of transcription 6) as a key regulator of the identified gene modules. Finally, potential therapeutic drugs that may target and reverse the expression of the identified altered gene modules examined for drug repurposing analyses and the unraveled compounds were further investigated in the literature by the text mining method. Accordingly, we found several repurposed drug candidates, including Trichostatin A, Vorinostat, Parthenolide, Panobinostat, Brefeldin A, Belinostat, and Danusertib. Through text mining analysis and literature search validation, Belinostat and Danusertib were suggested as possible novel drug candidates for GC treatment. These findings collectively inform multiple aspects of GC medical management, including its precision diagnosis, forecasting of possible outcomes, and drug repurposing for innovation in GC medicines in the future.
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Affiliation(s)
- Talip Yasir Demirtas
- Department of Bioengineering, Faculty of Engineering, Adana Alparslan Turkes Science and Technology University, Adana, Turkey
| | - Md Rezanur Rahman
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia
| | - Merve Capkin Yurtsever
- Department of Bioengineering, Faculty of Engineering, Adana Alparslan Turkes Science and Technology University, Adana, Turkey
| | - Esra Gov
- Department of Bioengineering, Faculty of Engineering, Adana Alparslan Turkes Science and Technology University, Adana, Turkey
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Saha C, Laha S, Chatterjee R, Bhattacharyya NP. Co-Regulation of Protein Coding Genes by Transcription Factor and Long Non-Coding RNA in SARS-CoV-2 Infected Cells: An In Silico Analysis. Noncoding RNA 2021; 7:74. [PMID: 34940755 PMCID: PMC8708613 DOI: 10.3390/ncrna7040074] [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: 10/15/2021] [Revised: 11/15/2021] [Accepted: 11/19/2021] [Indexed: 12/14/2022] Open
Abstract
Altered expression of protein coding gene (PCG) and long non-coding RNA (lncRNA) have been identified in SARS-CoV-2 infected cells and tissues from COVID-19 patients. The functional role and mechanism (s) of transcriptional regulation of deregulated genes in COVID-19 remain largely unknown. In the present communication, reanalyzing publicly available gene expression data, we observed that 66 lncRNA and 5491 PCG were deregulated in more than one experimental condition. Combining our earlier published results and using different publicly available resources, it was observed that 72 deregulated lncRNA interacted with 3228 genes/proteins. Many targets of deregulated lncRNA could also interact with SARS-CoV-2 coded proteins, modulated by IFN treatment and identified in CRISPR screening to modulate SARS-CoV-2 infection. The majority of the deregulated lncRNA and PCG were targets of at least one of the transcription factors (TFs), interferon responsive factors (IRFs), signal transducer, and activator of transcription (STATs), NFκB, MYC, and RELA/p65. Deregulated 1069 PCG was joint targets of lncRNA and TF. These joint targets are significantly enriched with pathways relevant for SARS-CoV-2 infection indicating that joint regulation of PCG could be one of the mechanisms for deregulation. Over all this manuscript showed possible involvement of lncRNA and mechanisms of deregulation of PCG in the pathogenesis of COVID-19.
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Affiliation(s)
- Chinmay Saha
- Department of Genome Science, School of Interdisciplinary Studies, University of Kalyani, Nadia 741235, India;
| | - Sayantan Laha
- Human Genetics Unit, Indian Statistical Institute, 203 B. T. Road, Kolkata 700108, India; (S.L.); (R.C.)
| | - Raghunath Chatterjee
- Human Genetics Unit, Indian Statistical Institute, 203 B. T. Road, Kolkata 700108, India; (S.L.); (R.C.)
| | - Nitai P. Bhattacharyya
- Department of Endocrinology and Metabolism, Institute of Post Graduate Medical Education & Research and Seth Sukhlal Karnani Memorial Hospital, Kolkata 700020, India
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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: 4] [Impact Index Per Article: 1.3] [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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Ghosh N, Saha I, Sharma N. Interactome of human and SARS-CoV-2 proteins to identify human hub proteins associated with comorbidities. Comput Biol Med 2021; 138:104889. [PMID: 34655901 PMCID: PMC8492901 DOI: 10.1016/j.compbiomed.2021.104889] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 09/22/2021] [Accepted: 09/22/2021] [Indexed: 02/06/2023]
Abstract
SARS-CoV-2 has a higher chance of progression in adults of any age with certain underlying health conditions or comorbidities like cancer, neurological diseases and in certain cases may even lead to death. Like other viruses, SARS-CoV-2 also interacts with host proteins to pave its entry into host cells. Therefore, to understand the behaviour of SARS-CoV-2 and design of effective antiviral drugs, host-virus protein-protein interactions (PPIs) can be very useful. In this regard, we have initially created a human-SARS-CoV-2 PPI database from existing works in the literature which has resulted in 7085 unique PPIs. Subsequently, we have identified at most 10 proteins with highest degrees viz. hub proteins from interacting human proteins for individual virus protein. The identification of these hub proteins is important as they are connected to most of the other human proteins. Consequently, when they get affected, the potential diseases are triggered in the corresponding pathways, thereby leading to comorbidities. Furthermore, the biological significance of the identified hub proteins is shown using KEGG pathway and GO enrichment analysis. KEGG pathway analysis is also essential for identifying the pathways leading to comorbidities. Among others, SARS-CoV-2 proteins viz. NSP2, NSP5, Envelope and ORF10 interacting with human hub proteins like COX4I1, COX5A, COX5B, NDUFS1, CANX, HSP90AA1 and TP53 lead to comorbidities. Such comorbidities are Alzheimer, Parkinson, Huntington, HTLV-1 infection, prostate cancer and viral carcinogenesis. Subsequently, using Enrichr tool possible repurposable drugs which target the human hub proteins are reported in this paper as well. Therefore, this work provides a consolidated study for human-SARS-CoV-2 protein interactions to understand the relationship between comorbidity and hub proteins so that it may pave the way for the development of anti-viral drugs.
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Affiliation(s)
- Nimisha Ghosh
- Department of Computer Science and Information Technology, Institute of Technical Education and Research, Siksha 'O' Anusandhan (Deemed to Be University), Bhubaneswar, Odisha, India; Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw, Poland
| | - Indrajit Saha
- Department of Computer Science and Engineering, National Institute of Technical Teachers' Training and Research, Kolkata, West Bengal, India.
| | - Nikhil Sharma
- Department of Electronics and Communication Engineering, Jaypee Institute of Information Technology, Noida, Uttar Pradesh, India
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