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Mosharaf MP, Alam K, Gow J, Mahumud RA. Exploration of key drug target proteins highlighting their related regulatory molecules, functional pathways and drug candidates associated with delirium: evidence from meta-data analyses. BMC Geriatr 2023; 23:767. [PMID: 37993790 PMCID: PMC10666371 DOI: 10.1186/s12877-023-04457-1] [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: 07/19/2023] [Accepted: 11/04/2023] [Indexed: 11/24/2023] Open
Abstract
BACKGROUND Delirium is a prevalent neuropsychiatric medical phenomenon that causes serious emergency outcomes, including mortality and morbidity. It also increases the suffering and the economic burden for families and carers. Unfortunately, the pathophysiology of delirium is still unknown, which is a major obstacle to therapeutic development. The modern network-based system biology and multi-omics analysis approach has been widely used to recover the key drug target biomolecules and signaling pathways associated with disease pathophysiology. This study aimed to identify the major drug target hub-proteins associated with delirium, their regulatory molecules with functional pathways, and repurposable drug candidates for delirium treatment. METHODS We used a comprehensive proteomic seed dataset derived from a systematic literature review and the Comparative Toxicogenomics Database (CTD). An integrated multi-omics network-based bioinformatics approach was utilized in this study. The STRING database was used to construct the protein-protein interaction (PPI) network. The gene set enrichment and signaling pathways analysis, the regulatory transcription factors and microRNAs were conducted using delirium-associated genes. Finally, hub-proteins associated repurposable drugs were retrieved from CMap database. RESULTS We have distinguished 11 drug targeted hub-proteins (MAPK1, MAPK3, TP53, JUN, STAT3, SRC, RELA, AKT1, MAPK14, HSP90AA1 and DLG4), 5 transcription factors (FOXC1, GATA2, YY1, TFAP2A and SREBF1) and 6 microRNA (miR-375, miR-17-5, miR-17-5p, miR-106a-5p, miR-125b-5p, and miR-125a-5p) associated with delirium. The functional enrichment and pathway analysis revealed the cytokines, inflammation, postoperative pain, oxidative stress-associated pathways, developmental biology, shigellosis and cellular senescence which are closely connected with delirium development and the hallmarks of aging. The hub-proteins associated computationally identified repurposable drugs were retrieved from database. The predicted drug molecules including aspirin, irbesartan, ephedrine-(racemic), nedocromil, and guanidine were characterized as anti-inflammatory, stimulating the central nervous system, neuroprotective medication based on the existing literatures. The drug molecules may play an important role for therapeutic development against delirium if they are investigated more extensively through clinical trials and various wet lab experiments. CONCLUSION This study could possibly help future research on investigating the delirium-associated therapeutic target biomarker hub-proteins and repurposed drug compounds. These results will also aid understanding of the molecular mechanisms that underlie the pathophysiology of delirium onset and molecular function.
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Affiliation(s)
- Md Parvez Mosharaf
- School of Business, Faculty of Business, Education, Law and Arts, University of Southern Queensland, Toowoomba, QLD, 4350, Australia.
- Bioinformatics Lab, Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh.
| | - Khorshed Alam
- School of Business, Faculty of Business, Education, Law and Arts, University of Southern Queensland, Toowoomba, QLD, 4350, Australia
| | - Jeff Gow
- School of Business, Faculty of Business, Education, Law and Arts, University of Southern Queensland, Toowoomba, QLD, 4350, Australia
- School of Accounting, Economics and Finance, University of KwaZulu-Natal, Durban, 4000, South Africa
| | - Rashidul Alam Mahumud
- NHMRC Clinical Trials Centre, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW, 2006, Australia
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Taylor K, Pearson M, Das S, Sardell J, Chocian K, Gardner S. Genetic risk factors for severe and fatigue dominant long COVID and commonalities with ME/CFS identified by combinatorial analysis. J Transl Med 2023; 21:775. [PMID: 37915075 PMCID: PMC10621206 DOI: 10.1186/s12967-023-04588-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 10/03/2023] [Indexed: 11/03/2023] Open
Abstract
BACKGROUND Long COVID is a debilitating chronic condition that has affected over 100 million people globally. It is characterized by a diverse array of symptoms, including fatigue, cognitive dysfunction and respiratory problems. Studies have so far largely failed to identify genetic associations, the mechanisms behind the disease, or any common pathophysiology with other conditions such as myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) that present with similar symptoms. METHODS We used a combinatorial analysis approach to identify combinations of genetic variants significantly associated with the development of long COVID and to examine the biological mechanisms underpinning its various symptoms. We compared two subpopulations of long COVID patients from Sano Genetics' Long COVID GOLD study cohort, focusing on patients with severe or fatigue dominant phenotypes. We evaluated the genetic signatures previously identified in an ME/CFS population against this long COVID population to understand similarities with other fatigue disorders that may be triggered by a prior viral infection. Finally, we also compared the output of this long COVID analysis against known genetic associations in other chronic diseases, including a range of metabolic and neurological disorders, to understand the overlap of pathophysiological mechanisms. RESULTS Combinatorial analysis identified 73 genes that were highly associated with at least one of the long COVID populations included in this analysis. Of these, 9 genes have prior associations with acute COVID-19, and 14 were differentially expressed in a transcriptomic analysis of long COVID patients. A pathway enrichment analysis revealed that the biological pathways most significantly associated with the 73 long COVID genes were mainly aligned with neurological and cardiometabolic diseases. Expanded genotype analysis suggests that specific SNX9 genotypes are a significant contributor to the risk of or protection against severe long COVID infection, but that the gene-disease relationship is context dependent and mediated by interactions with KLF15 and RYR3. Comparison of the genes uniquely associated with the Severe and Fatigue Dominant long COVID patients revealed significant differences between the pathways enriched in each subgroup. The genes unique to Severe long COVID patients were associated with immune pathways such as myeloid differentiation and macrophage foam cells. Genes unique to the Fatigue Dominant subgroup were enriched in metabolic pathways such as MAPK/JNK signaling. We also identified overlap in the genes associated with Fatigue Dominant long COVID and ME/CFS, including several involved in circadian rhythm regulation and insulin regulation. Overall, 39 SNPs associated in this study with long COVID can be linked to 9 genes identified in a recent combinatorial analysis of ME/CFS patient from UK Biobank. Among the 73 genes associated with long COVID, 42 are potentially tractable for novel drug discovery approaches, with 13 of these already targeted by drugs in clinical development pipelines. From this analysis for example, we identified TLR4 antagonists as repurposing candidates with potential to protect against long term cognitive impairment pathology caused by SARS-CoV-2. We are currently evaluating the repurposing potential of these drug targets for use in treating long COVID and/or ME/CFS. CONCLUSION This study demonstrates the power of combinatorial analytics for stratifying heterogeneous populations in complex diseases that do not have simple monogenic etiologies. These results build upon the genetic findings from combinatorial analyses of severe acute COVID-19 patients and an ME/CFS population and we expect that access to additional independent, larger patient datasets will further improve the disease insights and validate potential treatment options in long COVID.
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Affiliation(s)
- Krystyna Taylor
- PrecisionLife Ltd, Unit 8B Bankside, Hanborough Business Park, Oxford, OX29 8LJ, UK
| | - Matthew Pearson
- PrecisionLife Ltd, Unit 8B Bankside, Hanborough Business Park, Oxford, OX29 8LJ, UK
| | - Sayoni Das
- PrecisionLife Ltd, Unit 8B Bankside, Hanborough Business Park, Oxford, OX29 8LJ, UK
| | - Jason Sardell
- PrecisionLife Ltd, Unit 8B Bankside, Hanborough Business Park, Oxford, OX29 8LJ, UK
| | - Karolina Chocian
- PrecisionLife Ltd, Unit 8B Bankside, Hanborough Business Park, Oxford, OX29 8LJ, UK
| | - Steve Gardner
- PrecisionLife Ltd, Unit 8B Bankside, Hanborough Business Park, Oxford, OX29 8LJ, UK.
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Rahman T, Chowdhury MEH, Khandakar A, Mahbub ZB, Hossain MSA, Alhatou A, Abdalla E, Muthiyal S, Islam KF, Kashem SBA, Khan MS, Zughaier SM, Hossain M. BIO-CXRNET: a robust multimodal stacking machine learning technique for mortality risk prediction of COVID-19 patients using chest X-ray images and clinical data. Neural Comput Appl 2023; 35:1-23. [PMID: 37362565 PMCID: PMC10157130 DOI: 10.1007/s00521-023-08606-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 04/11/2023] [Indexed: 06/28/2023]
Abstract
Nowadays, quick, and accurate diagnosis of COVID-19 is a pressing need. This study presents a multimodal system to meet this need. The presented system employs a machine learning module that learns the required knowledge from the datasets collected from 930 COVID-19 patients hospitalized in Italy during the first wave of COVID-19 (March-June 2020). The dataset consists of twenty-five biomarkers from electronic health record and Chest X-ray (CXR) images. It is found that the system can diagnose low- or high-risk patients with an accuracy, sensitivity, and F1-score of 89.03%, 90.44%, and 89.03%, respectively. The system exhibits 6% higher accuracy than the systems that employ either CXR images or biomarker data. In addition, the system can calculate the mortality risk of high-risk patients using multivariate logistic regression-based nomogram scoring technique. Interested physicians can use the presented system to predict the early mortality risks of COVID-19 patients using the web-link: Covid-severity-grading-AI. In this case, a physician needs to input the following information: CXR image file, Lactate Dehydrogenase (LDH), Oxygen Saturation (O2%), White Blood Cells Count, C-reactive protein, and Age. This way, this study contributes to the management of COVID-19 patients by predicting early mortality risk. Supplementary Information The online version contains supplementary material available at 10.1007/s00521-023-08606-w.
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Affiliation(s)
- Tawsifur Rahman
- Department of Electrical Engineering, Qatar University, P.O. Box 2713, Doha, Qatar
| | | | - Amith Khandakar
- Department of Electrical Engineering, Qatar University, P.O. Box 2713, Doha, Qatar
| | - Zaid Bin Mahbub
- Department of Physics and Mathematics, North South University, Dhaka, 1229 Bangladesh
| | | | - Abraham Alhatou
- Department of Biology, University of South Carolina (USC), Columbia, SC 29208 USA
| | - Eynas Abdalla
- Anesthesia Department, Hamad General Hospital, P.O. Box 3050, Doha, Qatar
| | - Sreekumar Muthiyal
- Department of Radiology, Hamad General Hospital, P.O. Box 3050, Doha, Qatar
| | | | - Saad Bin Abul Kashem
- Department of Computer Science, AFG College with the University of Aberdeen, Doha, Qatar
| | - Muhammad Salman Khan
- Department of Electrical Engineering, Qatar University, P.O. Box 2713, Doha, Qatar
| | - Susu M. Zughaier
- Department of Basic Medical Sciences, College of Medicine, QU Health, Qatar University, P.O. Box 2713, Doha, Qatar
| | - Maqsud Hossain
- NSU Genome Research Institute (NGRI), North South University, Dhaka, 1229 Bangladesh
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Deng X, Luo Y, Guan T, Guo X. Identification of the Genetic Influence of SARS-CoV-2 Infections on IgA Nephropathy Based on Bioinformatics Method. Kidney Blood Press Res 2023; 48:367-384. [PMID: 37040729 PMCID: PMC10308545 DOI: 10.1159/000529687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 02/09/2023] [Indexed: 04/13/2023] Open
Abstract
INTRODUCTION Coronavirus disease-2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection. It was initially detected in Wuhan, China, in December 2019. In March 2020, the World Health Organization (WHO) declared COVID-19 a global pandemic. Compared to healthy individuals, patients with IgA nephropathy (IgAN) are at a higher risk of SARS-CoV-2 infection. However, the potential mechanisms remain unclear. This study explores the underlying molecular mechanisms and therapeutic agents for the management of IgAN and COVID-19 using the bioinformatics and system biology way. METHODS We first downloaded GSE73953 and GSE164805 from the Gene Expression Omnibus (GEO) database to obtain common differentially expressed genes (DEGs). Then, we performed the functional enrichment analysis, pathway analysis, protein-protein interaction (PPI) analysis, gene regulatory networks analysis, and potential drug analysis on these common DEGs. RESULTS We acquired 312 common DEGs from the IgAN and COVID-19 datasets and used various bioinformatics tools and statistical analyses to construct the PPI network to extract hub genes. Besides, we performed gene ontology (GO) and pathway analyses to reveal the common correlation between IgAN and COVID-19. Finally, on the basis of common DEGs, we determined the interactions between DEGs-miRNAs, the transcription factor-genes (TFs-genes), protein-drug, and gene-disease networks. CONCLUSION We successfully identified hub genes that may act as biomarkers of COVID-19 and IgAN and also screened out some potential drugs to provide new ideas for COVID-19 and IgAN treatment.
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Affiliation(s)
- Xiaoqi Deng
- Department of Nephrology, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Yu Luo
- School of Medicine, Xiamen University, Xiamen, China
| | - Tianjun Guan
- Department of Nephrology, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Xiaodan Guo
- Department of Nephrology, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
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Islam MA, Kibria MK, Hossen MB, Reza MS, Tasmia SA, Tuly KF, Mosharof MP, Kabir SR, Kabir MH, Mollah MNH. Bioinformatics-based investigation on the genetic influence between SARS-CoV-2 infections and idiopathic pulmonary fibrosis (IPF) diseases, and drug repurposing. Sci Rep 2023; 13:4685. [PMID: 36949176 PMCID: PMC10031699 DOI: 10.1038/s41598-023-31276-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2022] [Accepted: 03/09/2023] [Indexed: 03/24/2023] Open
Abstract
Some recent studies showed that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and idiopathic pulmonary fibrosis (IPF) disease might stimulate each other through the shared genes. Therefore, in this study, an attempt was made to explore common genomic biomarkers for SARS-CoV-2 infections and IPF disease highlighting their functions, pathways, regulators and associated drug molecules. At first, we identified 32 statistically significant common differentially expressed genes (cDEGs) between disease (SARS-CoV-2 and IPF) and control samples of RNA-Seq profiles by using a statistical r-package (edgeR). Then we detected 10 cDEGs (CXCR4, TNFAIP3, VCAM1, NLRP3, TNFAIP6, SELE, MX2, IRF4, UBD and CH25H) out of 32 as the common hub genes (cHubGs) by the protein-protein interaction (PPI) network analysis. The cHubGs regulatory network analysis detected few key TFs-proteins and miRNAs as the transcriptional and post-transcriptional regulators of cHubGs. The cDEGs-set enrichment analysis identified some crucial SARS-CoV-2 and IPF causing common molecular mechanisms including biological processes, molecular functions, cellular components and signaling pathways. Then, we suggested the cHubGs-guided top-ranked 10 candidate drug molecules (Tegobuvir, Nilotinib, Digoxin, Proscillaridin, Simeprevir, Sorafenib, Torin 2, Rapamycin, Vancomycin and Hesperidin) for the treatment against SARS-CoV-2 infections with IFP diseases as comorbidity. Finally, we investigated the resistance performance of our proposed drug molecules compare to the already published molecules, against the state-of-the-art alternatives publicly available top-ranked independent receptors by molecular docking analysis. Molecular docking results suggested that our proposed drug molecules would be more effective compare to the already published drug molecules. Thus, the findings of this study might be played a vital role for diagnosis and therapies of SARS-CoV-2 infections with IPF disease as comorbidity risk.
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Affiliation(s)
- Md Ariful Islam
- Bioinformatics Lab(Dry), Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh
| | - Md Kaderi Kibria
- Bioinformatics Lab(Dry), Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh
| | - Md Bayazid Hossen
- Bioinformatics Lab(Dry), Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh
| | - Md Selim Reza
- Bioinformatics Lab(Dry), Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh
| | - Samme Amena Tasmia
- Bioinformatics Lab(Dry), Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh
| | - Khanis Farhana Tuly
- Bioinformatics Lab(Dry), Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh
| | - Md Parvez Mosharof
- Bioinformatics Lab(Dry), Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh
- School of Business, University of Southern Queensland, Toowoomba, QLD, 4350, Australia
| | - Syed Rashel Kabir
- Department of Biochemistry and Molecular Biology, University of Rajshahi, Rajshahi, 6205, Bangladesh
| | - Md Hadiul Kabir
- Bioinformatics Lab(Dry), Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh
| | - Md Nurul Haque Mollah
- Bioinformatics Lab(Dry), Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh.
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6
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Patel MA, Knauer MJ, Nicholson M, Daley M, Van Nynatten LR, Cepinskas G, Fraser DD. Organ and cell-specific biomarkers of Long-COVID identified with targeted proteomics and machine learning. Mol Med 2023; 29:26. [PMID: 36809921 PMCID: PMC9942653 DOI: 10.1186/s10020-023-00610-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 01/13/2023] [Indexed: 02/24/2023] Open
Abstract
BACKGROUND Survivors of acute COVID-19 often suffer prolonged, diffuse symptoms post-infection, referred to as "Long-COVID". A lack of Long-COVID biomarkers and pathophysiological mechanisms limits effective diagnosis, treatment and disease surveillance. We performed targeted proteomics and machine learning analyses to identify novel blood biomarkers of Long-COVID. METHODS A case-control study comparing the expression of 2925 unique blood proteins in Long-COVID outpatients versus COVID-19 inpatients and healthy control subjects. Targeted proteomics was accomplished with proximity extension assays, and machine learning was used to identify the most important proteins for identifying Long-COVID patients. Organ system and cell type expression patterns were identified with Natural Language Processing (NLP) of the UniProt Knowledgebase. RESULTS Machine learning analysis identified 119 relevant proteins for differentiating Long-COVID outpatients (Bonferonni corrected P < 0.01). Protein combinations were narrowed down to two optimal models, with nine and five proteins each, and with both having excellent sensitivity and specificity for Long-COVID status (AUC = 1.00, F1 = 1.00). NLP expression analysis highlighted the diffuse organ system involvement in Long-COVID, as well as the involved cell types, including leukocytes and platelets, as key components associated with Long-COVID. CONCLUSIONS Proteomic analysis of plasma from Long-COVID patients identified 119 highly relevant proteins and two optimal models with nine and five proteins, respectively. The identified proteins reflected widespread organ and cell type expression. Optimal protein models, as well as individual proteins, hold the potential for accurate diagnosis of Long-COVID and targeted therapeutics.
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Affiliation(s)
- Maitray A Patel
- Epidemiology and Biostatistics, Western University, London, ON, N6A 3K7, Canada
| | - Michael J Knauer
- Pathology and Laboratory Medicine, Western University, London, ON, N6A 3K7, Canada
| | | | - Mark Daley
- Epidemiology and Biostatistics, Western University, London, ON, N6A 3K7, Canada.,Computer Science, Western University, London, ON, N6A 3K7, Canada
| | | | - Gediminas Cepinskas
- Lawson Health Research Institute, London, ON, N6C 2R5, Canada.,Medical Biophysics, Western University, London, ON, N6A 3K7, Canada
| | - Douglas D Fraser
- Lawson Health Research Institute, London, ON, N6C 2R5, Canada. .,Children's Health Research Institute, London, ON, N6C 4V3, Canada. .,Pediatrics, Western University, London, ON, N6A 3K7, Canada. .,Clinical Neurological Sciences, Western University, London, ON, N6A 3K7, Canada. .,Physiology and Pharmacology, Western University, London, ON, N6A 3K7, Canada. .,Room C2-C82, London Health Sciences Centre, 800 Commissioners Road East, London, ON, N6A 5W9, Canada.
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Omit SBS, Akhter S, Rana HK, Rana ARMMH, Podder NK, Rakib MI, Nobi A. Identification of Comorbidities, Genomic Associations, and Molecular Mechanisms for COVID-19 Using Bioinformatics Approaches. BIOMED RESEARCH INTERNATIONAL 2023; 2023:6996307. [PMID: 36685671 PMCID: PMC9848821 DOI: 10.1155/2023/6996307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 12/09/2022] [Accepted: 12/20/2022] [Indexed: 01/13/2023]
Abstract
Several studies have been done to identify comorbidities of COVID-19. In this work, we developed an analytical bioinformatics framework to reveal COVID-19 comorbidities, their genomic associations, and molecular mechanisms accomplishing transcriptomic analyses of the RNA-seq datasets provided by the Gene Expression Omnibus (GEO) database, where normal and infected tissues were evaluated. Using the framework, we identified 27 COVID-19 correlated diseases out of 7,092 collected diseases. Analyzing clinical and epidemiological research, we noticed that our identified 27 diseases are associated with COVID-19, where hypertension, diabetes, obesity, and lung cancer are observed several times in COVID-19 patients. Therefore, we selected the above four diseases and performed assorted analyses to demonstrate the association between COVID-19 and hypertension, diabetes, obesity, and lung cancer as comorbidities. We investigated genomic associations with the cross-comparative analysis and Jaccard's similarity index, identifying shared differentially expressed genes (DEGs) and linking DEGs of COVID-19 and the comorbidities, in which we identified hypertension as the most associated illness. We also revealed molecular mechanisms by identifying statistically significant ten pathways and ten ontologies. Moreover, to understand cellular physiology, we did protein-protein interaction (PPI) analyses among the comorbidities and COVID-19. We also used the degree centrality method and identified ten biomarker hub proteins (IL1B, CXCL8, FN1, MMP9, CXCL10, IL1A, IRF7, VWF, CXCL9, and ISG15) that associate COVID-19 with the comorbidities. Finally, we validated our findings by searching the published literature. Thus, our analytical approach elicited interconnections between COVID-19 and the aforementioned comorbidities in terms of remarkable DEGs, pathways, ontologies, PPI, and biomarker hub proteins.
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Affiliation(s)
- Shudeb Babu Sen Omit
- Department of Computer Science and Telecommunication Engineering, Noakhali Science and Technology University, Noakhali 3814, Bangladesh
| | - Salma Akhter
- Department of Environmental Science and Disaster Management, Noakhali Science and Technology University, Noakhali 3814, Bangladesh
| | - Humayan Kabir Rana
- Department of Computer Science and Engineering, Green University of Bangladesh, Dhaka 1207, Bangladesh
| | - A. R. M. Mahamudul Hasan Rana
- Department of Computer Science and Telecommunication Engineering, Noakhali Science and Technology University, Noakhali 3814, Bangladesh
| | - Nitun Kumar Podder
- Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna 9203, Bangladesh
| | - Mahmudul Islam Rakib
- Department of Computer Science and Telecommunication Engineering, Noakhali Science and Technology University, Noakhali 3814, Bangladesh
| | - Ashadun Nobi
- Department of Computer Science and Telecommunication Engineering, Noakhali Science and Technology University, Noakhali 3814, Bangladesh
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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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Pati SK, Gupta MK, Shai R, Banerjee A, Ghosh A. Missing value estimation of microarray data using Sim-GAN. Knowl Inf Syst 2022. [DOI: 10.1007/s10115-022-01718-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
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10
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Mosharaf MP, Reza MS, Gov E, Mahumud RA, Mollah MNH. Disclosing Potential Key Genes, Therapeutic Targets and Agents for Non-Small Cell Lung Cancer: Evidence from Integrative Bioinformatics Analysis. Vaccines (Basel) 2022; 10:vaccines10050771. [PMID: 35632527 PMCID: PMC9143695 DOI: 10.3390/vaccines10050771] [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: 03/06/2022] [Revised: 05/07/2022] [Accepted: 05/08/2022] [Indexed: 12/10/2022] Open
Abstract
Non-small-cell lung cancer (NSCLC) is considered as one of the malignant cancers that causes premature death. The present study aimed to identify a few potential novel genes highlighting their functions, pathways, and regulators for diagnosis, prognosis, and therapies of NSCLC by using the integrated bioinformatics approaches. At first, we picked out 1943 DEGs between NSCLC and control samples by using the statistical LIMMA approach. Then we selected 11 DEGs (CDK1, EGFR, FYN, UBC, MYC, CCNB1, FOS, RHOB, CDC6, CDC20, and CHEK1) as the hub-DEGs (potential key genes) by the protein–protein interaction network analysis of DEGs. The DEGs and hub-DEGs regulatory network analysis commonly revealed four transcription factors (FOXC1, GATA2, YY1, and NFIC) and five miRNAs (miR-335-5p, miR-26b-5p, miR-92a-3p, miR-155-5p, and miR-16-5p) as the key transcriptional and post-transcriptional regulators of DEGs as well as hub-DEGs. We also disclosed the pathogenetic processes of NSCLC by investigating the biological processes, molecular function, cellular components, and KEGG pathways of DEGs. The multivariate survival probability curves based on the expression of hub-DEGs in the SurvExpress web-tool and database showed the significant differences between the low- and high-risk groups, which indicates strong prognostic power of hub-DEGs. Then, we explored top-ranked 5-hub-DEGs-guided repurposable drugs based on the Connectivity Map (CMap) database. Out of the selected drugs, we validated six FDA-approved launched drugs (Dinaciclib, Afatinib, Icotinib, Bosutinib, Dasatinib, and TWS-119) by molecular docking interaction analysis with the respective target proteins for the treatment against NSCLC. The detected therapeutic targets and repurposable drugs require further attention by experimental studies to establish them as potential biomarkers for precision medicine in NSCLC treatment.
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Affiliation(s)
- Md. Parvez Mosharaf
- Bioinformatics Lab, Department of Statistics, University of Rajshahi, Rajshahi 6205, Bangladesh; (M.P.M.); (M.S.R.)
- School of Commerce, Faculty of Business, Education, Law and Arts, University of Southern Queensland, Toowoomba, QLD 4350, Australia
| | - Md. Selim Reza
- Bioinformatics Lab, Department of Statistics, University of Rajshahi, Rajshahi 6205, Bangladesh; (M.P.M.); (M.S.R.)
- Centre for High Performance Computing, Joint Engineering Research Centre for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Esra Gov
- Department of Bioengineering, Faculty of Engineering, Adana AlparslanTurkes Science and Technology University, Adana 01250, Turkey;
| | - Rashidul Alam Mahumud
- NHMRC Clinical Trials Centre, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2006, Australia;
| | - Md. Nurul Haque Mollah
- Bioinformatics Lab, Department of Statistics, University of Rajshahi, Rajshahi 6205, Bangladesh; (M.P.M.); (M.S.R.)
- Correspondence:
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11
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Ahmed FF, Reza MS, Sarker MS, Islam MS, Mosharaf MP, Hasan S, Mollah MNH. Identification of host transcriptome-guided repurposable drugs for SARS-CoV-1 infections and their validation with SARS-CoV-2 infections by using the integrated bioinformatics approaches. PLoS One 2022; 17:e0266124. [PMID: 35390032 PMCID: PMC8989220 DOI: 10.1371/journal.pone.0266124] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Accepted: 03/15/2022] [Indexed: 12/18/2022] Open
Abstract
Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is one of the most severe global pandemic due to its high pathogenicity and death rate starting from the end of 2019. Though there are some vaccines available against SAER-CoV-2 infections, we are worried about their effectiveness, due to its unstable sequence patterns. Therefore, beside vaccines, globally effective supporting drugs are also required for the treatment against SARS-CoV-2 infection. To explore commonly effective repurposable drugs for the treatment against different variants of coronavirus infections, in this article, an attempt was made to explore host genomic biomarkers guided repurposable drugs for SARS-CoV-1 infections and their validation with SARS-CoV-2 infections by using the integrated bioinformatics approaches. At first, we identified 138 differentially expressed genes (DEGs) between SARS-CoV-1 infected and control samples by analyzing high throughput gene-expression profiles to select drug target key receptors. Then we identified top-ranked 11 key DEGs (SMAD4, GSK3B, SIRT1, ATM, RIPK1, PRKACB, MED17, CCT2, BIRC3, ETS1 and TXN) as hub genes (HubGs) by protein-protein interaction (PPI) network analysis of DEGs highlighting their functions, pathways, regulators and linkage with other disease risks that may influence SARS-CoV-1 infections. The DEGs-set enrichment analysis significantly detected some crucial biological processes (immune response, regulation of angiogenesis, apoptotic process, cytokine production and programmed cell death, response to hypoxia and oxidative stress), molecular functions (transcription factor binding and oxidoreductase activity) and pathways (transcriptional mis-regulation in cancer, pathways in cancer, chemokine signaling pathway) that are associated with SARS-CoV-1 infections as well as SARS-CoV-2 infections by involving HubGs. The gene regulatory network (GRN) analysis detected some transcription factors (FOXC1, GATA2, YY1, FOXL1, TP53 and SRF) and micro-RNAs (hsa-mir-92a-3p, hsa-mir-155-5p, hsa-mir-106b-5p, hsa-mir-34a-5p and hsa-mir-19b-3p) as the key transcriptional and post- transcriptional regulators of HubGs, respectively. We also detected some chemicals (Valproic Acid, Cyclosporine, Copper Sulfate and arsenic trioxide) that may regulates HubGs. The disease-HubGs interaction analysis showed that our predicted HubGs are also associated with several other diseases including different types of lung diseases. Then we considered 11 HubGs mediated proteins and their regulatory 6 key TFs proteins as the drug target proteins (receptors) and performed their docking analysis with the SARS-CoV-2 3CL protease-guided top listed 90 anti-viral drugs out of 3410. We found Rapamycin, Tacrolimus, Torin-2, Radotinib, Danoprevir, Ivermectin and Daclatasvir as the top-ranked 7 candidate-drugs with respect to our proposed target proteins for the treatment against SARS-CoV-1 infections. Then, we validated these 7 candidate-drugs against the already published top-ranked 11 target proteins associated with SARS-CoV-2 infections by molecular docking simulation and found their significant binding affinity scores with our proposed candidate-drugs. Finally, we validated all of our findings by the literature review. Therefore, the proposed candidate-drugs might play a vital role for the treatment against different variants of SARS-CoV-2 infections with comorbidities, since the proposed HubGs are also associated with several comorbidities.
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Affiliation(s)
- Fee Faysal Ahmed
- Department of Mathematics, Jashore University of Science and Technology, Jashore, Bangladesh
- Bioinformatics Lab., Department of Statistics, Rajshahi University, Rajshahi, Bangladesh
| | - Md. Selim Reza
- Bioinformatics Lab., Department of Statistics, Rajshahi University, Rajshahi, Bangladesh
| | - Md. Shahin Sarker
- Department of Pharmacy, Jashore University of Science and Technology, Jashore, Bangladesh
| | - Md. Samiul Islam
- Department of Plant Pathology, Huazhong Agricultural University, Wuhan, Hubei Province, China
| | - Md. Parvez Mosharaf
- Bioinformatics Lab., Department of Statistics, Rajshahi University, Rajshahi, Bangladesh
| | - Sohel Hasan
- Department of Biochemistry and Molecular Biology, Rajshahi University, Rajshhi, Bangladesh
| | - Md. Nurul Haque Mollah
- Bioinformatics Lab., Department of Statistics, Rajshahi University, Rajshahi, Bangladesh
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12
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Computational identification of host genomic biomarkers highlighting their functions, pathways and regulators that influence SARS-CoV-2 infections and drug repurposing. Sci Rep 2022; 12:4279. [PMID: 35277538 PMCID: PMC8915158 DOI: 10.1038/s41598-022-08073-8] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2021] [Accepted: 02/15/2022] [Indexed: 12/13/2022] Open
Abstract
The pandemic threat of COVID-19 has severely destroyed human life as well as the economy around the world. Although, the vaccination has reduced the outspread, but people are still suffering due to the unstable RNA sequence patterns of SARS-CoV-2 which demands supplementary drugs. To explore novel drug target proteins, in this study, a transcriptomics RNA-Seq data generated from SARS-CoV-2 infection and control samples were analyzed. We identified 109 differentially expressed genes (DEGs) that were utilized to identify 10 hub-genes/proteins (TLR2, USP53, GUCY1A2, SNRPD2, NEDD9, IGF2, CXCL2, KLF6, PAG1 and ZFP36) by the protein–protein interaction (PPI) network analysis. The GO functional and KEGG pathway enrichment analyses of hub-DEGs revealed some important functions and signaling pathways that are significantly associated with SARS-CoV-2 infections. The interaction network analysis identified 5 TFs proteins and 6 miRNAs as the key regulators of hub-DEGs. Considering 10 hub-proteins and 5 key TFs-proteins as drug target receptors, we performed their docking analysis with the SARS-CoV-2 3CL protease-guided top listed 90 FDA approved drugs. We found Torin-2, Rapamycin, Radotinib, Ivermectin, Thiostrepton, Tacrolimus and Daclatasvir as the top ranked seven candidate drugs. We investigated their resistance performance against the already published COVID-19 causing top-ranked 11 independent and 8 protonated receptor proteins by molecular docking analysis and found their strong binding affinities, which indicates that the proposed drugs are effective against the state-of-the-arts alternatives independent receptor proteins also. Finally, we investigated the stability of top three drugs (Torin-2, Rapamycin and Radotinib) by using 100 ns MD-based MM-PBSA simulations with the two top-ranked proposed receptors (TLR2, USP53) and independent receptors (IRF7, STAT1), and observed their stable performance. Therefore, the proposed drugs might play a vital role for the treatment against different variants of SARS-CoV-2 infections.
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13
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Fu L, Yao M, Liu X, Zheng D. Using bioinformatics and systems biology to discover common pathogenetic processes between sarcoidosis and COVID-19. GENE REPORTS 2022; 27:101597. [PMID: 35317263 PMCID: PMC8931993 DOI: 10.1016/j.genrep.2022.101597] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Revised: 01/29/2022] [Accepted: 03/14/2022] [Indexed: 11/21/2022]
Abstract
The coronavirus disease (COVID-19) pandemic caused by SARS-CoV-2 is ongoing. Individuals with sarcoidosis tend to develop severe COVID-19; however, the underlying pathological mechanisms remain elusive. To determine common transcriptional signatures and pathways between sarcoidosis and COVID-19, we investigated the whole-genome transcriptome of peripheral blood mononuclear cells (PBMCs) from patients with COVID-19 and sarcoidosis and conducted bioinformatic analysis, including gene ontology and pathway enrichment, protein-protein interaction (PPI) network, and gene regulatory network (GRN) construction. We identified 33 abnormally expressed genes that were common between COVID-19 and sarcoidosis. Functional enrichment analysis showed that these differentially expressed genes were associated with cytokine production involved in the immune response and T cell cytokine production. We identified several hub genes from the PPI network encoded by the common genes. These hub genes have high diagnostic potential for COVID-19 and sarcoidosis and can be potential biomarkers. Moreover, GRN analysis identified important microRNAs and transcription factors that regulate the common genes. This study provides a novel characterization of the transcriptional signatures and biological processes commonly dysregulated in sarcoidosis and COVID-19 and identified several critical regulators and biomarkers. This study highlights a potential pathological association between COVID-19 and sarcoidosis, establishing a theoretical basis for future clinical trials.
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Du L, Xiao Y, Xu Y, Chen F, Chu X, Cao Y, Zhang X. The Potential Bioactive Components of Nine TCM Prescriptions Against COVID-19 in Lung Cancer Were Explored Based on Network Pharmacology and Molecular Docking. Front Med (Lausanne) 2022; 8:813119. [PMID: 35127768 PMCID: PMC8811133 DOI: 10.3389/fmed.2021.813119] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 12/13/2021] [Indexed: 01/08/2023] Open
Abstract
Objective The purpose of this study was to screen active components and molecular targets of nine prescriptions recommended by the National Health Commission (NHC) of China by network pharmacology, and to explore the potential mechanism of the core active components against COVID-19 with molecular docking. Methods Differentially expressed genes of lung adenocarcinoma (LUAD) screened by edgeR analysis were overlapped with immune-related genes in MMPORT and COVID-19-related genes in GeneCards. The overlapped genes were also COVID-19 immune-related genes in LUAD. TCMSP platform was used to identify active ingredients of the prescription, potential targets were identified by the UniProt database, and the cross genes with COVID-19 immune-related genes in LUAD were used to construct a Chinese Medicine-Logy-immune target network. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on the target genes of each prescription. Finally, the key active components were selected for molecular docking simulation with ACE2. Results We obtained 15 overlapping immunization target genes from FPQXZ, HSYFZ, HSZFZ, and QFPDT, 16 overlapping immunization target genes from QYLFZ, SDYFZ, SRYFZ, and YDBFZ, and 17 overlapping immunization target genes from QYLXZ. ADRB2, FOS, HMOX1, ICAM1, IL6, JUN, NFKBIA, and STAT1 also had the highest-ranked therapeutic targets for 9 prescriptions, and their expressions were positively correlated with TME-related stromal score, immune score, and ESTIMATE score. Among 9 compounds with the highest frequency of occurrence in the 9 prescriptions, baicalein had the highest ACE2 binding affinity and can be well-combined into the active pocket of ACE2 It is stabilized by forming hydrogen bonds with ASN290 and ILE291 in ACE2 and hydrophobic interaction with PHE438, ILE291, and PRO415. Conclusion The nine Chinese medicine prescriptions may play an anti-SARS-CoV-2 role via regulating viral transcription and immune function through multi-component, multi-target, and multi-pathway.
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Affiliation(s)
- Lin Du
- Department of Thoracic Surgery, Tianjin Chest Hospital, Tianjin, China
| | - Yajie Xiao
- Department of Clinical Translational Medicine, YuceBio Technology Co., Ltd., Shenzhen, China
| | - Yijun Xu
- Department of Thoracic Surgery, Tianjin Chest Hospital, Tianjin, China
| | - Feng Chen
- Department of Thoracic Surgery, Tianjin Chest Hospital, Tianjin, China
| | - Xianghui Chu
- Department of Thoracic Surgery, Tianjin Chest Hospital, Tianjin, China
| | - Yuqi Cao
- Department of Thoracic Surgery, Tianjin Chest Hospital, Tianjin, China
| | - Xun Zhang
- Department of Thoracic Surgery, Tianjin Chest Hospital, Tianjin, China
- *Correspondence: Xun Zhang
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15
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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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16
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Ganguli S, Howlader S, Dey K, Barua S, Islam MN, Aquib TI, Partho PB, Chakraborty RR, Barua B, Hawlader MDH, Biswas PK. Association of comorbidities with the COVID-19 severity and hospitalization: A study among the recovered individuals in Bangladesh. Int J Health Sci (Qassim) 2022; 16:30-45. [PMID: 35949693 PMCID: PMC9288138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
Objectives We aimed at the identification of the association of comorbidities with the COVID-19 severity and hospitalization. Methods It is a retrospective cross-sectional study to investigate the variation in age, sex, dwelling, comorbidities, and medication with the COVID-19 severity and hospitalization by enrolling 1025 recovered individuals while comparing their time of recovery with or without comorbidities. Results COVID-19 patients mostly suffered from fever. The predominant underlying medical conditions in them were hypertension (HTN) followed by diabetes mellitus (DM). Patients with cardiovascular disease (CVD) (54.3%) and hepatic disorders (HD) (43.6%) experienced higher severity. The risk of symptomatic cases was higher in aged (odds ratio, OR = 1.04, 95% CI = 1.02-1.06) and comorbid (OR = 1.87, 95% CI = 1.34-2.60) patients. T-test confirmed the differences between the comorbid and non-comorbid patients' recovery duration. The presence of multiple comorbidities increased the time of recovery (15-27 days) and hospitalization (20-40%). Increased symptomatic cases were found for patients having DM+HTN whereas CVD+Asthma patients were found with higher percentage of severity. Besides, DM+CKD (chronic kidney disease) was associated with higher hospitalization rate. Higher odds of severity were found for DM+CVD (OR = 4.42, 95% CI = 1.81-10.78) patients. Hospitalization risk was also increased for them (OR = 5.14, 95% CI = 2.02-13.07). Moreover, if they had HTN along with DM+CVD, they were found with even higher odds (OR = 6.82, 95% CI = 2.37-19.58) for hospitalization. Conclusion Our study indicates that people who are aged, females, living in urban area and have comorbid conditions are at a higher risk for developing COVID-19 severity. Clinicians and health management authorities should prioritize these high-risk groups to reduce mortality attributed to the disease.
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Affiliation(s)
- Sumon Ganguli
- Department of Applied Chemistry and Chemical Engineering, Faculty of Science, University of Chittagong, Chattogram-4331, Bangladesh,Address for correspondence: Sumon Ganguli, Biomaterials Research Laboratory, Department of Applied Chemistry and Chemical Engineering, Faculty of Science, University of Chittagong, Chattogram-4331, Bangladesh. E-mail:
| | - Sabbir Howlader
- Department of Applied Chemistry and Chemical Engineering, Faculty of Science, University of Chittagong, Chattogram-4331, Bangladesh
| | - Kamol Dey
- Department of Applied Chemistry and Chemical Engineering, Faculty of Science, University of Chittagong, Chattogram-4331, Bangladesh
| | - Suman Barua
- Department of Applied Chemistry and Chemical Engineering, Faculty of Science, University of Chittagong, Chattogram-4331, Bangladesh
| | - Md. Nazrul Islam
- Department of Applied Chemistry and Chemical Engineering, Faculty of Science, University of Chittagong, Chattogram-4331, Bangladesh,School of Pharmacy, The University of Queensland, Queensland, Australia
| | - Tahmidul Islam Aquib
- Department of Applied Chemistry and Chemical Engineering, Faculty of Science, University of Chittagong, Chattogram-4331, Bangladesh
| | - Prosenjit Biswas Partho
- Senior Medical Officer, Health Department, Ministry of Health and Family Welfare, Bangladesh
| | | | - Bidduth Barua
- Deputy Director, Chittagong Medical University, Chattogram, Bangladesh
| | | | - Paritosh Kumar Biswas
- Department of Microbiology and Veterinary Public Health, Chattogram Veterinary and Animal Sciences University, Khulshi, Chattogram, Bangladesh
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Ahamed KU, Islam M, Uddin A, Akhter A, Paul BK, Yousuf MA, Uddin S, Quinn JM, Moni MA. A deep learning approach using effective preprocessing techniques to detect COVID-19 from chest CT-scan and X-ray images. Comput Biol Med 2021; 139:105014. [PMID: 34781234 PMCID: PMC8566098 DOI: 10.1016/j.compbiomed.2021.105014] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 11/01/2021] [Accepted: 11/01/2021] [Indexed: 12/16/2022]
Abstract
Coronavirus disease-19 (COVID-19) is a severe respiratory viral disease first reported in late 2019 that has spread worldwide. Although some wealthy countries have made significant progress in detecting and containing this disease, most underdeveloped countries are still struggling to identify COVID-19 cases in large populations. With the rising number of COVID-19 cases, there are often insufficient COVID-19 diagnostic kits and related resources in such countries. However, other basic diagnostic resources often do exist, which motivated us to develop Deep Learning models to assist clinicians and radiologists to provide prompt diagnostic support to the patients. In this study, we have developed a deep learning-based COVID-19 case detection model trained with a dataset consisting of chest CT scans and X-ray images. A modified ResNet50V2 architecture was employed as deep learning architecture in the proposed model. The dataset utilized to train the model was collected from various publicly available sources and included four class labels: confirmed COVID-19, normal controls and confirmed viral and bacterial pneumonia cases. The aggregated dataset was preprocessed through a sharpening filter before feeding the dataset into the proposed model. This model attained an accuracy of 96.452% for four-class cases (COVID-19/Normal/Bacterial pneumonia/Viral pneumonia), 97.242% for three-class cases (COVID-19/Normal/Bacterial pneumonia) and 98.954% for two-class cases (COVID-19/Viral pneumonia) using chest X-ray images. The model acquired a comprehensive accuracy of 99.012% for three-class cases (COVID-19/Normal/Community-acquired pneumonia) and 99.99% for two-class cases (Normal/COVID-19) using CT-scan images of the chest. This high accuracy presents a new and potentially important resource to enable radiologists to identify and rapidly diagnose COVID-19 cases with only basic but widely available equipment.
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Affiliation(s)
- Khabir Uddin Ahamed
- Department of Computer Science and Engineering, Jagannath University, Dhaka, Bangladesh
| | - Manowarul Islam
- Department of Computer Science and Engineering, Jagannath University, Dhaka, Bangladesh,Corresponding author
| | - Ashraf Uddin
- Department of Computer Science and Engineering, Jagannath University, Dhaka, Bangladesh
| | - Arnisha Akhter
- Department of Computer Science and Engineering, Jagannath University, Dhaka, Bangladesh
| | - Bikash Kumar Paul
- Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Bangladesh
| | - Mohammad Abu Yousuf
- Institute of Information Technology, Jahangirnagar University, Dhaka, Bangladesh
| | - Shahadat Uddin
- Complex Systems Research Group, Faculty of Engineering, The University of Sydney, Darlington, NSW, 2008, Australia
| | - Julian M.W. Quinn
- Healthy Ageing Theme, Garvan Institute of Medical Research, Darlinghurst, NSW, 2010, Australia
| | - Mohammad Ali Moni
- Healthy Ageing Theme, Garvan Institute of Medical Research, Darlinghurst, NSW, 2010, Australia,Artificial Intelligence & Digital Health Data Science, School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland, St Lucia, QLD, 4072, Australia,Corresponding author. Artificial Intelligence & Digital Health Data Science, School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland, St Lucia, QLD, 4072, Australia
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18
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Nashiry MA, Sumi SS, Sharif Shohan MU, Alyami SA, Azad AKM, Moni MA. Bioinformatics and system biology approaches to identify the diseasome and comorbidities complexities of SARS-CoV-2 infection with the digestive tract disorders. Brief Bioinform 2021; 22:bbab126. [PMID: 33993223 PMCID: PMC8194728 DOI: 10.1093/bib/bbab126] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2021] [Revised: 03/16/2021] [Accepted: 03/16/2021] [Indexed: 01/08/2023] Open
Abstract
Coronavirus Disease 2019 (COVID-19), although most commonly demonstrates respiratory symptoms, but there is a growing set of evidence reporting its correlation with the digestive tract and faeces. Interestingly, recent studies have shown the association of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection with gastrointestinal symptoms in infected patients but any sign of respiratory issues. Moreover, some studies have also shown that the presence of live SARS-CoV-2 virus in the faeces of patients with COVID-19. Therefore, the pathophysiology of digestive symptoms associated with COVID-19 has raised a critical need for comprehensive investigative efforts. To address this issue we have developed a bioinformatics pipeline involving a system biological framework to identify the effects of SARS-CoV-2 messenger RNA expression on deciphering its association with digestive symptoms in COVID-19 positive patients. Using two RNA-seq datasets derived from COVID-19 positive patients with celiac (CEL), Crohn's (CRO) and ulcerative colitis (ULC) as digestive disorders, we have found a significant overlap between the sets of differentially expressed genes from SARS-CoV-2 exposed tissue and digestive tract disordered tissues, reporting 7, 22 and 13 such overlapping genes, respectively. Moreover, gene set enrichment analysis, comprehensive analyses of protein-protein interaction network, gene regulatory network, protein-chemical agent interaction network revealed some critical association between SARS-CoV-2 infection and the presence of digestive disorders. The infectome, diseasome and comorbidity analyses also discover the influences of the identified signature genes in other risk factors of SARS-CoV-2 infection to human health. We hope the findings from this pathogenetic analysis may reveal important insights in deciphering the complex interplay between COVID-19 and digestive disorders and underpins its significance in therapeutic development strategy to combat against COVID-19 pandemic.
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Affiliation(s)
- Md Asif Nashiry
- Department of Computer Science and Engineering, Jashore University of Science and Technology, Jashore, Bangladesh
| | - Shauli Sarmin Sumi
- Department of Computer Science and Engineering, Jashore University of Science and Technology, Jashore, Bangladesh
| | | | - Salem A Alyami
- Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi Arabia
| | - A K M Azad
- iThree Institute, Faculty of Science, University Technology of Sydney, Australia
| | - Mohammad Ali Moni
- WHO Collaborating Centre on eHealth, UNSW Digital Health, School of Public Health and Community Medicine, Faculty of Medicine, UNSW Sydney, Australia
- Healthy Ageing Theme, The Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia
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19
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Chowdhury UN, Faruqe MO, Mehedy M, Ahmad S, Islam MB, Shoombuatong W, Azad A, Moni MA. Effects of Bacille Calmette Guerin (BCG) vaccination during COVID-19 infection. Comput Biol Med 2021; 138:104891. [PMID: 34624759 PMCID: PMC8479467 DOI: 10.1016/j.compbiomed.2021.104891] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 09/21/2021] [Accepted: 09/21/2021] [Indexed: 12/16/2022]
Abstract
The coronavirus disease 2019 (COVID-19) is caused by the infection of highly contagious severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), also known as the novel coronavirus. In most countries, the containment of this virus spread is not controlled, which is driving the pandemic towards a more difficult phase. In this study, we investigated the impact of the Bacille Calmette Guerin (BCG) vaccination on the severity and mortality of COVID-19 by performing transcriptomic analyses of SARS-CoV-2 infected and BCG vaccinated samples in peripheral blood mononuclear cells (PBMC). A set of common differentially expressed genes (DEGs) were identified and seeded into their functional enrichment analyses via Gene Ontology (GO)-based functional terms and pre-annotated molecular pathways databases, and their Protein-Protein Interaction (PPI) network analysis. We further analysed the regulatory elements, possible comorbidities and putative drug candidates for COVID-19 patients who have not been BCG-vaccinated. Differential expression analyses of both BCG-vaccinated and COVID-19 infected samples identified 62 shared DEGs indicating their discordant expression pattern in their respected conditions compared to control. Next, PPI analysis of those DEGs revealed 10 hub genes, namely ITGB2, CXCL8, CXCL1, CCR2, IFNG, CCL4, PTGS2, ADORA3, TLR5 and CD33. Functional enrichment analyses found significantly enriched pathways/GO terms including cytokine activities, lysosome, IL-17 signalling pathway, TNF-signalling pathways. Moreover, a set of identified TFs, miRNAs and potential drug molecules were further investigated to assess their biological involvements in COVID-19 and their therapeutic possibilities. Findings showed significant genetic interactions between BCG vaccination and SARS-CoV-2 infection, suggesting an interesting prospect of the BCG vaccine in relation to the COVID-19 pandemic. We hope it may potentially trigger further research on this critical phenomenon to combat COVID-19 spread.
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Affiliation(s)
- Utpala Nanda Chowdhury
- Department of Computer Science and Engineering, University of Rajshahi, Rajshahi, Bangladesh
| | - Md Omar Faruqe
- Department of Computer Science and Engineering, University of Rajshahi, Rajshahi, Bangladesh
| | - Md Mehedy
- Department of Computer Science and Engineering, University of Rajshahi, Rajshahi, Bangladesh
| | - Shamim Ahmad
- Department of Computer Science and Engineering, University of Rajshahi, Rajshahi, Bangladesh
| | - M. Babul Islam
- Department of Electrical and Electronic Engineering, University of Rajshahi, Rajshahi, Bangladesh
| | - Watshara Shoombuatong
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
| | - A.K.M. Azad
- Faculty of Science, Engineering & Technology, Swinburne University of Technology Sydney, Australia
| | - Mohammad Ali Moni
- School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland, Brisbane, QLD 4072, Australia,Corresponding author
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20
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Islam MB, Chowdhury UN, Nain Z, Uddin S, Ahmed MB, Moni MA. Identifying molecular insight of synergistic complexities for SARS-CoV-2 infection with pre-existing type 2 diabetes. Comput Biol Med 2021; 136:104668. [PMID: 34340124 PMCID: PMC8299293 DOI: 10.1016/j.compbiomed.2021.104668] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 06/30/2021] [Accepted: 07/17/2021] [Indexed: 01/07/2023]
Abstract
The ongoing COVID-19 outbreak, caused by SARS-CoV-2, has posed a massive threat to global public health, especially to people with underlying health conditions. Type 2 diabetes (T2D) is lethal comorbidity of COVID-19. However, its pathogenetic link remains unclear. This research aims to determine the genetic factors and processes contributing to the synergistic severity of SARS-CoV-2 infection among T2D patients through bioinformatics approaches. We analyzed two sets of transcriptomic data of SARS-CoV-2 infection obtained from lung epithelium cells and PBMCs, and two sets of T2D data from pancreatic islet cells and PBMCs to identify the associated differentially expressed genes (DEGs) followed by their functional enrichment analyses in terms of protein-protein interaction (PPI) to detect hub-proteins and associated comorbidities, transcription factors (TFs), microRNAs (miRNAs) as well as the potential drug candidates. In PPI analysis, four potential hub-proteins (i.e., BIRC3, C3, MME, and IL1B) were identified among 25 DEGs shared between the disease pair. Enrichment analyses using the mutually overlapped DEGs revealed the most prevalent GO and cell signalling pathways, including TNF signalling, cytokine-cytokine receptor interaction, and IL-17 signalling, which are related to cytokine activities. Furthermore, as significant TFs, we identified IRF1, KLF11, FOSL1, and CREB3L1 while miRNAs including miR-1-3p, 34a-5p, 16–5p, 155–5p, 20a-5p, and let-7b-5p were found to be noteworthy. The findings illustrated the significant association between COVID-19 and T2D at the molecular level. These genetic determinants can further be explored for their specific roles in disease progression and therapeutic intervention, while significant pathways can also be studied as molecular checkpoints. Finally, the identified drug candidates may be evaluated for their potency to minimize the severity of COVID-19 patients with pre-existing T2D.
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Affiliation(s)
- M Babul Islam
- Department of Electrical and Electronic Engineering, University of Rajshahi, Rajshahi, Bangladesh
| | - Utpala Nanda Chowdhury
- Department of Computer Science and Engineering, University of Rajshahi, Rajshahi, Bangladesh
| | - Zulkar Nain
- Department of Biotechnology and Genetic Engineering, Islamic University, Kushtia, Bangladesh
| | - Shahadat Uddin
- Complex Systems Research Group & Project Management Program, Faculty of Engineering, The University of Sydney, NSW, 2006, Australia
| | - Mohammad Boshir Ahmed
- School of Material Science and Engineering, Gwangju Institute of Science and Technology, Gwangju, 61005, Republic of Korea
| | - Mohammad Ali Moni
- Healthy Ageing Theme, Garvan Institute of Medical Research, Darlinghurst, NSW, 2010, Australia; WHO Collaborating Centre on eHealth, UNSW Digital Health, School of Public Health and Community Medicine, Faculty of Medicine, UNSW Sydney, NSW, 2052, Australia.
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21
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Peroxiredoxins-The Underrated Actors during Virus-Induced Oxidative Stress. Antioxidants (Basel) 2021; 10:antiox10060977. [PMID: 34207367 PMCID: PMC8234473 DOI: 10.3390/antiox10060977] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 06/09/2021] [Accepted: 06/15/2021] [Indexed: 12/19/2022] Open
Abstract
Enhanced production of reactive oxygen species (ROS) triggered by various stimuli, including viral infections, has attributed much attention in the past years. It has been shown that different viruses that cause acute or chronic diseases induce oxidative stress in infected cells and dysregulate antioxidant its antioxidant capacity. However, most studies focused on catalase and superoxide dismutases, whereas a family of peroxiredoxins (Prdx), the most effective peroxide scavengers, were given little or no attention. In the current review, we demonstrate that peroxiredoxins scavenge hydrogen and organic peroxides at their physiological concentrations at various cell compartments, unlike many other antioxidant enzymes, and discuss their recycling. We also provide data on the regulation of their expression by various transcription factors, as they can be compared with the imprint of viruses on transcriptional machinery. Next, we discuss the involvement of peroxiredoxins in transferring signals from ROS on specific proteins by promoting the oxidation of target cysteine groups, as well as briefly demonstrate evidence of nonenzymatic, chaperone, functions of Prdx. Finally, we give an account of the current state of research of peroxiredoxins for various viruses. These data clearly show that Prdx have not been given proper attention despite all the achievements in general redox biology.
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22
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Chowdhury UN, Ahmad S, Islam MB, Alyami SA, Quinn JMW, Eapen V, Moni MA. System biology and bioinformatics pipeline to identify comorbidities risk association: Neurodegenerative disorder case study. PLoS One 2021; 16:e0250660. [PMID: 33956862 PMCID: PMC8101720 DOI: 10.1371/journal.pone.0250660] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Accepted: 04/12/2021] [Indexed: 12/17/2022] Open
Abstract
Alzheimer's disease (AD) is the commonest progressive neurodegenerative condition in humans, and is currently incurable. A wide spectrum of comorbidities, including other neurodegenerative diseases, are frequently associated with AD. How AD interacts with those comorbidities can be examined by analysing gene expression patterns in affected tissues using bioinformatics tools. We surveyed public data repositories for available gene expression data on tissue from AD subjects and from people affected by neurodegenerative diseases that are often found as comorbidities with AD. We then utilized large set of gene expression data, cell-related data and other public resources through an analytical process to identify functional disease links. This process incorporated gene set enrichment analysis and utilized semantic similarity to give proximity measures. We identified genes with abnormal expressions that were common to AD and its comorbidities, as well as shared gene ontology terms and molecular pathways. Our methodological pipeline was implemented in the R platform as an open-source package and available at the following link: https://github.com/unchowdhury/AD_comorbidity. The pipeline was thus able to identify factors and pathways that may constitute functional links between AD and these common comorbidities by which they affect each others development and progression. This pipeline can also be useful to identify key pathological factors and therapeutic targets for other diseases and disease interactions.
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Affiliation(s)
- Utpala Nanda Chowdhury
- Department of Computer Science and Engineering, University of Rajshahi, Rajshahi, Bangladesh
| | - Shamim Ahmad
- Department of Computer Science and Engineering, University of Rajshahi, Rajshahi, Bangladesh
| | - M. Babul Islam
- Department of Electrical and Electronic Engineering, University of Rajshahi, Rajshahi, Bangladesh
| | - Salem A. Alyami
- Department of Mathematics and Statistics, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
| | - Julian M. W. Quinn
- Healthy Ageing Theme, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia
| | - Valsamma Eapen
- School of Psychiatry, Faculty of Medicine, University of New South Wales, Sydney, Australia
| | - Mohammad Ali Moni
- Healthy Ageing Theme, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia
- School of Psychiatry, Faculty of Medicine, University of New South Wales, Sydney, Australia
- WHO Collaborating Centre on eHealth, School of Public Health and Community Medicine, Faculty of Medicine, UNSW Sydney, Sydney, Australia
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23
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Aktar S, Ahamad MM, Rashed-Al-Mahfuz M, Azad A, Uddin S, Kamal A, Alyami SA, Lin PI, Islam SMS, Quinn JM, Eapen V, Moni MA. Machine Learning Approach to Predicting COVID-19 Disease Severity Based on Clinical Blood Test Data: Statistical Analysis and Model Development. JMIR Med Inform 2021; 9:e25884. [PMID: 33779565 PMCID: PMC8045777 DOI: 10.2196/25884] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 01/21/2021] [Accepted: 03/21/2021] [Indexed: 12/12/2022] Open
Abstract
Background Accurate prediction of the disease severity of patients with COVID-19 would greatly improve care delivery and resource allocation and thereby reduce mortality risks, especially in less developed countries. Many patient-related factors, such as pre-existing comorbidities, affect disease severity and can be used to aid this prediction. Objective Because rapid automated profiling of peripheral blood samples is widely available, we aimed to investigate how data from the peripheral blood of patients with COVID-19 can be used to predict clinical outcomes. Methods We investigated clinical data sets of patients with COVID-19 with known outcomes by combining statistical comparison and correlation methods with machine learning algorithms; the latter included decision tree, random forest, variants of gradient boosting machine, support vector machine, k-nearest neighbor, and deep learning methods. Results Our work revealed that several clinical parameters that are measurable in blood samples are factors that can discriminate between healthy people and COVID-19–positive patients, and we showed the value of these parameters in predicting later severity of COVID-19 symptoms. We developed a number of analytical methods that showed accuracy and precision scores >90% for disease severity prediction. Conclusions We developed methodologies to analyze routine patient clinical data that enable more accurate prediction of COVID-19 patient outcomes. With this approach, data from standard hospital laboratory analyses of patient blood could be used to identify patients with COVID-19 who are at high risk of mortality, thus enabling optimization of hospital facilities for COVID-19 treatment.
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Affiliation(s)
- Sakifa Aktar
- Department of Computer Science and Engineering, Bangabandhu Sheikh Mujibur Rahman Science & Technology University, Gopalganj, Bangladesh
| | - Md Martuza Ahamad
- Department of Computer Science and Engineering, Bangabandhu Sheikh Mujibur Rahman Science & Technology University, Gopalganj, Bangladesh
| | - Md Rashed-Al-Mahfuz
- Department of Computer Science and Engineering, University of Rajshahi, Rajshahi, Bangladesh
| | - Akm Azad
- iThree Institute, Faculty of Science, University Technology of Sydney, Sydney, Australia
| | - Shahadat Uddin
- Complex Systems Research Group, Faculty of Engineering, The University of Sydney, Darlington, Sydney, Australia
| | - Ahm Kamal
- Department of Computer Science and Engineering, Jatiya Kabi Kazi Nazrul Islam University, Mymensingh, Bangladesh
| | - Salem A Alyami
- Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
| | - Ping-I Lin
- School of Psychiatry, Faculty of Medicine, University of New South Wales, Sydney, Australia
| | | | - Julian Mw Quinn
- Healthy Ageing Theme, The Garvan Institute of Medical Research, Darlington, Australia
| | - Valsamma Eapen
- School of Psychiatry, Faculty of Medicine, University of New South Wales, Sydney, Australia
| | - Mohammad Ali Moni
- School of Psychiatry, Faculty of Medicine, University of New South Wales, Sydney, Australia.,Healthy Ageing Theme, The Garvan Institute of Medical Research, Darlington, Australia.,WHO Collaborating Centre on eHealth, UNSW Digital Health, School of Public Health and Community Medicine, Faculty of Medicine, University of New South Wales, Sydney, Australia
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24
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Auwul MR, Rahman MR, Gov E, Shahjaman M, Moni MA. Bioinformatics and machine learning approach identifies potential drug targets and pathways in COVID-19. Brief Bioinform 2021; 22:6220170. [PMID: 33839760 PMCID: PMC8083354 DOI: 10.1093/bib/bbab120] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 02/15/2021] [Accepted: 03/13/2021] [Indexed: 12/12/2022] Open
Abstract
Current coronavirus disease-2019 (COVID-19) pandemic has caused massive loss of lives. Clinical trials of vaccines and drugs are currently being conducted around the world; however, till now no effective drug is available for COVID-19. Identification of key genes and perturbed pathways in COVID-19 may uncover potential drug targets and biomarkers. We aimed to identify key gene modules and hub targets involved in COVID-19. We have analyzed SARS-CoV-2 infected peripheral blood mononuclear cell (PBMC) transcriptomic data through gene coexpression analysis. We identified 1520 and 1733 differentially expressed genes (DEGs) from the GSE152418 and CRA002390 PBMC datasets, respectively (FDR < 0.05). We found four key gene modules and hub gene signature based on module membership (MMhub) statistics and protein-protein interaction (PPI) networks (PPIhub). Functional annotation by enrichment analysis of the genes of these modules demonstrated immune and inflammatory response biological processes enriched by the DEGs. The pathway analysis revealed the hub genes were enriched with the IL-17 signaling pathway, cytokine-cytokine receptor interaction pathways. Then, we demonstrated the classification performance of hub genes (PLK1, AURKB, AURKA, CDK1, CDC20, KIF11, CCNB1, KIF2C, DTL and CDC6) with accuracy >0.90 suggesting the biomarker potential of the hub genes. The regulatory network analysis showed transcription factors and microRNAs that target these hub genes. Finally, drug-gene interactions analysis suggests amsacrine, BRD-K68548958, naproxol, palbociclib and teniposide as the top-scored repurposed drugs. The identified biomarkers and pathways might be therapeutic targets to the COVID-19.
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Affiliation(s)
- Md Rabiul Auwul
- School of Economics and Statistics, Guangzhou University, Guangzhou 510006, China
| | - Md Rezanur Rahman
- Department of Biochemistry and Biotechnology, School of Biomedical Science, Khwaja Yunus Ali University, Sirajgonj-6751, Bangladesh
| | - Esra Gov
- Department of Bioengineering, Adana Alparslan Turkes Science and Technology University, Adana-01250, Turkey
| | - Md Shahjaman
- Department of Statistics, Begum Rokeya University, Rangpur-5400, Bangladesh
| | - Mohammad Ali Moni
- WHO Collaborating Centre on eHealth, UNSW Digital Health, School of Public Health and Community Medicine, Faculty of Medicine, UNSW Sydney, Australia
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