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Kole A, Bag AK, Pal AJ, De D. Generic model to unravel the deeper insights of viral infections: an empirical application of evolutionary graph coloring in computational network biology. BMC Bioinformatics 2024; 25:74. [PMID: 38365632 PMCID: PMC10874019 DOI: 10.1186/s12859-024-05690-0] [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: 11/22/2023] [Accepted: 02/02/2024] [Indexed: 02/18/2024] Open
Abstract
PURPOSE Graph coloring approach has emerged as a valuable problem-solving tool for both theoretical and practical aspects across various scientific disciplines, including biology. In this study, we demonstrate the graph coloring's effectiveness in computational network biology, more precisely in analyzing protein-protein interaction (PPI) networks to gain insights about the viral infections and its consequences on human health. Accordingly, we propose a generic model that can highlight important hub proteins of virus-associated disease manifestations, changes in disease-associated biological pathways, potential drug targets and respective drugs. We test our model on SARS-CoV-2 infection, a highly transmissible virus responsible for the COVID-19 pandemic. The pandemic took significant human lives, causing severe respiratory illnesses and exhibiting various symptoms ranging from fever and cough to gastrointestinal, cardiac, renal, neurological, and other manifestations. METHODS To investigate the underlying mechanisms of SARS-CoV-2 infection-induced dysregulation of human pathobiology, we construct a two-level PPI network and employed a differential evolution-based graph coloring (DEGCP) algorithm to identify critical hub proteins that might serve as potential targets for resolving the associated issues. Initially, we concentrate on the direct human interactors of SARS-CoV-2 proteins to construct the first-level PPI network and subsequently applied the DEGCP algorithm to identify essential hub proteins within this network. We then build a second-level PPI network by incorporating the next-level human interactors of the first-level hub proteins and use the DEGCP algorithm to predict the second level of hub proteins. RESULTS We first identify the potential crucial hub proteins associated with SARS-CoV-2 infection at different levels. Through comprehensive analysis, we then investigate the cellular localization, interactions with other viral families, involvement in biological pathways and processes, functional attributes, gene regulation capabilities as transcription factors, and their associations with disease-associated symptoms of these identified hub proteins. Our findings highlight the significance of these hub proteins and their intricate connections with disease pathophysiology. Furthermore, we predict potential drug targets among the hub proteins and identify specific drugs that hold promise in preventing or treating SARS-CoV-2 infection and its consequences. CONCLUSION Our generic model demonstrates the effectiveness of DEGCP algorithm in analyzing biological PPI networks, provides valuable insights into disease biology, and offers a basis for developing novel therapeutic strategies for other viral infections that may cause future pandemic.
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Affiliation(s)
- Arnab Kole
- Department of Computer Application, The Heritage Academy, Kolkata, W.B., 700107, India.
| | - Arup Kumar Bag
- Beckman Research Institute of City of Hope, Duarte, CA, 91010, USA
| | | | - Debashis De
- Department of Computer Science and Engineering, Maulana Abul Kalam Azad University of Technology, Nadia, W.B., 741249, India
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Farooq QUA, Aiman S, Ali Y, Shaukat Z, Ali Y, Khan A, Samad A, Wadood A, Li C. A comprehensive protein interaction map and druggability investigation prioritized dengue virus NS1 protein as promising therapeutic candidate. PLoS One 2023; 18:e0287905. [PMID: 37498862 PMCID: PMC10374080 DOI: 10.1371/journal.pone.0287905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 06/15/2023] [Indexed: 07/29/2023] Open
Abstract
Dengue Virus (DENV) is a serious threat to human life worldwide and is one of the most dangerous vector-borne diseases, causing thousands of deaths annually. We constructed a comprehensive PPI map of DENV with its host Homo sapiens and performed various bioinformatics analyses. We found 1195 interactions between 858 human and 10 DENV proteins. Pathway enrichment analysis was performed on the two sets of gene products, and the top 5 human proteins with the maximum number of interactions with dengue viral proteins revealed noticeable results. The non-structural protein NS1 in DENV had the maximum number of interactions with the host protein, followed by NS5 and NS3. Among the human proteins, HBA1 and UBE2I were associated with 7 viral proteins, and 3 human proteins (CSNK2A1, RRP12, and HSP90AB1) were found to interact with 6 viral proteins. Pharmacophore-based virtual screening of millions of compounds in the public databases was performed to identify potential DENV-NS1 inhibitors. The lead compounds were selected based on RMSD values, docking scores, and strong binding affinities. The top ten hit compounds were subjected to ADME profiling which identified compounds C2 (MolPort-044-180-163) and C6 (MolPort-001-742-737) as lead inhibitors against DENV-NS1. Molecular dynamics trajectory analysis and intermolecular interactions between NS1 and the ligands displayed the molecular stability of the complexes in the cellular environment. The in-silico approaches used in this study could pave the way for the development of potential specie-specific drugs and help in eliminating deadly viral infections. Therefore, experimental and clinical assays are required to validate the results of this study.
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Affiliation(s)
- Qurrat Ul Ain Farooq
- Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing, China
| | - Sara Aiman
- Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing, China
| | - Yasir Ali
- National Center for Bioinformatics, Quaid-i-Azam University, Islamabad, Pakistan
| | - Zeeshan Shaukat
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
| | - Yasir Ali
- School of Biomedical Sciences, The Chinese University of Hong Kong, Hong Kong, Hong Kong
| | - Asifullah Khan
- Department of Biochemistry, Abdul Wali Khan University, Mardan, Pakistan
| | - Abdus Samad
- Department of Biochemistry, Abdul Wali Khan University, Mardan, Pakistan
| | - Abdul Wadood
- Department of Biochemistry, Abdul Wali Khan University, Mardan, Pakistan
| | - Chunhua Li
- Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing, China
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Masuta Y, Watanabe T, Minaga K, Kudo M. Reply to Response to "'A Case of Ulcerative Colitis Relapse Characterized by Systemic Type I Interferon Responses After COVID-19 Vaccination'" by Mungmunpuntipantip and Wiwanitkiton. Inflamm Bowel Dis 2022; 28:e113. [PMID: 35394530 PMCID: PMC9383714 DOI: 10.1093/ibd/izac081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/09/2022]
Affiliation(s)
- Yasuhiro Masuta
- Department of Gastroenterology and Hepatology, Kindai University Faculty of Medicine, Osaka-Sayama, Osaka, Japan
| | - Tomohiro Watanabe
- Address correspondence to: Tomohiro Watanabe MD, PhD, Department of Gastroenterology and Hepatology, Kindai University Faculty of Medicine, 377-2 Ohno-Higashi, Osaka-Sayama, Osaka 589-8511, Japan ()
| | - Kosuke Minaga
- Department of Gastroenterology and Hepatology, Kindai University Faculty of Medicine, Osaka-Sayama, Osaka, Japan
| | - Masatoshi Kudo
- Department of Gastroenterology and Hepatology, Kindai University Faculty of Medicine, Osaka-Sayama, Osaka, Japan
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Mungmunpuntipantip R, Wiwanitkit V. Ulcerative Colitis Relapse After COVID-19 Vaccination: Correspondence. Inflamm Bowel Dis 2022; 28:e112. [PMID: 35394524 PMCID: PMC9383703 DOI: 10.1093/ibd/izac080] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/09/2022]
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Kusari M, Dey L, Mukhopadhyay A. ChikvInt: A Chikungunya Virus-Host Protein-Protein Interaction Database. Lett Appl Microbiol 2022; 74:992-1000. [PMID: 35174520 DOI: 10.1111/lam.13677] [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: 08/05/2021] [Revised: 02/08/2022] [Accepted: 02/09/2022] [Indexed: 11/29/2022]
Abstract
Chikungunya is a fast mutating virus causing Chikungunya virus disease (ChikvD) with a significant load of disability-adjusted life years (DALY) around the world. The outbreak of this virus is significantly higher in the tropical countries. Several experiments have identified crucial viral-host protein-protein interactions (PPIs) between Chikungunya Virus (Chikv) and the human host. However, no standard database that catalogs this PPI information exists. Here we develop a Chikv-Human PPI database, ChikvInt, to facilitate understanding ChikvD disease pathogenesis and the progress of vaccine studies. ChikvInt consists of 109 interactions and is available at www.chikvint.com.
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Affiliation(s)
- Mitrajyoti Kusari
- Dept. of Computer Science & Engg, University of Kalyani, Kalyani, India
| | - Lopamudra Dey
- Dept. of Computer Science & Engg, Heritage Institute of Technology, Kolkata, India
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Amoriello R, Chernigovskaya M, Greiff V, Carnasciali A, Massacesi L, Barilaro A, Repice AM, Biagioli T, Aldinucci A, Muraro PA, Laplaud DA, Lossius A, Ballerini C. TCR repertoire diversity in Multiple Sclerosis: High-dimensional bioinformatics analysis of sequences from brain, cerebrospinal fluid and peripheral blood. EBioMedicine 2021; 68:103429. [PMID: 34127432 PMCID: PMC8245901 DOI: 10.1016/j.ebiom.2021.103429] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 05/12/2021] [Accepted: 05/19/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND T cells play a key role in the pathogenesis of multiple sclerosis (MS), a chronic, inflammatory, demyelinating disease of the central nervous system (CNS). Although several studies recently investigated the T-cell receptor (TCR) repertoire in cerebrospinal fluid (CSF) of MS patients by high-throughput sequencing (HTS), a deep analysis on repertoire similarities and differences among compartments is still missing. METHODS We performed comprehensive bioinformatics on high-dimensional TCR Vβ sequencing data from published and unpublished MS and healthy donors (HD) studies. We evaluated repertoire polarization, clone distribution, shared CDR3 amino acid sequences (CDR3s-a.a.) across repertoires, clone overlap with public databases, and TCR similarity architecture. FINDINGS CSF repertoires showed a significantly higher public clones percentage and sequence similarity compared to peripheral blood (PB). On the other hand, we failed to reject the null hypothesis that the repertoire polarization is the same between CSF and PB. One Primary-Progressive MS (PPMS) CSF repertoire differed from the others in terms of TCR similarity architecture. Cluster analysis splits MS from HD. INTERPRETATION In MS patients, the presence of a physiological barrier, the blood-brain barrier, does not impact clone prevalence and distribution, but impacts public clones, indicating CSF as a more private site. We reported a high Vβ sequence similarity in the CSF-TCR architecture in one PPMS. If confirmed it may be an interesting insight into MS progressive inflammatory mechanisms. The clustering of MS repertoires from HD suggests that disease shapes the TCR Vβ clonal profile. FUNDING This study was partly financially supported by the Italian Multiple Sclerosis Foundation (FISM), that contributed to Ballerini-DB data collection (grant #2015 R02).
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Affiliation(s)
- Roberta Amoriello
- Dipartimento di Medicina Sperimentale e Clinica (DMSC), Laboratory of Neuroimmunology, University of Florence, Viale Pieraccini 6, 50139 Florence, Italy
| | | | - Victor Greiff
- Department of Immunology, University of Oslo, Oslo, Norway
| | - Alberto Carnasciali
- Dipartimento di Medicina Sperimentale e Clinica (DMSC), Laboratory of Neuroimmunology, University of Florence, Viale Pieraccini 6, 50139 Florence, Italy
| | - Luca Massacesi
- Dipartimento di Neuroscienze, Psicologia, Area del Farmaco e Salute del Bambino (NEUROFARBA), University of Florence, Italy
| | - Alessandro Barilaro
- Dipartimento di Neuroscienze, Psicologia, Area del Farmaco e Salute del Bambino (NEUROFARBA), University of Florence, Italy
| | - Anna M Repice
- Dipartimento di Neuroscienze, Psicologia, Area del Farmaco e Salute del Bambino (NEUROFARBA), University of Florence, Italy
| | - Tiziana Biagioli
- Laboratorio Generale, Careggi University Hospital, Florence, Italy
| | | | - Paolo A Muraro
- Wolfson Neuroscience Laboratory, Department of Brain Sciences, Imperial College London, London, United Kingdom
| | - David A Laplaud
- CRTI-Inserm U1064, CIC0004 and Service de Neurologie, CHU de Nantes, Hôpital Nord Laënnec, Nantes, France
| | - Andreas Lossius
- Institute of Clinical Medicine, University of Oslo, Postboks 1105, Blindern 0317 Oslo, Norway.
| | - Clara Ballerini
- Dipartimento di Medicina Sperimentale e Clinica (DMSC), Laboratory of Neuroimmunology, University of Florence, Viale Pieraccini 6, 50139 Florence, Italy.
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