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Mehta P, Liu CSC, Sinha S, Mohite R, Arora S, Chattopadhyay P, Budhiraja S, Tarai B, Pandey R. Reduced protein-coding transcript diversity in severe dengue emphasises the role of alternative splicing. Life Sci Alliance 2024; 7:e202402683. [PMID: 38830771 PMCID: PMC11147948 DOI: 10.26508/lsa.202402683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 05/22/2024] [Accepted: 05/22/2024] [Indexed: 06/05/2024] Open
Abstract
Dengue fever, a neglected tropical arboviral disease, has emerged as a global health concern in the past decade. Necessitating a nuanced comprehension of the intricate dynamics of host-virus interactions influencing disease severity, we analysed transcriptomic patterns using bulk RNA-seq from 112 age- and gender-matched NS1 antigen-confirmed hospital-admitted dengue patients with varying severity. Severe cases exhibited reduced platelet count, increased lymphocytosis, and neutropenia, indicating a dysregulated immune response. Using bulk RNA-seq, our analysis revealed a minimal overlap between the differentially expressed gene and transcript isoform, with a distinct expression pattern across the disease severity. Severe patients showed enrichment in retained intron and nonsense-mediated decay transcript biotypes, suggesting altered splicing efficiency. Furthermore, an up-regulated programmed cell death, a haemolytic response, and an impaired interferon and antiviral response at the transcript level were observed. We also identified the potential involvement of the RBM39 gene among others in the innate immune response during dengue viral pathogenesis, warranting further investigation. These findings provide valuable insights into potential therapeutic targets, underscoring the importance of exploring transcriptomic landscapes between different disease sub-phenotypes in infectious diseases.
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Affiliation(s)
- Priyanka Mehta
- https://ror.org/05ef28661 Division of Immunology and Infectious Disease Biology, INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) Laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Chinky Shiu Chen Liu
- https://ror.org/05ef28661 Division of Immunology and Infectious Disease Biology, INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) Laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
| | - Sristi Sinha
- https://ror.org/05ef28661 Division of Immunology and Infectious Disease Biology, INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) Laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
| | - Ramakant Mohite
- https://ror.org/05ef28661 Division of Immunology and Infectious Disease Biology, INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) Laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
| | - Smriti Arora
- https://ror.org/05ef28661 Division of Immunology and Infectious Disease Biology, INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) Laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
| | - Partha Chattopadhyay
- https://ror.org/05ef28661 Division of Immunology and Infectious Disease Biology, INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) Laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Sandeep Budhiraja
- https://ror.org/00e7r7m66 Max Super Speciality Hospital (A Unit of Devki Devi Foundation), Max Healthcare, Delhi, India
| | - Bansidhar Tarai
- https://ror.org/00e7r7m66 Max Super Speciality Hospital (A Unit of Devki Devi Foundation), Max Healthcare, Delhi, India
| | - Rajesh Pandey
- https://ror.org/05ef28661 Division of Immunology and Infectious Disease Biology, INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) Laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
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Liu X, Li H, Wang Y, Li S, Ren W, Yuan J, Pang Y. Discovering common pathogenetic processes between tuberculosis and COVID-19 by bioinformatics and system biology approach. Heliyon 2024; 10:e28664. [PMID: 38596062 PMCID: PMC11002586 DOI: 10.1016/j.heliyon.2024.e28664] [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: 08/01/2023] [Revised: 03/20/2024] [Accepted: 03/21/2024] [Indexed: 04/11/2024] Open
Abstract
Background SARS-CoV-2, the cause of the COVID-19 pandemic, poses a significant threat to humanity. Individuals with pulmonary tuberculosis (PTB) are at increased risk of developing severe COVID-19, due to long-term lung damage that heightens their susceptibility to full-blown disease. Methods Three COVID-19 datasets (GSE157103, GSE166253, and GSE171110) and one PTB dataset (GSE83456) were obtained from the Gene Expression Omnibus databases. Subsequently, data were subjected to weighted gene co-expression network analysis(WGCNA)followed by functional enrichment analysis using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway databases. These analyses revealed two overlapping disease-specific modules, each comprising co-regulated genes with potentially related biological functions. Using Cytoscape, we visualised the interaction network containing common disease-related genes found within the intersection between modules and predicted transcription factors (TFs). Real-time qPCR was conducted to quantify expression levels of these genes in blood samples from COVID-19 and PTB patients. Finally, DisGeNET and the Drug Signatures database were employed to analyze these common genes, unveiling their connections to clinical disease features and potential drug treatments. Results Examination of the overlap between COVID-19 and PTB gene modules unveiled 11 common genes. Functional enrichment analyses using KEGG and GO shed light on potential functional relationships among these genes, providing insights into their potential roles in the heightened mortality of PTB patients due to SARS-CoV-2 infection. Furthermore, results of various bioinformatics-based analyses of common TFs and target genes led to identification of shared pathways and therapeutic targets for PTB patients with COVID-19, along with potential drug treatments for these patients. Conclusion Our results unveiled a potential biological connection between COVID-19 and PTB, as supported by results of functional enrichment analysis that highlighted potential biological processes and signaling pathways shared by both diseases. Building on these findings, we propose potential drug treatments for PTB patients with COVID-19, pending verification of drug safety and efficacy through laboratory and multicentre studies before clinical use.
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Affiliation(s)
| | | | | | - Shanshan Li
- Department of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China
| | - Weicong Ren
- Department of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China
| | - Jinfeng Yuan
- Department of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China
| | - Yu Pang
- Department of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China
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Amahong K, Zhang W, Liu Y, Li T, Huang S, Han L, Tao L, Zhu F. RVvictor: Virus RNA-directed molecular interactions for RNA virus infection. Comput Biol Med 2024; 169:107886. [PMID: 38157777 DOI: 10.1016/j.compbiomed.2023.107886] [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: 08/06/2023] [Revised: 12/14/2023] [Accepted: 12/18/2023] [Indexed: 01/03/2024]
Abstract
RNA viruses are major human pathogens that cause seasonal epidemics and occasional pandemic outbreaks. Due to the nature of their RNA genomes, it is anticipated that virus's RNA interacts with host protein (INTPRO), messenger RNA (INTmRNA), and non-coding RNA (INTncRNA) to perform their particular functions during their transcription and replication. In other words, thus, it is urgently needed to have such valuable data on virus RNA-directed molecular interactions (especially INTPROs), which are highly anticipated to attract broad research interests in the fields of RNA virus translation and replication. In this study, a new database was constructed to describe the virus RNA-directed interaction (INTPRO, INTmRNA, INTncRNA) for RNA virus (RVvictor). This database is unique in a) unambiguously characterizing the interactions between viruses RNAs and host proteins, b) providing, for the first time, the most systematic RNA-directed interaction data resources in providing clues to understand the molecular mechanisms of RNA viruses' translation, and replication, and c) in RVvictor, comprehensive enrichment analysis is conducted for each virus RNA based on its associated target genes/proteins, and the enrichment results were explicitly illustrated using various graphs. We found significant enrichment of a suite of pathways related to infection, translation, and replication, e.g., HIV infection, coronavirus disease, regulation of viral genome replication, and so on. Due to the devastating and persistent threat posed by the RNA virus, RVvictor constructed, for the first time, a possible network of cross-talk in RNA-directed interaction, which may ultimately explain the pathogenicity of RNA virus infection. The knowledge base might help develop new anti-viral therapeutic targets in the future. It's now free and publicly accessible at: https://idrblab.org/rvvictor/.
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Affiliation(s)
- Kuerbannisha Amahong
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China; Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 330110, China
| | - Wei Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China; Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 330110, China
| | - Yuhong Liu
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Teng Li
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Shijie Huang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China; Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 330110, China
| | - Lianyi Han
- Greater Bay Area Institute of Precision Medicine (Guangzhou), School of Life Sciences, Fudan University, Shanghai, 315211, China.
| | - Lin Tao
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China.
| | - Feng Zhu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China; Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 330110, China.
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Malvankar S, Singh A, Ravi Kumar YS, Sahu S, Shah M, Murghai Y, Seervi M, Srivastava RK, Verma B. Modulation of various host cellular machinery during COVID-19 infection. Rev Med Virol 2023; 33:e2481. [PMID: 37758688 DOI: 10.1002/rmv.2481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 07/24/2023] [Accepted: 09/10/2023] [Indexed: 09/29/2023]
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) emerged in December 2019, causing a range of respiratory infections from mild to severe. This resulted in the ongoing global COVID-19 pandemic, which has had a significant impact on public health. The World Health Organization declared COVID-19 as a global pandemic in March 2020. Viruses are intracellular pathogens that rely on the host's machinery to establish a successful infection. They exploit the gene expression machinery of host cells to facilitate their own replication. Gaining a better understanding of gene expression modulation in SARS-CoV2 is crucial for designing and developing effective antiviral strategies. Efforts are currently underway to understand the molecular-level interaction between the host and the pathogen. In this review, we describe how SARS-CoV2 infection modulates gene expression by interfering with cellular processes, including transcription, post-transcription, translation, post-translation, epigenetic modifications as well as processing and degradation pathways. Additionally, we emphasise the therapeutic implications of these findings in the development of new therapies to treat SARS-CoV2 infection.
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Affiliation(s)
- Shivani Malvankar
- Department of Biotechnology, All India Institute of Medical Sciences, New Delhi, India
| | - Anjali Singh
- Department of Biotechnology, All India Institute of Medical Sciences, New Delhi, India
| | - Y S Ravi Kumar
- Department of Biotechnology, M. S. Ramaiah Institute of Technology, Bengaluru, India
| | - Swetangini Sahu
- Department of Biotechnology, All India Institute of Medical Sciences, New Delhi, India
| | - Megha Shah
- Department of Biotechnology, All India Institute of Medical Sciences, New Delhi, India
| | - Yamini Murghai
- Department of Biotechnology, All India Institute of Medical Sciences, New Delhi, India
| | - Mahendra Seervi
- Department of Biotechnology, All India Institute of Medical Sciences, New Delhi, India
| | - Rupesh K Srivastava
- Department of Biotechnology, All India Institute of Medical Sciences, New Delhi, India
| | - Bhupendra Verma
- Department of Biotechnology, All India Institute of Medical Sciences, New Delhi, India
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