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Gong W, Guo P, Liu L, Yan R, Liu S, Wang S, Xue F, Zhou X, Sun X, Yuan Z. Genomics-driven integrative analysis highlights immune-related plasma proteins for psychiatric disorders. J Affect Disord 2025; 370:124-133. [PMID: 39491680 DOI: 10.1016/j.jad.2024.10.126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/17/2024] [Revised: 09/21/2024] [Accepted: 10/30/2024] [Indexed: 11/05/2024]
Abstract
BACKGROUND Genome-wide association studies (GWAS) have identified numerous variants associated with psychiatric disorders. However, it remains largely unknown on how GWAS risk variants contribute to psychiatric disorders. METHODS Through integrating two largest, publicly available, independent protein quantitative trait loci datasets of plasma protein and nine large-scale GWAS summary statistics of psychiatric disorders, we first performed proteome-wide association study (PWAS) to identify psychiatric disorders-associated plasma proteins, followed by enrichment analysis to reveal the underlying biological processes and pathways. Then, we conducted Mendelian randomization (MR) and Bayesian colocalization (COLOC) analyses, with both discovery and parallel replication datasets, to further identify protein-disorder pairs with putatively causal relationships. We finally prioritized the potential drug targets using Drug Gene Interaction Database. RESULTS PWAS totally identified 112 proteins, which were significantly enriched in biological processes relevant to immune regulation and response to stimulus including regulation of immune system process (adjusted P = 1.69 × 10-7) and response to external stimulus (adjusted P = 4.13 × 10-7), and viral infection related pathways, including COVID-19 (adjusted P = 2.94 × 10-2). MR and COLOC analysis further identified 26 potentially causal protein-disorder pairs in both discovery and replication analysis. Notably, eight protein-coding genes were immune-related, such as IRF3, CSK, and ACE, five among 16 druggable genes were reported to interact with drugs, including ACE, CSK, PSMB4, XPNPEP1, and MICB. CONCLUSIONS Our findings highlighted the immunological hypothesis and identified potentially causal plasma proteins for psychiatric disorders, providing biological insights into the pathogenesis and benefit the development of preventive or therapeutic drugs for psychiatric disorders.
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Affiliation(s)
- Weiming Gong
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China; Institute for Medical Dataology, Shandong University, Jinan, Shandong, China
| | - Ping Guo
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China; Institute for Medical Dataology, Shandong University, Jinan, Shandong, China
| | - Lu Liu
- Department of Biostatistics, University of Michigan, Ann Arbor, USA; Center for Statistical Genetics, University of Michigan, Ann Arbor, USA
| | - Ran Yan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China; Institute for Medical Dataology, Shandong University, Jinan, Shandong, China
| | - Shuai Liu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China; Institute for Medical Dataology, Shandong University, Jinan, Shandong, China
| | - Shukang Wang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China; Institute for Medical Dataology, Shandong University, Jinan, Shandong, China
| | - Fuzhong Xue
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China; Institute for Medical Dataology, Shandong University, Jinan, Shandong, China
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor, USA; Center for Statistical Genetics, University of Michigan, Ann Arbor, USA
| | - Xiubin Sun
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China; Institute for Medical Dataology, Shandong University, Jinan, Shandong, China.
| | - Zhongshang Yuan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China; Institute for Medical Dataology, Shandong University, Jinan, Shandong, China.
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Huang Y, Urban C, Hubel P, Stukalov A, Pichlmair A. Protein turnover regulation is critical for influenza A virus infection. Cell Syst 2024; 15:911-929.e8. [PMID: 39368468 DOI: 10.1016/j.cels.2024.09.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 08/16/2024] [Accepted: 09/13/2024] [Indexed: 10/07/2024]
Abstract
The abundance of a protein is defined by its continuous synthesis and degradation, a process known as protein turnover. Here, we systematically profiled the turnover of proteins in influenza A virus (IAV)-infected cells using a pulse-chase stable isotope labeling by amino acids in cell culture (SILAC)-based approach combined with downstream statistical modeling. We identified 1,798 virus-affected proteins with turnover changes (tVAPs) out of 7,739 detected proteins (data available at pulsechase.innatelab.org). In particular, the affected proteins were involved in RNA transcription, splicing and nuclear transport, protein translation and stability, and energy metabolism. Many tVAPs appeared to be known IAV-interacting proteins that regulate virus propagation, such as KPNA6, PPP6C, and POLR2A. Notably, our analysis identified additional IAV host and restriction factors, such as the splicing factor GPKOW, that exhibit significant turnover rate changes while their total abundance is minimally affected. Overall, we show that protein turnover is a critical factor both for virus replication and antiviral defense.
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Affiliation(s)
- Yiqi Huang
- Institute of Virology, Technical University of Munich, School of Medicine, Munich, Germany
| | - Christian Urban
- Institute of Virology, Technical University of Munich, School of Medicine, Munich, Germany
| | - Philipp Hubel
- Core Facility Hohenheim, Universität Hohenheim, Stuttgart, Germany
| | - Alexey Stukalov
- Institute of Virology, Technical University of Munich, School of Medicine, Munich, Germany
| | - Andreas Pichlmair
- Institute of Virology, Technical University of Munich, School of Medicine, Munich, Germany; Institute of Virology, Helmholtz Munich, Munich, Germany; German Centre for Infection Research (DZIF), Partner Site, Munich, Germany.
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Husain M. Influenza Virus Host Restriction Factors: The ISGs and Non-ISGs. Pathogens 2024; 13:127. [PMID: 38392865 PMCID: PMC10893265 DOI: 10.3390/pathogens13020127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 01/18/2024] [Accepted: 01/26/2024] [Indexed: 02/25/2024] Open
Abstract
Influenza virus has been one of the most prevalent and researched viruses globally. Consequently, there is ample information available about influenza virus lifecycle and pathogenesis. However, there is plenty yet to be known about the determinants of influenza virus pathogenesis and disease severity. Influenza virus exploits host factors to promote each step of its lifecycle. In turn, the host deploys antiviral or restriction factors that inhibit or restrict the influenza virus lifecycle at each of those steps. Two broad categories of host restriction factors can exist in virus-infected cells: (1) encoded by the interferon-stimulated genes (ISGs) and (2) encoded by the constitutively expressed genes that are not stimulated by interferons (non-ISGs). There are hundreds of ISGs known, and many, e.g., Mx, IFITMs, and TRIMs, have been characterized to restrict influenza virus infection at different stages of its lifecycle by (1) blocking viral entry or progeny release, (2) sequestering or degrading viral components and interfering with viral synthesis and assembly, or (3) bolstering host innate defenses. Also, many non-ISGs, e.g., cyclophilins, ncRNAs, and HDACs, have been identified and characterized to restrict influenza virus infection at different lifecycle stages by similar mechanisms. This review provides an overview of those ISGs and non-ISGs and how the influenza virus escapes the restriction imposed by them and aims to improve our understanding of the host restriction mechanisms of the influenza virus.
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Affiliation(s)
- Matloob Husain
- Department of Microbiology and Immunology, University of Otago, P.O. Box 56, Dunedin 9054, New Zealand
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Strategies of Influenza A Virus to Ensure the Translation of Viral mRNAs. Pathogens 2022; 11:pathogens11121521. [PMID: 36558855 PMCID: PMC9783940 DOI: 10.3390/pathogens11121521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 12/08/2022] [Accepted: 12/09/2022] [Indexed: 12/14/2022] Open
Abstract
Viruses are obligatorily intracellular pathogens. To generate progeny virus particles, influenza A viruses (IAVs) have to divert the cellular machinery to ensure sufficient translation of viral mRNAs. To this end, several strategies have been exploited by IAVs, such as host gene shutoff, suppression of host innate immune responses, and selective translation of viral mRNAs. Various IAV proteins are responsible for host gene shutoff, e.g., NS1, PA-X, and RdRp, through inhibition of cellular gene transcription, suppression of cellular RNA processing, degradation of cellular RNAs, and blockage of cellular mRNA export from the nucleus. Host shutoff should suppress the innate immune responses and also increase the translation of viral mRNAs indirectly due to the reduced competition from cellular mRNAs for cellular translational machinery. However, many other mechanisms are also responsible for the suppression of innate immune responses by IAV, such as prevention of the detection of the viral RNAs by the RLRs, inhibition of the activities of proteins involved in signaling events of interferon production, and inhibition of the activities of interferon-stimulated genes, mainly through viral NS1, PB1-F2, and PA-X proteins. IAV mRNAs may be selectively translated in favor of cellular mRNAs through interacting with viral and/or cellular proteins, such as NS1, PABPI, and/or IFIT2, in the 5'-UTR of viral mRNAs. This review briefly summarizes the strategies utilized by IAVs to ensure sufficient translation of viral mRNAs focusing on recent developments.
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