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Li B, Li X, Li X, Wang L, Lu J, Wang J. Prediction of influenza A virus-human protein-protein interactions using XGBoost with continuous and discontinuous amino acids information. PeerJ 2025; 13:e18863. [PMID: 39897484 PMCID: PMC11787804 DOI: 10.7717/peerj.18863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2024] [Accepted: 12/23/2024] [Indexed: 02/04/2025] Open
Abstract
Influenza A virus (IAV) has the characteristics of high infectivity and high pathogenicity, which makes IAV infection a serious public health threat. Identifying protein-protein interactions (PPIs) between IAV and human proteins is beneficial for understanding the mechanism of viral infection and designing antiviral drugs. In this article, we developed a sequence-based machine learning method for predicting PPI. First, we applied a new negative sample construction method to establish a high-quality IAV-human PPI dataset. Then we used conjoint triad (CT) and Moran autocorrelation (Moran) to encode biologically relevant features. The joint consideration utilizing the complementary information between contiguous and discontinuous amino acids provides a more comprehensive description of PPI information. After comparing different machine learning models, the eXtreme Gradient Boosting (XGBoost) model was determined as the final model for the prediction. The model achieved an accuracy of 96.89%, precision of 98.79%, recall of 94.85%, F1-score of 96.78%. Finally, we successfully identified 3,269 potential target proteins. Gene ontology (GO) and pathway analysis showed that these genes were highly associated with IAV infection. The analysis of the PPI network further revealed that the predicted proteins were classified as core proteins within the human protein interaction network. This study may encourage the identification of potential targets for the discovery of more effective anti-influenza drugs. The source codes and datasets are available at https://github.com/HVPPIlab/IVA-Human-PPI/.
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Affiliation(s)
- Binghua Li
- College of Informatics, Huazhong Agricultural University, Wuhan, China
- Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China
- Key Laboratory of Smart Farming for Agricultural Animals, Huazhong Agriultrual University, Wuhan, China
| | - Xin Li
- College of Informatics, Huazhong Agricultural University, Wuhan, China
- Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China
- Key Laboratory of Smart Farming for Agricultural Animals, Huazhong Agriultrual University, Wuhan, China
| | - Xiaoyu Li
- College of Informatics, Huazhong Agricultural University, Wuhan, China
- Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China
- Key Laboratory of Smart Farming for Agricultural Animals, Huazhong Agriultrual University, Wuhan, China
| | - Li Wang
- College of Informatics, Huazhong Agricultural University, Wuhan, China
- Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China
- Key Laboratory of Smart Farming for Agricultural Animals, Huazhong Agriultrual University, Wuhan, China
| | - Jun Lu
- College of Engineering, Huazhong Agricultural University, Wuhan, China
| | - Jia Wang
- College of Informatics, Huazhong Agricultural University, Wuhan, China
- Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China
- Key Laboratory of Smart Farming for Agricultural Animals, Huazhong Agriultrual University, Wuhan, China
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Martinez-Sobrido L, Nogales A. Recombinant Influenza A Viruses Expressing Reporter Genes from the Viral NS Segment. Int J Mol Sci 2024; 25:10584. [PMID: 39408912 PMCID: PMC11476892 DOI: 10.3390/ijms251910584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2024] [Revised: 09/26/2024] [Accepted: 09/29/2024] [Indexed: 10/20/2024] Open
Abstract
Studying influenza A viruses (IAVs) requires secondary experimental procedures to detect the presence of the virus in infected cells or animals. The ability to generate recombinant (r)IAV using reverse genetics techniques has allowed investigators to generate viruses expressing foreign genes, including fluorescent and luciferase proteins. These rIAVs expressing reporter genes have allowed for easily tracking viral infections in cultured cells and animal models of infection without the need for secondary approaches, representing an excellent option to study different aspects in the biology of IAV where expression of reporter genes can be used as a readout of viral replication and spread. Likewise, these reporter-expressing rIAVs provide an excellent opportunity for the rapid identification and characterization of prophylactic and/or therapeutic approaches. To date, rIAV expressing reporter genes from different viral segments have been described in the literature. Among those, rIAV expressing reporter genes from the viral NS segment have been shown to represent an excellent option to track IAV infection in vitro and in vivo, eliminating the need for secondary approaches to identify the presence of the virus. Here, we summarize the status on rIAV expressing traceable reporter genes from the viral NS segment and their applications for in vitro and in vivo influenza research.
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Affiliation(s)
| | - Aitor Nogales
- Center for Animal Health Research, CISA-INIA-CSIC, 28130 Madrid, Spain
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Probst L, Laloli L, Licheri MF, Licheri M, Gultom M, Holwerda M, V’kovski P, Dijkman R. Generation and Characterization of an Influenza D Reporter Virus. Viruses 2023; 15:2444. [PMID: 38140686 PMCID: PMC10747006 DOI: 10.3390/v15122444] [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: 08/31/2023] [Revised: 12/11/2023] [Accepted: 12/12/2023] [Indexed: 12/24/2023] Open
Abstract
Influenza D virus (IDV) can infect various livestock animals, such as cattle, swine, and small ruminants, and was shown to have zoonotic potential. Therefore, it is important to identify viral factors involved in the broad host tropism and identify potential antiviral compounds that can inhibit IDV infection. Recombinant reporter viruses provide powerful tools for studying viral infections and antiviral drug discovery. Here we present the generation of a fluorescent reporter IDV using our previously established reverse genetic system for IDV. The mNeonGreen (mNG) fluorescent reporter gene was incorporated into the IDV non-structural gene segment as a fusion protein with the viral NS1 or NS2 proteins, or as a separate protein flanked by two autoproteolytic cleavage sites. We demonstrate that only recombinant reporter viruses expressing mNG as an additional separate protein or as an N-terminal fusion protein with NS1 could be rescued, albeit attenuated, compared to the parental reverse genetic clone. Serial passaging experiments demonstrated that the mNG gene is stably integrated for up to three passages, after which internal deletions accumulate. We conducted a proof-of-principle antiviral screening with the established fluorescent reporter viruses and identified two compounds influencing IDV infection. These results demonstrate that the newly established recombinant IDV reporter virus can be applied for antiviral drug discovery and monitoring viral replication, adding a new molecular tool for investigating IDV.
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Affiliation(s)
- Lukas Probst
- Institute for Infectious Diseases, University of Bern, 3001 Bern, Switzerland
- Graduate School for Cellular and Biomedical Sciences, University of Bern, 3012 Bern, Switzerland
| | - Laura Laloli
- Institute for Infectious Diseases, University of Bern, 3001 Bern, Switzerland
- Graduate School for Cellular and Biomedical Sciences, University of Bern, 3012 Bern, Switzerland
| | - Manon Flore Licheri
- Institute for Infectious Diseases, University of Bern, 3001 Bern, Switzerland
| | - Matthias Licheri
- Institute for Infectious Diseases, University of Bern, 3001 Bern, Switzerland
- Graduate School for Cellular and Biomedical Sciences, University of Bern, 3012 Bern, Switzerland
| | - Mitra Gultom
- Institute for Infectious Diseases, University of Bern, 3001 Bern, Switzerland
| | - Melle Holwerda
- Institute for Infectious Diseases, University of Bern, 3001 Bern, Switzerland
| | - Philip V’kovski
- Institute for Infectious Diseases, University of Bern, 3001 Bern, Switzerland
| | - Ronald Dijkman
- Institute for Infectious Diseases, University of Bern, 3001 Bern, Switzerland
- Multidisciplinary Center for Infectious Diseases, University of Bern, 3012 Bern, Switzerland
- European Virus Bioinformatics Center, 07743 Jena, Germany
- Microscope Imaging Center, University of Bern, 3012 Bern, Switzerland
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