Time-Course Transcriptome Analysis Reveals Distinct Phases and Identifies Two Key Genes during Severe Fever with Thrombocytopenia Syndrome Virus Infection in PMA-Induced THP-1 Cells
In recent years, there have been significant advancements in the research of Severe Fever with Thrombocytopenia Syndrome Virus (SFTSV). However, several limitations and challenges still exist. For instance, researchers face constraints regarding experimental conditions and the feasibility of sample acquisition for studying SFTSV. To enhance the quality and comprehensiveness of SFTSV research, we opted to employ PMA-induced THP-1 cells as a model for SFTSV infection. Multiple time points of SFTSV infection were designed to capture the dynamic nature of the virus-host interaction. Through a comprehensive analysis utilizing various bioinformatics approaches, including diverse clustering methods, MUfzz analysis, and LASSO/Cox machine learning, we performed dynamic analysis and identified key genes associated with SFTSV infection at the host cell transcriptomic level. Notably, successful clustering was achieved for samples infected at different time points, leading to the identification of two important genes, PHGDH and NLRP12. And these findings may provide valuable insights into the pathogenesis of SFTSV and contribute to our understanding of host-virus interactions.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:16 |
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| Enthalten in: |
Viruses - 16(2023), 1 vom: 29. Dez. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Huang, Tao [VerfasserIn] |
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| Links: |
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| Themen: |
Journal Article |
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| Anmerkungen: |
Date Completed 24.01.2024 Date Revised 29.01.2024 published: Electronic Citation Status MEDLINE |
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| doi: |
10.3390/v16010059 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM367482495 |
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| 520 | |a In recent years, there have been significant advancements in the research of Severe Fever with Thrombocytopenia Syndrome Virus (SFTSV). However, several limitations and challenges still exist. For instance, researchers face constraints regarding experimental conditions and the feasibility of sample acquisition for studying SFTSV. To enhance the quality and comprehensiveness of SFTSV research, we opted to employ PMA-induced THP-1 cells as a model for SFTSV infection. Multiple time points of SFTSV infection were designed to capture the dynamic nature of the virus-host interaction. Through a comprehensive analysis utilizing various bioinformatics approaches, including diverse clustering methods, MUfzz analysis, and LASSO/Cox machine learning, we performed dynamic analysis and identified key genes associated with SFTSV infection at the host cell transcriptomic level. Notably, successful clustering was achieved for samples infected at different time points, leading to the identification of two important genes, PHGDH and NLRP12. And these findings may provide valuable insights into the pathogenesis of SFTSV and contribute to our understanding of host-virus interactions | ||
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| 700 | 1 | |a Wu, Wei |e verfasserin |4 aut | |
| 700 | 1 | |a Xu, Xiao |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Chuan |e verfasserin |4 aut | |
| 700 | 1 | |a Wen, Yanhan |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Boyang |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Yang |e verfasserin |4 aut | |
| 700 | 1 | |a Sun, Lina |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Jiandong |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Mengxuan |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Tiezhu |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Shiwen |e verfasserin |4 aut | |
| 700 | 1 | |a Liang, Mifang |e verfasserin |4 aut | |
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