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Li Y, Han L, Li P, Ge J, Xue Y, Chen L. Potential network markers and signaling pathways for B cells of COVID-19 based on single-cell condition-specific networks. BMC Genomics 2023; 24:619. [PMID: 37853311 PMCID: PMC10583333 DOI: 10.1186/s12864-023-09719-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 10/05/2023] [Indexed: 10/20/2023] Open
Abstract
To explore the potential network markers and related signaling pathways of human B cells infected by COVID-19, we performed standardized integration and analysis of single-cell sequencing data to construct conditional cell-specific networks (CCSN) for each cell. Then the peripheral blood cells were clustered and annotated based on the conditional network degree matrix (CNDM) and gene expression matrix (GEM), respectively, and B cells were selected for further analysis. Besides, based on the CNDM of B cells, the hub genes and 'dark' genes (a gene has a significant difference between case and control samples not in a gene expression level but in a conditional network degree level) closely related to COVID-19 were revealed. Interestingly, some of the 'dark' genes and differential degree genes (DDGs) encoded key proteins in the JAK-STAT pathway, which had antiviral effects. The protein p21 encoded by the 'dark' gene CDKN1A was a key regulator for the COVID-19 infection-related signaling pathway. Elevated levels of proteins encoded by some DDGs were directly related to disease severity of patients with COVID-19. In short, the proteins encoded by 'dark' genes complement some missing links in COVID-19 and these signaling pathways played an important role in the growth and activation of B cells.
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Affiliation(s)
- Ying Li
- School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang, 471023, China
- Longmen Laboratory, Luoyang, 471003, Henan, China
| | - Liqin Han
- School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang, 471023, China
- Longmen Laboratory, Luoyang, 471003, Henan, China
| | - Peiluan Li
- School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang, 471023, China.
- Longmen Laboratory, Luoyang, 471003, Henan, China.
| | - Jing Ge
- Shanghai Immune Therapy Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200032, China
| | - Yun Xue
- College of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang, 471023, China
| | - Luonan Chen
- Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, 201100, China.
- Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310000, China.
- School of Life Science and Technology, ShanghaiTech University, Shanghai, 201100, China.
- West China Biomedical Big Data Center, Med-X Center for Informatics, West China Hospital, Sichuan University, Chengdu, 610041, China.
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