Screening Therapeutic Core Genes in Sepsis Using Network Pharmacology and Single-Cell RNA Sequencing.

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Tác giả: Guihong Chen, Yingchun Hu, Shaolan Li, Chenglin Wang, Wen Zhang

Ngôn ngữ: eng

Ký hiệu phân loại: 978.02 1800–1899

Thông tin xuất bản: United States : Biochemical genetics , 2025

Mô tả vật lý:

Bộ sưu tập: NCBI

ID: 727765

Sepsis, a life-threatening condition characterized by a systemic inflammatory response, leads to organ dysfunction and high mortality rates. Honeysuckle, a traditional herbal remedy, has shown promise in attenuating organ damage and inhibiting pro-inflammatory factors in sepsis. However, the underlying molecular mechanisms remain unclear. We employed a multi-omics approach to elucidate honeysuckle's potential therapeutic effects in sepsis. RNA sequencing was performed on blood samples from 22 sepsis patients and 10 healthy controls to identify differentially expressed genes. Network pharmacology was utilized to predict effective ingredients and therapeutic targets of honeysuckle in sepsis. Meta-analysis compared gene expression between sepsis survivors and non-survivors. Single-cell RNA sequencing was employed to localize target gene expression at the cellular level. We identified 1328 differentially expressed genes in sepsis, with 221 upregulated and 1107 downregulated. Network analysis revealed 15 genes linked to 12 honeysuckle components. Four genes-DPP4, CD40LG, BCL2, and TP53-emerged as core therapeutic targets, showing decreased expression in non-survivors but upregulation in survivors. Single-cell analysis demonstrated that these genes were primarily expressed in T cells and other immune cells, suggesting their role in regulating immune response and inflammation. This study uses single-cell RNA sequencing and network analysis to identify DPP4, CD40LG, BCL2, and TP53 as key regulatory targets in sepsis, providing insights into disease mechanisms and potential therapeutic interventions. Network pharmacology analysis suggests possible interactions with honeysuckle compounds, though experimental validation is needed.
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