Data-Independent Acquisition-Based Quantitative Proteomics Analysis Reveals Dynamic Network Profiles during the Macrophage Inflammatory Response

基于数据独立采集的定量蛋白质组学分析揭示巨噬细胞炎症反应过程中的动态网络特征

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作者:Lei Li, Li Chen, Xinya Lu, Chenyang Huang, Haihua Luo, Jingmiao Jin, Zhuzhong Mei, Jinghua Liu, Cuiting Liu, Junmin Shi, Peng Chen, Yong Jiang

Abstract

Understanding of the molecular regulatory mechanisms underlying the inflammatory response is incomplete. The present study focuses on characterizing the proteome in a model of inflammation in macrophages treated with lipopolysaccharide (LPS). A total of 3597 proteins are identified in macrophages with the data-independent acquisition (DIA) method. Bioinformatic analyses reveal discrete modules and the underlying molecular mechanisms, as well as the signaling network that modulates the development of inflammation. It is found that a total of 87 differentially expressed proteins are shared by all stages of LPS-induced inflammation in macrophages and that 18 of these proteins participate in metabolic processes by forming a tight interaction network. Data support the hypothesis that ribosome proteins play a key role in regulating the macrophage response to LPS. Interestingly, conjoint analyses of the transcriptome and proteome in macrophages treated with LPS reveal that the genes upregulated at both the mRNA and protein levels are mainly involved in inflammation and the immune response, whereas the genes downregulated are significantly enriched in metabolism-related processes. These results not only provide a more comprehensive understanding of the molecular mechanisms of inflammation mediated by bacterial infection but also provide a dynamic proteomic resource for further studies.

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