PXD016668

PXD016668 is an original dataset announced via ProteomeXchange.

Title	Data-independent acquisition-based quantitative proteomics analysis reveals dynamic network profiles during the macrophage inflammatory response.
Description	Understanding of the molecular regulatory mechanisms underlying the inflammatory response isincomplete, especially with respect to the global proteomic response to microbial infection. Data-independent acquisition (DIA)-based quantitative proteomic analysis has been widely applied inproteomics research due to its advantages, such as improved protein coverage and reliable dataacquisition. In the present study, we focused on characterizing the proteome in a model ofinflammation in macrophages treated with lipopolysaccharide (LPS), which is important forilluminating the fundamental mechanisms of the inflammatory response to bacterial infection. Atotal of 3597 proteins were identified in macrophages with the DIA method, which provided acomprehensive view of inflammation in macrophages stimulated with LPS for different times.Bioinformatic analyses, including gene expression pattern analysis, GO enrichment analysis,KEGG pathway analysis and STRING analysis, revealed discrete modules and the underlyingmolecular mechanisms, as well as the signaling network that modulates the development ofinflammation. We found that a total of 87 differentially expressed proteins (DEPs) were shared byall stages of LPS-induced inflammation in macrophages and that 18 of these proteins participatein metabolic processes by forming a tight interaction network. Our data support the hypothesisthat ribosome proteins play a key role in regulating the macrophage response to LPS, whichprovides a novel insight into the regulation of inflammation. Interestingly, conjoint analyses of thetranscriptome and proteome in macrophages treated with LPS for 6 h revealed that the genesupregulated at both the mRNA and protein levels were mainly involved in inflammation and theimmune response, whereas the genes downregulated at both the mRNA and protein levels weresignificantly enriched in metabolism-related processes. Taken together, these results not onlyprovide a more comprehensive understanding of the molecular mechanisms of inflammationmediated by bacterial infection but also provide a dynamic proteomic resource for further studieson potential biomarkers for clinical diagnosis and protein targets for drug screening in the contextof various inflammatory diseases.
HostingRepository	iProX
AnnounceDate	2019-12-09
AnnouncementXML	Submission_2020-11-18_21:00:35.xml
DigitalObjectIdentifier
ReviewLevel	Peer-reviewed dataset
DatasetOrigin	Original dataset
RepositorySupport	Unsupported dataset by repository
PrimarySubmitter	Haihua Luo
SpeciesList	scientific name: Mus musculus; NCBI TaxID: 10090;
ModificationList	No PTMs are included in the dataset
Instrument	Orbitrap Fusion

Revision	Datetime	Status	ChangeLog Entry
0	2019-12-09 18:45:40	ID requested
1	2019-12-09 18:47:01	announced
⏵ 2	2020-11-18 21:00:36	announced	2020-11-19: Update publication information

Li L, Chen L, Lu X, Huang C, Luo H, Jin J, Mei Z, Liu J, Liu C, Shi J, Chen P, Jiang Y, Data-Independent Acquisition-Based Quantitative Proteomics Analysis Reveals Dynamic Network Profiles during the Macrophage Inflammatory Response. Proteomics, 20(2):e1900203(2020) [pubmed]

submitter keyword: lipopolysaccharide, inflammation, proteomics, transcriptome, data-independent acquisition

Prof. Yong Jiang
contact affiliation	Guangdong Provincial Key Laboratory of Proteomics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
contact email	jiang48231@163.com
lab head
Haihua Luo
contact affiliation	Guangdong Provincial Key Laboratory of Proteomics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
contact email	btxlhh@qq.com
dataset submitter

iProX dataset URI