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Temporal proteomic characterization of COVID-19 infected mouse lungs

ABSTRACT: We performed time-course proteomic profiling of lung tissues obtained from K18-hACE2 transgenic mice with TMT-based quantification using LC-MS/MS. The Proteome Discoverer analysis identified 10,844 protein groups, 8,279 of which were quantifiable. As a result of the comparison between our proteome data and the previously reported proteomes of COVID-19 infected lung tissues generated using LC-MS/MS analysis, we demonstrated the high comprehensiveness of our temporal proteome of COVID-19 infected lung tissues. To assess the correlation between our proteome and the proteins involved in COVID-19 pathophysiology, we examined the cellular processes of our proteome by performing the enrichment analysis of Gene Ontology biological processes (GOBP) and ConsensusPathDB software. In addition, we detailed temporal transitions of perturbed molecular networks activated along with COVID-19 infection by conducting clustering analysis that identifies early, middle, and late-responsive proteins. Also, we identified two key pathways, neutrophil extracellular trap formation (NETosis) and ciliogenesis, which can induce the severity-associated transition of the disease.

INSTRUMENT(S):

ORGANISM(S): Mus Musculus (mouse)

TISSUE(S): Lung

SUBMITTER: Dongyoon Shin

LAB HEAD: Youngsoo Kim

PROVIDER: PXD036193 | Pride | 2026-07-03

REPOSITORIES: Pride

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			Action	DRS
	COVID19_TMT.mgf	Mgf
	COVID19_TMT.msf	Msf
	COVID19_TMT.mzid	Mzid
	COVID19_TMT.pep.xml	Pepxml
	COVID19_TMT.prot.xml	Xml

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