Quantitative analysis of differentially expressed proteins in psoriasis vulgaris using tandem mass tags and parallel reaction monitoring

Clin Proteomics. 2020 Aug 12:17:30. doi: 10.1186/s12014-020-09293-8. eCollection 2020.

Abstract

Background: Psoriasis vulgaris (PV) is a chronic autoimmune inflammatory disease with epidermal hyperkeratosis and parakeratosis.

Methods: The study was to elucidate the pathogenesis of PV by quantitative proteomic analysis of skin lesion biopsies of PV and healthy tissues with tandem mass tags (TMTs) coupled with liquid chromatography-mass spectrometry (LC-MS)/MS.

Results: A total of 4562 differentially expressed proteins (DEPs) between PV lesional tissues (n = 11) and healthy tissues (n = 11) were identified, of which 299 were upregulated and 206 were downregulated using |fold change| > 1.3 as the cutoff threshold. The Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed that the DEPs were mainly enriched in the activation of immune cells (drug metabolism pathway, NOD-like pathway, and IL-17 pathway), cell proliferation (ribosomal pathway, DNA replication pathway, and base replication pathway), metabolism-related pathways (fatty acid biosynthesis and metabolism, PPAR pathway, glycerophospholipid metabolism, and cortisol synthesis and breakdown), and glandular secretion (saliva secretion, gastric acid secretion, and pancreatic fluid secretion). Thirteen DEPs that were relatively highly expressed in the drug metabolism pathway were validated with parallel reaction monitoring (PRM), of which MPO, TYMP, IMPDH2, GSTM4, and ALDH3A1 were highly expressed in PV, whereas CES1, MAOB, MGST1, and GSTT1 were less expressed in PV.

Conclusions: These findings confirmed that these proteins participate in the drug metabolism-other enzyme pathways and play crucial roles in the activation and proliferation of immune cells in the pathogenesis of PV.

Keywords: Differentially expressed proteins; Drug metabolism pathway; Psoriasis vulgaris; Quantitative proteomics.