Metabolomics Reveals Molecular Signatures for Psoriasis Biomarkers and Drug Targets Discovery

Abstract

Purpose

Psoriasis is a chronic, multi-system skin disease that can be influenced by immunological, environmental, and genetic factors. Plasma metabolomic analysis can provide a great deal of information on potential diagnostic biomarkers, pathogenesis and personalized treatment. However, the role of metabolites in psoriasis is unknown.

Patients and Methods

We performed an untargeted metabolomic analysis of plasma based on high-resolution liquid chromatography mass spectrometry from 10 plaque psoriasis patients and 10 healthy controls.

Results

A total of 301 differential metabolites were detected, of which 10 metabolites were possible potential biomarkers, including vitamins, amino acids, and lipids. At the same time, KEGG pathway enrichment analysis was performed for all detected differential metabolites, and it was found that protein digestion and absorption, amino acid metabolism and lipid metabolism may be jointly involved in regulating the pathogenesis of psoriasis. In addition, the proteins ESR1, OPRM1 and HSD11B1 were identified as possible potential topical therapeutic targets for psoriasis through analysis of the metabolite-protein interaction network.

Conclusion

In this study, we identified 10 differential metabolites as possible potential combinatorial biomarkers for the diagnosis of psoriasis. 12 metabolic pathways were significantly enriched that may be closely related to the occurrence and development of psoriasis. Three proteins, ESR1, OPRM1, and HSD11B1, were identified as possible potential therapeutic targets for psoriasis.

Keywords:

• psoriasis
• metabolomics
• biomarker
• KEGG pathway
• therapeutic target

Data Sharing Statement

The raw data of mass spectrometry are deposited in the MetaboLights database⁵² and can be accessed at www.ebi.ac.uk/metabolights/MTBLS5185.

Disclosure

The authors report no conflict of interest.

Additional information

Funding

This study received financial support from the Xingtai Science and Technology Plan in 2017 (Project number 2017ZC051).