Multi-omics analyses, cell experiments, and network pharmacology tools identified key proteins and candidate drugs for alopecia areata treatment

Alopecia Areata (AA) is an inflammatory non-cicatricial alopecia with a high prevalence. Some patients with AA show an inferior response to treatment. To find key proteins in AA, Genome-wide association study data from three cohorts were analyzed using Mendelian randomization (MR) method. The gene expression of the identified proteins was further evaluated and compared between AA and healthy samples from two single-cell RNA datasets (GSE212447 and GSE233906). A cell model was also built to validate the findings. Autodock Vina and GROMACS, were also employed to search for ingredients from traditional Chinese medicine(TCM) that interacted with the identified protein. Three proteins, DEFB1, HGFAC, and CYB5D2, were identified as potential drug targets. The altered gene expression in lesional samples of AA patients was consistent with the promoting or protective effects of identified proteins on the disease. The overexpression of risk factor DEFB1 upregulated the RNA and protein expression of MICA in HaCaT cells. The TCM ingredient cimigenol was found to interact with DEFB1 via molecular docking, and molecular dynamics simulations confirmed the stability of this interaction. DEFB1 is a potential drug target with promising prospects for the development of novel drugs for treating AA. The TCM ingredient, cimigenol, is a promising drug for AA treatment.

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Publication Details

Journal
Clinical Proteomics
Published
2025-05-29
DOI
https://doi.org/10.1186/s12014-025-09544-6
Primary Topic
Hair Growth and Disorders
Type
article
Field-Weighted Citation Impact
0.00

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article

Multi-omics analyses, cell experiments, and network pharmacology tools identified key proteins and candidate drugs for alopecia areata treatment

Lingbo Bi, Yujun Sheng, Jingkai Xu, Zining Xu et al.
Clinical Proteomics
Hair Growth and Disorders
article

Multi-omics analyses, cell experiments, and network pharmacology tools identified key proteins and candidate drugs for alopecia areata treatment

Lingbo Bi, Yujun Sheng, Jingkai Xu, Zining Xu, Junhua Wang, Yong Cui, Kejun Chen, Zhuang Zhou, Xianbo Zuo, Jungang Yang
article en

Abstract

Alopecia Areata (AA) is an inflammatory non-cicatricial alopecia with a high prevalence. Some patients with AA show an inferior response to treatment. To find key proteins in AA, Genome-wide association study data from three cohorts were analyzed using Mendelian randomization (MR) method. The gene expression of the identified proteins was further evaluated and compared between AA and healthy samples from two single-cell RNA datasets (GSE212447 and GSE233906). A cell model was also built to validate the findings. Autodock Vina and GROMACS, were also employed to search for ingredients from traditional Chinese medicine(TCM) that interacted with the identified protein. Three proteins, DEFB1, HGFAC, and CYB5D2, were identified as potential drug targets. The altered gene expression in lesional samples of AA patients was consistent with the promoting or protective effects of identified proteins on the disease. The overexpression of risk factor DEFB1 upregulated the RNA and protein expression of MICA in HaCaT cells. The TCM ingredient cimigenol was found to interact with DEFB1 via molecular docking, and molecular dynamics simulations confirmed the stability of this interaction. DEFB1 is a potential drug target with promising prospects for the development of novel drugs for treating AA. The TCM ingredient, cimigenol, is a promising drug for AA treatment.

Clinical ProteomicsVol. 22(1)
Chinese Academy of Medical Sciences & Peking Union Medical College (CN), China-Japan Friendship Hospital (CN), Beijing Friendship Hospital (CN)
National Natural Science Foundation of China, Natural Science Foundation of Anhui Province, National Key Research and Development Program of China, China-Japan Friendship Hospital, China-Japan Friendship Hospital Youth Science and Technology Excellence Project
Openalex Percentile: Top 83%
Hair Growth and Disorders
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