Rui Tao,1,* Xuejie Chen,1,* Yingying Wang,2,* Sicheng Li,1 Shengzhi Zhou,1 Sis Aghayants,1 Lingling Yan,3 Qi Zhang,4 Zhanyong Zhu1 

1Department of Plastic Surgery, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, 430060, People’s Republic of China; 2Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, 430060, People’s Republic of China; 3Department of Plastic and Cosmetic Surgery, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, Hubei Province, 430061, People’s Republic of China; 4Department of Plastic and Cosmetic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, 430030, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Lingling Yan, Department of Plastic and Cosmetic Surgery, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, Hubei Province, People’s Republic of China, Email yanll197803@126.com Zhanyong Zhu, Department of Plastic Surgery, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, People’s Republic of China, Email zyzhu@whu.edu.cn

Background: Atopic dermatitis (AD) is a chronic inflammatory skin condition characterized by itching and rashes, influenced by genetic, environmental, and immune factors. Despite significant research, the molecular mechanisms underlying AD are not fully understood. This study aims to integrate single-cell RNA sequencing (scRNA-seq) with Mendelian Randomization (MR) to uncover genetic and metabolic pathways contributing to AD.
Materials and Methods: Data from scRNA-seq and bulk RNA sequencing datasets were analyzed to identify differentially expressed genes. The edgeR package was used for differential expression analysis, and candidate genes were explored using MR, employing eQTL data to determine causal relationships with AD. The inverse variance weighted method facilitated MR analysis, while gene set enrichment analysis (GSEA) was used to identify pathways associated with AD. Single-cell analysis was performed with the Seurat package to explore cellular heterogeneity, and pseudotime and cellular communication analyses were conducted to understand cell differentiation and interactions in AD.
Results: The study identified key genes—PCLAF, MICB, CHAD, and CA4—linked to AD, with PCLAF notably acting as a risk factor. These genes are involved in cell cycle regulation, immune evasion, cell adhesion, and metabolic processes. The MR analysis highlighted lipid, amino acid, and energy metabolism as critical pathways in AD. Single-cell analysis revealed increased cellular communication in AD, especially in Langerhans cells, keratinocytes, and T cells, signifying dysregulated immune responses and inflammatory pathways. Pseudotime analysis indicated abnormal differentiation trajectories in these cell types.
Conclusion: Our study highlights the importance of PCLAF in the pathogenesis of AD, indicating it as a potential target for future therapeutic strategies aimed at alleviating the disease by addressing genetic and metabolic disruptions.

Keywords: atopic dermatitis, Mendelian randomization, single-cell RNA sequencing, gene expression