Identification of Potential Drug Targets for the Treatment of Severe Burn Wounds from a Multi-Omics Perspective

Abstract

Gehua Zhu,1,* Jiaqi Li,2,* Jiamin Xu,1 Guanghua Guo,1 Feng Zhu2 1Medical Centre of Burn Plastic and Wound Repair, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, People’s Republic of China; 2Department of Critical Care Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, 200120, People’s Republic of China*These authors contributed equally to this workCorrespondence: Guanghua Guo, Medical Centre of Burn Plastic and Wound Repair, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, No. 17, Yongwai Zhengjie, Donghu District, Nanchang City, Jiangxi Province, People’s Republic of China, Email [email protected] Feng Zhu, Department of Critical Care Medicine, Shanghai East Hospital, Tongji University School of Medicine, 150 Jimo Road, Pudong New Area, Shanghai, 200120, People’s Republic of China, Email [email protected]: Severe burns result in significant skin damage, impairing its primary role as an infection barrier and presenting substantial treatment challenges. Despite improvements in the treatment of burn patients due to advancements in materials and techniques, there remains a need for novel therapeutic approaches to enhance burn prognosis further.Patients and Methods: Several types of genomic methods are used in this study, such as differential gene expression analysis, weighted gene co-expression network analysis (WGCNA), machine learning, and Mendelian randomization (MR), to find genes that are linked to severe burns and create a diagnostic nomogram to see how well these genes can predict severe burns. Drug prediction was conducted using the DsigDB database, and molecular docking was used to validate the pharmacological value of drug targets. The effects of genes and drugs on burn wounds were validated through Western Blot (WB) and cell scratch assays.Results: In patients with severe burns, multi-omics analysis revealed increased CYP19A1 expression. In severe burn cell models, WB further confirmed the elevated expression of CYP19A1. Drug prediction indicated that mevastatin binds effectively to the CYP19A1 gene expression protein. The healing area of scalded HaCat cells was much bigger after 24 hours of mevastatin treatment compared to the scald-only group, as shown by cell scratch assays after 24 and 48 hours.Conclusion: This study innovatively integrates multi-omics approaches into burn wound research, uncovering for the first time that mevastatin promotes burn wound healing by downregulating CYP19A1 expression. This discovery may provide a new foundation for developing burn wound therapeutics and potentially reduce drug development costs.Keywords: burn, WGCNA, machine learning, Mendelian randomization, drug targets, genetics

Keywords

• burn
• wgcna
• machine learning
• mendelian randomization
• drug targets
• genetics