Mechanisms Tackling Salivary Gland Diseases with Extracellular Vesicle Therapies

间充质干细胞 细胞外小泡 胞外囊泡 干细胞 叙述性评论 科学网 再生医学 小RNA 微泡 生物信息学 医学 再生(生物学) 计算生物学 生物 梅德林 细胞生物学 重症监护医学 生物化学 基因
作者
Pritsana Sawutdeechaikul,Sohyun Hwang,Supaporn Klangprapan,Thanh Vân Phan,Christopher Lam,Yong‐Jin Yoon,Sang Woo Seo,Samin Hong,Jae‐Yol Lim,João N. Ferreira
出处
期刊:Journal of Dental Research [SAGE Publishing]
标识
DOI:10.1177/00220345251319295
摘要

Extracellular vesicles (EVs) are lipid-enclosed particles released from cells, containing lipids, DNA, RNA, metabolites, and cytosolic and cell surface proteins. EVs support intercellular communication and orchestrate organogenesis by transferring bioactive molecules in between cells. Mesenchymal stem cells are known to produce EVs, which exhibit immunomodulatory and regenerative capabilities in many target organs, including the salivary glands (SGs). Since cell-based therapies still pose challenges (e.g., donor variability, limited hemocompatibility, and safety), specific EVs may constitute a therapeutic alternative for SG diseases. New EV guidelines (MISEV2023) have recently been updated and reported by our consortium to consolidate the principles of EV biology and expand the boundaries toward innovative therapies. These guidelines provide valuable guidance for researchers to consistently assess the effectiveness of mesenchymal stem cell–derived EV cargo cues, such as microRNA, proteins, and other molecules, to target SG diseases. This review provides a narrative synthesis of preclinical studies on EVs by highlighting EV mechanisms and their potential therapeutic applications for SG diseases, such as radiotherapy-induced SG hypofunction and Sjögren’s syndrome, as well as inflammatory and aging-related SG conditions. Additionally, we highlight key areas of the MISEV2023 guidelines that will support future EV-based therapies in SG research. This review adhered to PRESS guidelines (Peer Review of Electronic Search Strategies) and utilized established databases, including Medline/PubMed, Embase, Web of Science, and Scopus, alongside machine learning tools for sorting the most impactful EV studies for SG diseases.
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