From the laboratory to the food systems: translational barriers and future directions for quorum sensing inhibition

群体感应 计算机科学 生物 计算生物学 背景(考古学) 钥匙(锁) 医学 工程类 人类健康 化学
作者
Chen Niu,Jing Yang,Wang Yaqi,Chaofan Kong,Yahong Yuan,Yue Tianli
出处
期刊:Trends in Food Science and Technology [Elsevier BV]
卷期号:177: 106025-106025
标识
DOI:10.1016/j.tifs.2026.106025
摘要

Background Quorum sensing inhibition is a non-lethal control strategy that modulates collective microbial behaviours by disrupting intercellular communication, providing a potential approach for microbial control in food systems. Although laboratory studies have demonstrated that quorum-sensing inhibitors (QSIs) modulate biofilm formation, virulence expression and spoilage-associated phenotypes, their translation to real-world food systems remains limited. The influence of food microecosystems on QSI efficacy remains insufficiently understood. Scope and approach This review analyses the barriers limiting QSI application in food systems from the perspective of food microbial ecology. Quorum sensing (QS) regulatory networks are coupled across multiple circuits and integrated with global regulators, enabling microorganisms to buffer the effects of QSIs. Meanwhile, phase partitioning, nonspecific binding, spatial constraints and environmental changes also reduce QSI effective exposure within food matrices. Microbial adaptive reconfiguration and ecological processes, including community interactions, functional compensation and resource sharing, may further weaken QSI-mediated control of spoilage-associated processes. Based on these insights, this review discusses translational approaches involving QS intervention node selection, matrix-adapted delivery and integration with processing and preservation operations. Key findings and conclusions The translation of QSIs into food systems requires researchers to move beyond single-strain models toward systematic evaluation in food microecosystems. Future studies should integrate food matrix characteristics, microbial community dynamics and QS regulatory networks to identify suitable ecological contexts and operating conditions for reliable QSI applications.
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