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Zebrafish as a translational filter for natural sleep modulators

斑马鱼 神经科学 光遗传学 机制(生物学) 接口 睡眠(系统调用) 计算机科学 计算生物学 鉴定(生物学) 生物神经网络 转化研究 生物 认知 吞吐量 药物发现 医学 生物信息学 清醒 天然产物 睡眠障碍
作者
Xiaohui Tan,Zemei Nong,Xiaohong Deng,Chenxin Xie,Ji Zhang,Liping Wang,Ganlin Chen
出处
期刊:Frontiers in Nutrition [Frontiers Media]
卷期号:13
标识
DOI:10.3389/fnut.2026.1913649
摘要

The escalating global burden of sleep disorders and their tight association with cognitive decline, metabolic abnormalities, and psychiatric comorbidities constitute a major public health challenge. Long term use of current chemically synthesized sedative hypnotics is constrained by dependence, tolerance, and cognitive side effects, creating an urgent demand for safer, mechanism traceable nutritional intervention strategies. Plant derived natural products, owing to their structural diversity, broad spectrum target potential, and favorable safety profiles, represent a critical reservoir of next generation sleep modulating functional factors for precision nutrition. However, their development has long been hindered by a systemic translational gap between high throughput in vitro screening and physiologically relevant in vivo validation, leaving numerous potentially bioactive molecules stranded at the preclinical stage. The zebrafish, as a genetically tractable vertebrate model, exhibits deep evolutionary conservation with mammals in sleep regulatory behavioral outputs, core neural circuits (monoaminergic, GABAergic, orexin/hypocretin, and melatonin systems), and molecular clock mechanisms, alongside unique technical advantages including optical transparency, rapid development, and cost effective scalability. Here, we narratively review the application of zebrafish in evaluating the sleep modulating activity of natural products, and propose a conceptual, forward-looking, integrated, scalable in vivo platform that unifies behavioral phenotyping, neural circuit interrogation, and molecular target validation into a coherent pipeline—from high throughput phenotypic hit identification to mechanism guided lead prioritization. By interfacing with artificial intelligence assisted behavioral analytics, human organoid validation, and cross species data integration, this framework bridges the structural discovery validation gap that has historically impeded natural product sleep research. Importantly, we position the zebrafish not as a direct surrogate for clinical efficacy, but as a proposed translational filter and hypothesis generating engine that may accelerate the conversion of plant derived, mechanism defined sleep promoters into reproducible, quantifiable paradigms for functional food development and precision nutritional intervention.
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