基于纸喷雾电离质谱法研究罗非鱼表皮黏液的非侵入性代谢

Paper Spray Ionization Mass Spectrometry for Non-invasive Metabolomic Investigation of Epidermal Mucus in Tilapia

摘要

针对全氟化合物(perfluorinated chemicals,PFCs)难降解、在水环境与水生生物中分布广泛及其对生态环境和人体健康的危害问题,采用纸喷雾电离质谱法(paper spray ionization mass spectrometry,PS-MS)技术,建立罗非鱼表皮黏液非侵入性内源性代谢物的直接检测方法,并结合多元统计分析筛选差异代谢物,通过数据库匹配与通路富集分析解析代谢紊乱机制.结果表明:优化后的PS-MS方法可实现黏液样本的直接快速检测,单个样本分析时长≤1 min;在正离子模式下筛选出44种差异代谢物,负离子模式下筛选出40种差异代谢物,其中差异代谢物主要富集15条代谢通路,包含氨基酸代谢、能量代谢、脂质代谢及碳水化合物代谢多个类别的富集通路,证实全氟辛酸对鱼类的代谢扰动影响.该研究在实验室条件下验证了PS-MS方法可快速获取罗非鱼表皮黏液的代谢指纹,为实验室条件下非侵入性考察新兴污染物对生物代谢的影响提供了有效途径.

Abstract

Aiming at the characteristics of perfluorinated chemicals (PFCs), including refractory nature, widespread occurrence in aquatic environments and aquatic organisms, as well as their adverse impacts on ecological environment and human health, paper spray ionization mass spectrometry (PS-MS) was employed to establish a non-invasive direct detection method for endogenous metabolites in tilapia epidermal mucus. Differential metabolites were screened by multivariate statistical analysis, and the mechanism of metabolic disorders was elucidated through database matching and pathway enrichment analysis. The optimized PS-MS method allowed direct and rapid detection of mucus samples, with an analysis time of ≤1 min per sample. A total of 44 differential metabolites were screened in positive ion mode and 40 in negative ion mode. These differential metabolites were mainly enriched in 15 metabolic pathways, covering amino acid metabolism, energy metabolism, lipid metabolism, and carbohydrate metabolism, confirming the metabolic disturbance by perfluorooctanoic acid in fish. This study verified that the PS-MS method can rapidly obtain the metabolic fingerprints of tilapia epidermal mucus under laboratory conditions, providing an effective approach for the non-invasive exploration of the biological metabolic effects of emerging pollutants.

关键词

纸喷雾电离质谱法 / 表皮黏液 / 非侵入性检测 / 代谢组学

Key words

paper spray ionization mass spectrometry / epidermal mucus / non-invasive detection / metabolomics

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基金资助

吉林省科技发展计划项目(20240101168JC)