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多组学技术在水生生态毒理学中的应用:综述

Application of Multi-Omics Techniques in Aquatic Ecotoxicology: A Review.

作者信息

Li Boyang, Zhang Yizhang, Du Jinzhe, Liu Chen, Zhou Guorui, Li Mingrui, Yan Zhenguang

机构信息

State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China.

Research Institute for Environmental Innovation (Tianjin Binhai), Tianjin 300450, China.

出版信息

Toxics. 2025 Jul 31;13(8):653. doi: 10.3390/toxics13080653.

Abstract

Traditional ecotoxicology primarily investigates pollutant toxicity through physiological, biochemical, and single-molecular indicators. However, the limited data obtained through this approach constrain its application in the mechanistic analysis of pollutant toxicity. Omics technologies have emerged as a major research focus in recent years, enabling the comprehensive and accurate analysis of biomolecular-level changes. The integration of multi-omics technologies can holistically reveal the molecular toxicity mechanisms of pollutants, representing a primary emphasis in current toxicological research. This paper introduces the fundamental concepts of genomics, transcriptomics, proteomics, and metabolomics, while reviewing recent advancements in integrated omics approaches within aquatic toxicology. Furthermore, it provides a reference framework for the implementation of multi-omics strategies in ecotoxicological investigations. While multi-omics integration enables the unprecedented reconstruction of pollutant-induced molecular cascades and earlier biomarker discovery (notably through microbiome-metabolome linkages), its full potential requires experimental designs, machine learning-enhanced data integration, and validation against traditional toxicological endpoints within environmentally relevant models.

摘要

传统生态毒理学主要通过生理、生化和单分子指标来研究污染物毒性。然而,通过这种方法获得的数据有限,限制了其在污染物毒性机制分析中的应用。组学技术近年来已成为主要的研究重点,能够对生物分子水平的变化进行全面而准确的分析。多组学技术的整合可以全面揭示污染物的分子毒性机制,是当前毒理学研究的一个主要重点。本文介绍了基因组学、转录组学、蛋白质组学和代谢组学的基本概念,同时回顾了水生毒理学中综合组学方法的最新进展。此外,它为在生态毒理学研究中实施多组学策略提供了一个参考框架。虽然多组学整合能够以前所未有的方式重建污染物诱导的分子级联反应并更早地发现生物标志物(特别是通过微生物组-代谢组联系),但其全部潜力需要实验设计、机器学习增强的数据整合以及在环境相关模型中针对传统毒理学终点进行验证。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0962/12389999/1cdb5fe7c357/toxics-13-00653-g001.jpg

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