机器学习辅助多重复合光标记miRNAs成像解码用于组合菌毒素毒性评估
Lixin Kang1,2, Xianfeng Lin1,2, Jiaqi Feng1,2
1State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi 214122, China.
Analytical chemistry
|April 3, 2025
概括
这项研究开发了一个新的检测平台,可以同时测量微RNA (miRNA) 和评估食物污染物中脱氧尼瓦伦 (DON) 和泽拉伦 (ZEN) 的联合毒性. 机器学习揭示了这些真菌毒素之间的复杂相互作用,影响细胞健康.
科学领域:
- 生物化学 生物化学
- 毒理学 毒理学 毒理学
- 分子生物学分子生物学
背景情况:
- 诸如deoxynivalenol (DON) 和zearalenone (ZEN) 这样的真菌毒素是普遍存在的食品污染物,通常在谷物中同时存在.
- 它们的联合存在对健康构成重大风险,需要评估协同作用或对抗作用的毒性作用的方法.
研究的目的:
- 开发一个多重检测平台,同时量化和成像三个特定的microRNAs (miRNAs).
- 整合机器学习以评估基于miRNA表达型的DON和ZEN的联合毒性.
- 建立一个敏感的分析工具,用于多元组件毒性评估.
主要方法:
- 开发用于miR-21,miR-221和miR-27a的高度敏感的光分子信标探测器 (MBs),使用外核酶III辅助信号放大.
- 脂质体介导的内细胞解剖对于MBs的有效的细胞内传递,使同时的miRNA成像成为可能.
- 机器学习算法 (LDA,PCA) 与光图像的RGB值的集成,用于分类miRNA表达模式和评估组合毒性.
主要成果:
- 目标miRNAs的显著检测极限得到实现 (0.18 pM用于miR-21,0.22 pM用于miR-221,0.21 pM用于miR-27a).
- 该平台成功实现了三种miRNA的同时细胞内成像.
- 机器学习模型准确地分类了miRNA表达模式,并揭示了ZEN与DON具有剂量依赖的对抗和协同效应.
结论:
- 开发的敏感多重检测方法显示了miRNA表达特征和DON/ZEN毒性之间的强烈相关性.
- 该平台提供了一种创新的分析工具,用于评估与食品污染物同时发生的复杂毒理相互作用.
- 这些发现突出了miRNA分析在理解和评估食品安全中的多组分毒性方面的潜力.
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