Machine Learning-Assisted Multiplexed Fluorescence-Labeled miRNAs Imaging Decoding for Combined Mycotoxins Toxicity...

Area of Science:

• Biochemistry
• Toxicology
• Molecular Biology

Background:

• Mycotoxins like deoxynivalenol (DON) and zearalenone (ZEN) are prevalent food contaminants, often co-occurring in grains.
• Their combined presence poses significant health risks, necessitating methods to assess synergistic or antagonistic toxic effects.

Purpose of the Study:

• To develop a multiplexed detection platform for simultaneous quantification and imaging of three specific microRNAs (miRNAs).
• To integrate machine learning for evaluating the combined toxicity of DON and ZEN based on miRNA expression profiles.
• To establish a sensitive analytical tool for multicomponent toxicity assessment.

Main Methods:

• Development of highly sensitive fluorescent molecular beacon probes (MBs) for miR-21, miR-221, and miR-27a, utilizing Exonuclease III-assisted signal amplification.
• Liposome-mediated endocytosis for efficient intracellular delivery of MBs, enabling simultaneous miRNA imaging.
• Integration of machine learning algorithms (LDA, PCA) with RGB values from fluorescence images for classifying miRNA expression patterns and assessing combined toxicity.

Main Results:

• Remarkable detection limits for the targeted miRNAs were achieved (0.18 pM for miR-21, 0.22 pM for miR-221, 0.21 pM for miR-27a).
• The platform successfully enabled simultaneous intracellular imaging of the three miRNAs.
• Machine learning models accurately classified miRNA expression patterns and revealed that ZEN exhibits dose-dependent antagonistic and synergistic effects with DON.

Conclusions:

• The developed sensitive, multiplexed detection method shows a strong correlation between miRNA expression profiles and DON/ZEN toxicity.
• This platform offers an innovative analytical tool for assessing the complex toxicological interactions of co-occurring food contaminants.
• The findings highlight the potential of miRNA profiling in understanding and evaluating multicomponent toxicity in food safety.