[Percellome Project: research on molecular mechanisms of toxicological responses based on transcriptomics and...

Area of Science:

• Toxicogenomics
• Computational Biology
• Biomedical Informatics

Background:

• Understanding chemical toxicity mechanisms is crucial for safety assessment.
• Existing methods for predicting toxicity have limitations.
• The 3Rs principle (Replacement, Reduction, Refinement) guides ethical animal testing.

Purpose of the Study:

• To construct the Percellome Database for toxicity prediction.
• To elucidate molecular mechanisms of chemical toxicity.
• To develop and refine in silico toxicity prediction technologies.

Main Methods:

• Generating transcriptomes from mice exposed to various chemicals.
• Analyzing transcriptome similarity for acute toxicity prediction.
• Investigating transcriptome-epigenome relationships (histone modification, DNA methylation) for repeated dose toxicity.
• Implementing artificial intelligence for enhanced analysis and prediction.

Main Results:

• Demonstrated potential for predicting acute toxicity via transcriptome similarity.
• Initiated analysis of transcriptome-epigenome interactions for repeated dose toxicity.
• Developing AI-driven approaches to improve data analysis efficiency and scale.

Conclusions:

• The Percellome Database is a valuable resource for toxicogenomics research.
• AI integration enhances the potential of toxicogenomics for toxicity prediction and mechanism elucidation.
• This approach supports the optimization of toxicity testing protocols and promotes the 3Rs principle.