TRIumph in nanotoxicology: simplifying transcriptomics into a single predictive variable

Abstract

The primary aim of our study was to address the problem of transcriptomic data complexity by introducing a novel transcriptomic response index (TRI), compressing the entire transcriptomic space into a single variable, and linking it with the inhaled multiwalled carbon nanotubes (MWCNTs) properties. This methodology allows us to predict fold change values of thousands of differentially expressed genes (DEGs) using a single variable and a single quantitative structure–activity relationship (QSAR) model. In the context of this work, TRI compressed 5167 DEGs into a single variable, explaining 99.9% of the entire transcriptomic space. Further TRI was linked to the properties of inhaled MWCNTs using a nano-QSAR model with statistics R² = 0.83, Q_CV² = 0.8, and Q² = 0.78, which show a high level of goodness-of-fit, robustness, and predictability of the obtained model. By training a nano-QSAR model on fold changes of thousands of DEGs using a single variable, our study significantly contributes not only to new approach methodologies (NAMs) focused on reducing animal testing but also decreases the amount of computational resources needed for work with complex transcriptomic data. Developed during this work, the software called ChemBioML Platform (https://chembioml.com) offers researchers a powerful free-to-use tool for training regulatory acceptable machine learning (ML) models without a strong background in programming. The ChemBioML Platform integrates the ML capabilities of Python with the advanced graphical interface of unreal engine 5, creating a bridge between scientific research and the game development industry.