Computational Toxicogenomic Assessment of Inhalation Risks from Titanium Dioxide Sanding-Dust Particulate Emissions

Mechanical processing of titanium dioxide-containing coatings and composites generates airborne particulate emissions that contribute to occupational and near-field environmental exposure. Titanium dioxide nanoparticles (TiO₂-NPs) are widely used in industry, and their potential to cause adverse effects in the respiratory system raises concerns for both environmental impact and human health risk assessment. Importantly, new approach methodologies (NAM)-relevant computational toxicogenomic models for TiO₂-NPs are derived almost exclusively from studies of “free” nanoparticles in simplified media, whereas real-world scenarios involve complex sanding-dust (SD) mixtures. We investigated whether inhalation of TiO₂-NP SD produces lung transcriptomic responses comparable to those induced by free TiO₂-NPs, and whether existing transcriptomic biomarkers can serve as mechanistic monitoring tools for SD exposure.

We analyzed in vivo lung transcriptomic data for mice exposed to free TiO₂-NPs and SD emissions across multiple doses (54–486 μg/animal), post-exposure periods (1–28 days), and particle sizes (10–38 nm), yielding 649 differentially expressed genes across 54 exposure scenarios. Unsupervised machine learning methods (principal component analysis, hierarchical clustering, correlation analysis) were applied to identify global patterns and relate SD-induced signatures to established TiO₂-NP biomarkers.

Analyses showed that SD-induced profiles clustered closely with free TiO₂-NP exposures, indicating broadly comparable transcriptomic responses. We further observed that many key biomarkers previously perturbed after free TiO₂-NP inhalation (Saa, Ccl, Cxcl, Il families relevant to acute neutrophilic inflammation and chemokine-driven response) are strongly upregulated following SD inhalation at 1 day post-exposure, indicating a pronounced acute response to sanding-generated particulate matter relative to later time points. These findings support the extension of NAM-relevant computational toxicogenomic approaches and transcriptomic biomarkers from free TiO₂-NPs to more realistic particulate emissions, thereby strengthening mechanism-based environmental impact and regulatory-relevant health risk assessment of TiO₂-containing materials.

Funded via the Polish National Science Centre in the frame of the TransNANO project (UMO-2020/37/B/ST5/01894).