Transcriptomics-Based and AOP-Informed Structure-Activity Relationships to Predict Pulmonary Pathology Induced by...

Area of Science:

• Toxicology and Nanomaterial Safety
• Computational Chemistry
• Bioinformatics

Background:

• Adverse Outcome Pathways (AOPs) provide a framework for understanding toxicity.
• Lung fibrosis involves key events (KEs) such as tissue inflammation.
• Quantitative Structure-Activity Relationships (QSAR) can predict chemical toxicity.

Purpose of the Study:

• To develop a novel Nano-QSAR strategy for predicting transcriptomic pathway responses.
• To model lung tissue inflammation as a key event in the lung fibrosis AOP.
• To correlate multiwalled carbon nanotube (MWCNT) properties with pathway-level biological responses.

Main Methods:

• Analysis of transcriptomic profiles from mouse lungs exposed to ten MWCNTs.
• Application of statistical and bioinformatics tools to identify perturbed pathways.
• Development of Nano-QSAR models based on canonical pathways and MWCNT aspect ratio (κ).

Main Results:

• Three specific pathways ('agranulocyte adhesion and diapedesis,' 'granulocyte adhesion and diapedesis,' 'acute phase signaling') were identified as relevant to lung inflammation and fibrosis AOP.
• A direct correlation was established between MWCNT aspect ratio (κ) and the benchmark doses (BMDs) of the identified pathways.
• A MWCNT grouping strategy based on pathway-associated gene κ-values was proposed.

Conclusions:

• The study establishes a robust methodology for QSAR construction utilizing canonical pathways.
• The findings demonstrate the utility of the AOP framework in guiding QSAR modeling.
• Nano-QSAR outcomes can refine AOPs, specifically for lung fibrosis, by linking MWCNT properties to biological responses.