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Published on: June 5, 2020
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Modeling study for predicting altered cellular activity induced by nanomaterials based on Dlk1-Dio3 gene expression
Beilei Yuan1, Yunlin Wang1, Cheng Zong1
1College of Safety Science and Engineering, Nanjing Tech University, Nanjing, Jiangsu, 210009, China.
Chemosphere
|June 2, 2023
Summary
This study developed a novel prediction model for nanometal oxide biotoxicity by integrating gene expression and structural data. The enhanced nano-quantitative structure-activity relationship (QSAR) models accurately predict toxicity in lung cells, improving safety assessments.
Area of Science:
- Environmental Science
- Toxicology
- Computational Chemistry
Background:
- Nanomaterials, particularly nanometal oxides, exhibit unique properties but raise concerns regarding their biological toxicity.
- Existing quantitative structure-activity relationship (QSAR) models often lack mechanistic insights, necessitating improved prediction methods.
- Assessing the safety of nanomaterials requires robust models that account for both structural and biological factors.
Purpose of the Study:
- To develop and validate enhanced nano-QSAR models for predicting the biotoxicity of nanometal oxides.
- To integrate gene expression data with structural information for a more comprehensive toxicity assessment.
- To improve the accuracy and reliability of nanomaterial safety evaluations.
Main Methods:
- Exposure of A549 and BEAS-2B lung cells to 21 nanometal oxides.
- Assessment of cell viability using the CCK8 assay and measurement of Dlk1-Dio3 gene cluster expression.
- Construction of nano-QSAR models using Monte Carlo partial least squares (MC-PLS) incorporating structural and gene expression data.
Main Results:
- Nano-QSAR models combining gene expression and structural parameters showed superior performance compared to structure-only models.
- For A549 cells, R2 increased from 0.9044 to 0.9969, and RMSE decreased from 0.1922 to 0.0348.
- For BEAS-2B cells, R2 increased from 0.9355 to 0.9705, and RMSE decreased from 0.1206 to 0.0874.
- Model validation confirmed good prediction accuracy, generalization ability, and stability.
Conclusions:
- Integrating gene expression with structural data significantly enhances the predictive power of nano-QSAR models for nanometal oxide biotoxicity.
- The developed models offer a more systematic and reliable approach to nanomaterial safety evaluation.
- This study provides a novel perspective for assessing the biological effects of nanomaterials.
