Modeling study for predicting altered cellular activity induced by nanomaterials based on Dlk1-Dio3 gene expression and structural relationships

Nanomaterials have been widely applied and developed due to its unique physicochemical characteristics, such as their small size. The environmental and biological effects caused by nanomaterials have raised concerns. In particular, some nanometal oxides have obvious biological toxicity and pose a major safety problem. The prediction model established by combining the expression levels of key genes with quantitative structure–activity relationship (QSAR) studies can predict the biotoxicity of nanomaterials by relying on both structural information and gene regulation information. This model can fill the gap of missing mechanisms in QSAR studies. In this study, we exposed A549 cells and BEAS-2B cells to 21 nanometal oxides for 24 h. Cell viability was assessed by measuring absorbance values using the CCK8 assay, and the expression levels of the Dlk1-Dio3 gene cluster were measured. By using the theoretical basis of the nano-QSAR model and the improved principles of the SMILES-based descriptors to combine specific gene expression and structural factors, new models were constructed using Monte Carlo partial least squares (MC-PLS) for the biotoxicity of the nanometal oxides on two different lung cells. The overall quality of the nano-QSAR models constructed by combining specific gene expression and structural parameters for A549 and BEAS-2B cells was better than that of the models constructed based on structural parameters only. The coefficient of determination (R2) of the A549 cell model increased from 0.9044 to 0.9969, and the Root Mean Square Error (RMSE) decreased from 0.1922 to 0.0348. The R2 of the BEAS-2B cell model increased from 0.9355 to 0.9705, and the RMSE decreased from 0.1206 to 0.0874. The model validation proved the proposed models have a good prediction, generalization ability and model stability. This study offers a new research perspective for the toxicity assessment of nanometal oxides, contributing to a more systematic safety evaluation of nanomaterials.