Biomarkers of nanomaterials hazard from multi-layer data.

ABSTRACT: There is an urgent need to apply effective, data-driven approaches to reliably predict engineered nanomaterial (ENM) toxicity. Here we introduce a predictive computational framework based on the molecular and phenotypic effects of a large panel of ENMs across multiple in vitro and in vivo models. Our methodology allows for the grouping of ENMs based on multi-omics approaches combined with robust toxicity tests. Importantly, we identify mRNA-based toxicity markers and extensively replicate them in multiple independent datasets. We find that models based on combinations of omics-derived features and material intrinsic properties display significantly improved predictive accuracy as compared to physicochemical properties alone.

SUBMITTER: Fortino V 

PROVIDER: S-EPMC9249793 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature