Biomarkers of nanomaterials hazard from multi-layer data

Nat Commun. 2022 Jul 1;13(1):3798. doi: 10.1038/s41467-022-31609-5.


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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Biomarkers
  • Nanostructures* / toxicity
  • RNA, Messenger / genetics


  • Biomarkers
  • RNA, Messenger