Biomarkers of nanomaterials hazard from multi-layer data

Vittorio Fortino, Pia Anneli Sofia Kinaret, Michele Fratello, Angela Serra, Laura Aliisa Saarimäki, Audrey Gallud, Govind Gupta, Gerard Vales, Manuel Correia, Omid Rasool, Jimmy Ytterberg, Marco Monopoli, Tiina Skoog, Peter Ritchie, Sergio Moya, Socorro Vázquez-Campos, Richard Handy, Roland Grafström, Lang Tran, Roman ZubarevRiitta Lahesmaa, Kenneth Dawson, Katrin Loeschner, Erik Husfeldt Larsen, Fritz Krombach, Hannu Norppa, Juha Kere, Kai Savolainen, Harri Alenius, Bengt Fadeel, Dario Greco*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

14 Citations (Scopus)
9 Downloads (Pure)


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.

Original languageEnglish
Article number3798
JournalNature Communications
Issue number1
Publication statusPublished - Dec 2022
MoE publication typeA1 Journal article-refereed


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