Refereed journal article or data article (A1)
Biomarkers of nanomaterials hazard from multi-layer data
List of Authors: Fortino Vittorio, Kinaret Pia Anneli Sofia, Fratello Michele, Serra Angela, Saarimäki Laura Aliisa, Gallud Audrey, Gupta Govind, Vales Gerard, Correia Manuel, Rasool Omid, Ytterberg Jimmy, Monopoli Marco, Skoog Tiina, Ritchie Peter, Moya Sergio, Vázquez-Campos Socorro, Handy Richard, Grafström Roland, Tran Lang, Zubarev Roman, Lahesmaa Riitta, Dawson Kenneth, Loeschner Katrin, Larsen Erik Husfeldt, Krombach Fritz, Norppa Hannu, Kere Juha, Savolainen Kai, Alenius Harri, Fadeel Bengt, Greco Dario
Publisher: Nature Portfolio
Publication year: 2022
Journal: Nature Communications
Journal name in source: NATURE COMMUNICATIONS
Journal acronym: NAT COMMUN
Volume number: 13
Number of pages: 10
DOI: http://dx.doi.org/10.1038/s41467-022-31609-5
URL: https://www.nature.com/articles/s41467-022-31609-5
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/175995977
Nanomaterials have a range of potential applications, however, toxicity remains a concern, limiting application and requiring extensive testing. Here, the authors report on a predictive framework made using a range of tests linking materials properties with toxicity, allowing the prediction of toxicity from physiochemical and biological properties.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.
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