Biomarkers of nanomaterials hazard from multi-layer data

Fortino, Vittorio; Kinaret, Pia Anneli Sofia; Fratello, Michele; Serra, Angela; Saarimaki, Laura Aliisa; Gallud, Audrey; Gupta, Govind; Vales, Gerard; Correia, Manuel; Rasool, Omid; Ytterberg, Jimmy; Monopoli, Marco; Skoog, Tiina; Ritchie, Peter; Moya, Sergio; Vazquez-Campos, Socorro; Handy, Richard; Grafstrom, 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 and Greco, Dario (2022): Biomarkers of nanomaterials hazard from multi-layer data. In: Nature Communications, Vol. 13, No. 1, 3798

Abstract

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.

• Item Type: Journal article
• Faculties: Medicine
• Subjects: 600 Technology > 610 Medicine and health
• Language: English