TY - JOUR
T1 - Biomarkers of nanomaterials hazard from multi-layer data
AU - Fortino, Vittorio
AU - Kinaret, Pia Anneli Sofia
AU - Fratello, Michele
AU - Serra, Angela
AU - Saarimäki, Laura Aliisa
AU - Gallud, Audrey
AU - Gupta, Govind
AU - Vales, Gerard
AU - Correia, Manuel
AU - Rasool, Omid
AU - Ytterberg, Jimmy
AU - Monopoli, Marco
AU - Skoog, Tiina
AU - Ritchie, Peter
AU - Moya, Sergio
AU - Vázquez-Campos, Socorro
AU - Handy, Richard
AU - Grafström, Roland
AU - Tran, Lang
AU - Zubarev, Roman
AU - Lahesmaa, Riitta
AU - Dawson, Kenneth
AU - Loeschner, Katrin
AU - Larsen, Erik Husfeldt
AU - Krombach, Fritz
AU - Norppa, Hannu
AU - Kere, Juha
AU - Savolainen, Kai
AU - Alenius, Harri
AU - Fadeel, Bengt
AU - Greco, Dario
N1 - Funding Information:
This work was supported by the European Commission through the Seventh Framework Program (FP7-NANOSOLUTIONS; grant agreement no. 309329). D.G. was also supported by the Academy of Finland (grant agreement no. 322761) and the EU H2020 project NanoSolveIT (grant agreement no. 814572). The authors wish to thank the following members of the FP7-NANOSOLUTIONS consortium for the provision and characterization of ENMs: Jaimé Ruiz, Didier Astruc, Alexej Antipov, Yirij Fedutik, Carsten Jost, Alexey Kalachev, Alexandros Besinis, Guocheng Wang, Nicky Ehrlich, Zeljka Krpetic, Francesco Muraca, Alejandro Vilchez, Vicenç Pomar Portillo, Jose Luiz Muñoz, Julie Muller, Nathalie Luizie, Zahraa Al-ahmady, Cyrill Bussy, and Kostas Kostarelos. We also thank Michael Persson, Nouryon (Sweden), for the provision of colloidal silica particles, and Vesa Hongisto, Ivica Letunic, and Roberto Tagliaferri for assistance with data management during the course of the project. Finally, we wish to thank Troy Faithfull, Tampere University, for language editing.
Funding Information:
This work was supported by the European Commission through the Seventh Framework Program (FP7-NANOSOLUTIONS; grant agreement no. 309329). D.G. was also supported by the Academy of Finland (grant agreement no. 322761) and the EU H2020 project NanoSolveIT (grant agreement no. 814572). The authors wish to thank the following members of the FP7-NANOSOLUTIONS consortium for the provision and characterization of ENMs: Jaimé Ruiz, Didier Astruc, Alexej Antipov, Yirij Fedutik, Carsten Jost, Alexey Kalachev, Alexandros Besinis, Guocheng Wang, Nicky Ehrlich, Zeljka Krpetic, Francesco Muraca, Alejandro Vilchez, Vicenç Pomar Portillo, Jose Luiz Muñoz, Julie Muller, Nathalie Luizie, Zahraa Al-ahmady, Cyrill Bussy, and Kostas Kostarelos. We also thank Michael Persson, Nouryon (Sweden), for the provision of colloidal silica particles, and Vesa Hongisto, Ivica Letunic, and Roberto Tagliaferri for assistance with data management during the course of the project. Finally, we wish to thank Troy Faithfull, Tampere University, for language editing.
Publisher Copyright:
© 2022, The Author(s).
PY - 2022/12
Y1 - 2022/12
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85133339386&partnerID=8YFLogxK
U2 - 10.1038/s41467-022-31609-5
DO - 10.1038/s41467-022-31609-5
M3 - Article
C2 - 35778420
AN - SCOPUS:85133339386
SN - 2041-1723
VL - 13
JO - Nature Communications
JF - Nature Communications
IS - 1
M1 - 3798
ER -