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 Zubarev, Riitta Lahesmaa, Kenneth Dawson, Katrin Loeschner, Erik Husfeldt Larsen, Fritz Krombach, Hannu Norppa, Juha Kere, Kai Savolainen, Harri Alenius, Bengt Fadeel and Dario Greco ()
Additional contact information
Vittorio Fortino: University of Eastern Finland
Pia Anneli Sofia Kinaret: Tampere University
Michele Fratello: Tampere University
Angela Serra: Tampere University
Laura Aliisa Saarimäki: Tampere University
Audrey Gallud: Karolinska Institutet
Govind Gupta: Karolinska Institutet
Gerard Vales: Finnish Institute of Occupational Health
Manuel Correia: Technical University of Denmark
Omid Rasool: University of Turku, and Åbo Akademi University
Jimmy Ytterberg: Karolinska Institutet
Marco Monopoli: Royal College of Surgeons in Ireland
Tiina Skoog: Karolinska Institutet
Peter Ritchie: Institute of Occupational Medicine
Sergio Moya: CIC biomaGUNE
Socorro Vázquez-Campos: Leitat Technological Center
Richard Handy: University of Plymouth
Roland Grafström: Karolinska Institutet
Lang Tran: Institute of Occupational Medicine
Roman Zubarev: Karolinska Institutet
Riitta Lahesmaa: University of Turku, and Åbo Akademi University
Kenneth Dawson: University College Dublin
Katrin Loeschner: Technical University of Denmark
Erik Husfeldt Larsen: Technical University of Denmark
Fritz Krombach: Ludwig-Maximilians-Universität München
Hannu Norppa: Finnish Institute of Occupational Health
Juha Kere: Karolinska Institutet
Kai Savolainen: Finnish Institute of Occupational Health
Harri Alenius: Karolinska Institutet
Bengt Fadeel: Karolinska Institutet
Dario Greco: Tampere University

Nature Communications, 2022, vol. 13, issue 1, 1-10

Abstract: Abstract 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.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-31609-5

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DOI: 10.1038/s41467-022-31609-5

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