Biomarkers of nanomaterials hazard from multi-layer data
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 (2022-07)
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
07 / 2022
3798
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202208056242
https://urn.fi/URN:NBN:fi:tuni-202208056242
Kuvaus
Peer reviewed
Tiivistelmä
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.
Kokoelmat
- TUNICRIS-julkaisut [17001]