Biomarkers of nanomaterials hazard from multi-layer data
Grafström R; Ritchie P; Rasool O; Saarimäki LA; Savolainen K; Krombach F; Serra A; Vázquez-Campos S; Moya S; Tran L; Greco D; Gupta G; Norppa H; Skoog T; Dawson K; Fratello M; Handy R; Ytterberg J; Monopoli M; Loeschner K; Zubarev R; Gallud A; Vales G; Alenius H; Fadeel B; Correia M; Lahesmaa R; Kere J; Kinaret PAS; Larsen EH; Fortino V
Biomarkers of nanomaterials hazard from multi-layer data
Grafström R
Ritchie P
Rasool O
Saarimäki LA
Savolainen K
Krombach F
Serra A
Vázquez-Campos S
Moya S
Tran L
Greco D
Gupta G
Norppa H
Skoog T
Dawson K
Fratello M
Handy R
Ytterberg J
Monopoli M
Loeschner K
Zubarev R
Gallud A
Vales G
Alenius H
Fadeel B
Correia M
Lahesmaa R
Kere J
Kinaret PAS
Larsen EH
Fortino V
NATURE PORTFOLIO
Julkaisun pysyvä osoite on:
https://urn.fi/URN:NBN:fi-fe2022091258827
https://urn.fi/URN:NBN:fi-fe2022091258827
Tiivistelmä
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.
Kokoelmat
- Rinnakkaistallenteet [19206]