Biomarkers of nanomaterials hazard from multi-layer data

V Fortino; PAS Kinaret; M Fratello; A Serra; LA Saarimäki; A Gallud; G Gupta; G Vales; M Correia; O Rasool; J Ytterberg; M Monopoli; T Skoog; P Ritchie; S Moya; S Vázquez-Campos; R Handy; R Grafström; L Tran; R Zubarev; R Lahesmaa; K Dawson; K Loeschner; EH Larsen; F Krombach; H Norppa; J Kere; K Savolainen; H Alenius; B Fadeel; D Greco

doi:10.1038/s41467-022-31609-5

Biomarkers of nanomaterials hazard from multi-layer data

V Fortino, PAS Kinaret, M Fratello, A Serra, LA Saarimäki, A Gallud, G Gupta, G Vales, M Correia, O Rasool, J Ytterberg, M Monopoli, T Skoog, P Ritchie, S Moya, S Vázquez-Campos, R Handy, R Grafström, L Tran, R ZubarevR Lahesmaa, K Dawson, K Loeschner, EH Larsen, F Krombach, H Norppa, J Kere, K Savolainen, H Alenius, B Fadeel, D Greco

School of Biological and Marine Sciences

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Abstract

AbstractThere 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.

Original language	English
Number of pages	0
Journal	Nature Communications
Volume	13
Issue number	1
Early online date	1 Jul 2022
DOIs	https://doi.org/10.1038/s41467-022-31609-5
Publication status	Published - 1 Jul 2022

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Biomarkers of nanomaterials hazard from multi-layer dataLicence: CC BY-NC-ND

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Fortino, V., Kinaret, PAS., Fratello, M., Serra, A., Saarimäki, LA., Gallud, A., Gupta, G., Vales, G., Correia, M., Rasool, O., Ytterberg, J., Monopoli, M., Skoog, T., Ritchie, P., Moya, S., Vázquez-Campos, S., Handy, R., Grafström, R., Tran, L., ... Greco, D. (2022). Biomarkers of nanomaterials hazard from multi-layer data. Nature Communications, 13(1). https://doi.org/10.1038/s41467-022-31609-5

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title = "Biomarkers of nanomaterials hazard from multi-layer data",

abstract = "AbstractThere 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.",

author = "V Fortino and PAS Kinaret and M Fratello and A Serra and LA Saarim{\"a}ki and A Gallud and G Gupta and G Vales and M Correia and O Rasool and J Ytterberg and M Monopoli and T Skoog and P Ritchie and S Moya and S V{\'a}zquez-Campos and R Handy and R Grafstr{\"o}m and L Tran and R Zubarev and R Lahesmaa and K Dawson and K Loeschner and EH Larsen and F Krombach and H Norppa and J Kere and K Savolainen and H Alenius and B Fadeel and D Greco",

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Fortino, V, Kinaret, PAS, Fratello, M, Serra, A, Saarimäki, LA, Gallud, A, Gupta, G, Vales, G, Correia, M, Rasool, O, Ytterberg, J, Monopoli, M, Skoog, T, Ritchie, P, Moya, S, Vázquez-Campos, S, Handy, R, Grafström, R, Tran, L, Zubarev, R, Lahesmaa, R, Dawson, K, Loeschner, K, Larsen, EH, Krombach, F, Norppa, H, Kere, J, Savolainen, K, Alenius, H, Fadeel, B & Greco, D 2022, 'Biomarkers of nanomaterials hazard from multi-layer data', Nature Communications, vol. 13, no. 1. https://doi.org/10.1038/s41467-022-31609-5

TY - JOUR

T1 - Biomarkers of nanomaterials hazard from multi-layer data

AU - Fortino, V

AU - Kinaret, PAS

AU - Fratello, M

AU - Serra, A

AU - Saarimäki, LA

AU - Gallud, A

AU - Gupta, G

AU - Vales, G

AU - Correia, M

AU - Rasool, O

AU - Ytterberg, J

AU - Monopoli, M

AU - Skoog, T

AU - Ritchie, P

AU - Moya, S

AU - Vázquez-Campos, S

AU - Handy, R

AU - Grafström, R

AU - Tran, L

AU - Zubarev, R

AU - Lahesmaa, R

AU - Dawson, K

AU - Loeschner, K

AU - Larsen, EH

AU - Krombach, F

AU - Norppa, H

AU - Kere, J

AU - Savolainen, K

AU - Alenius, H

AU - Fadeel, B

AU - Greco, D

PY - 2022/7/1

Y1 - 2022/7/1

N2 - AbstractThere 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 - AbstractThere 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.

U2 - 10.1038/s41467-022-31609-5

DO - 10.1038/s41467-022-31609-5

M3 - Article

SN - 2041-1723

VL - 13

JO - Nature Communications

JF - Nature Communications

IS - 1

ER -

Biomarkers of nanomaterials hazard from multi-layer data

Abstract

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