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; Dario Greco

doi:10.1038/s41467-022-31609-5

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 ZubarevRiitta Lahesmaa, Kenneth Dawson, Katrin Loeschner, Erik Husfeldt Larsen, Fritz Krombach, Hannu Norppa, Juha Kere, Kai Savolainen, Harri Alenius, Bengt Fadeel, Dario Greco^*

^*Corresponding author for this work

Research output: Contribution to journal › Journal article › Research › peer-review

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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.

Original language	English
Article number	3798
Journal	Nature Communications
Volume	13
Issue number	1
Number of pages	10
ISSN	2041-1723
DOIs	https://doi.org/10.1038/s41467-022-31609-5
Publication status	Published - 2022

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Fortino, V., Kinaret, P. A. S., Fratello, M., Serra, A., Saarimäki, L. A., 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), Article 3798. https://doi.org/10.1038/s41467-022-31609-5

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

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.",

author = "Vittorio Fortino and Kinaret, {Pia Anneli Sofia} and Michele Fratello and Angela Serra and Saarim{\"a}ki, {Laura Aliisa} and Audrey Gallud and Govind Gupta and Gerard Vales and Manuel Correia and Omid Rasool and Jimmy Ytterberg and Marco Monopoli and Tiina Skoog and Peter Ritchie and Sergio Moya and Socorro V{\'a}zquez-Campos and Richard Handy and Roland Grafstr{\"o}m and Lang Tran and Roman Zubarev and Riitta Lahesmaa and Kenneth Dawson and Katrin Loeschner and Larsen, {Erik Husfeldt} and Fritz Krombach and Hannu Norppa and Juha Kere and Kai Savolainen and Harri Alenius and Bengt Fadeel and Dario Greco",

year = "2022",

doi = "10.1038/s41467-022-31609-5",

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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, 3798. https://doi.org/10.1038/s41467-022-31609-5

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

PY - 2022

Y1 - 2022

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.

U2 - 10.1038/s41467-022-31609-5

DO - 10.1038/s41467-022-31609-5

M3 - Journal article

C2 - 35778420

SN - 2041-1723

VL - 13

JO - Nature Communications

JF - Nature Communications

IS - 1

M1 - 3798

ER -

Biomarkers of nanomaterials hazard from multi-layer data

Abstract

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