Biomarkers of nanomaterials hazard from multi-layer data
dc.contributor.author | Fortino, V | |
dc.contributor.author | Kinaret, PAS | |
dc.contributor.author | Fratello, M | |
dc.contributor.author | Serra, A | |
dc.contributor.author | Saarimäki, LA | |
dc.contributor.author | Gallud, A | |
dc.contributor.author | Gupta, G | |
dc.contributor.author | Vales, G | |
dc.contributor.author | Correia, M | |
dc.contributor.author | Rasool, O | |
dc.contributor.author | Ytterberg, J | |
dc.contributor.author | Monopoli, M | |
dc.contributor.author | Skoog, T | |
dc.contributor.author | Ritchie, P | |
dc.contributor.author | Moya, S | |
dc.contributor.author | Vázquez-Campos, S | |
dc.contributor.author | Handy, Richard | |
dc.contributor.author | Grafström, R | |
dc.contributor.author | Tran, L | |
dc.contributor.author | Zubarev, R | |
dc.contributor.author | Lahesmaa, R | |
dc.contributor.author | Dawson, K | |
dc.contributor.author | Loeschner, K | |
dc.contributor.author | Larsen, EH | |
dc.contributor.author | Krombach, F | |
dc.contributor.author | Norppa, H | |
dc.contributor.author | Kere, J | |
dc.contributor.author | Savolainen, K | |
dc.contributor.author | Alenius, H | |
dc.contributor.author | Fadeel, B | |
dc.contributor.author | Greco, D | |
dc.date.accessioned | 2022-08-04T16:26:00Z | |
dc.date.issued | 2022-07-01 | |
dc.identifier.issn | 2041-1723 | |
dc.identifier.issn | 2041-1723 | |
dc.identifier.other | 3798 | |
dc.identifier.uri | http://hdl.handle.net/10026.1/19498 | |
dc.description.abstract |
<jats:title>Abstract</jats:title><jats:p>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.</jats:p> | |
dc.format.extent | 3798- | |
dc.format.medium | Electronic | |
dc.language | en | |
dc.language.iso | eng | |
dc.publisher | Nature Research | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.subject | Biomarkers | |
dc.subject | Nanostructures | |
dc.subject | RNA, Messenger | |
dc.title | Biomarkers of nanomaterials hazard from multi-layer data | |
dc.type | journal-article | |
dc.type | Journal Article | |
dc.type | Research Support, Non-U.S. Gov't | |
plymouth.author-url | https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000819790100014&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008 | |
plymouth.issue | 1 | |
plymouth.volume | 13 | |
plymouth.publication-status | Published online | |
plymouth.journal | Nature Communications | |
dc.identifier.doi | 10.1038/s41467-022-31609-5 | |
plymouth.organisational-group | /Plymouth | |
plymouth.organisational-group | /Plymouth/Faculty of Science and Engineering | |
plymouth.organisational-group | /Plymouth/Faculty of Science and Engineering/School of Biological and Marine Sciences | |
plymouth.organisational-group | /Plymouth/REF 2021 Researchers by UoA | |
plymouth.organisational-group | /Plymouth/REF 2021 Researchers by UoA/UoA06 Agriculture, Veterinary and Food Science | |
plymouth.organisational-group | /Plymouth/Research Groups | |
plymouth.organisational-group | /Plymouth/Research Groups/Marine Institute | |
plymouth.organisational-group | /Plymouth/Users by role | |
plymouth.organisational-group | /Plymouth/Users by role/Academics | |
dc.publisher.place | England | |
dcterms.dateAccepted | 2022-06-17 | |
dc.rights.embargodate | 2022-8-5 | |
dc.identifier.eissn | 2041-1723 | |
dc.rights.embargoperiod | Not known | |
rioxxterms.versionofrecord | 10.1038/s41467-022-31609-5 | |
rioxxterms.licenseref.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
rioxxterms.licenseref.startdate | 2022-07-01 | |
rioxxterms.type | Journal Article/Review | |
plymouth.funder | NANOSOLUTIONS Biological Foundation for the Safety Classification of Engineered Nanomaterials (ENM): Systems Biology Approaches to Understand::European Commission FP7 |