Shotgun mass spectrometry-based lipid profiling identifies and distinguishes between chronic inflammatory diseases
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , , , , , , , , , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10362/125096 |
Resumo: | Funding Information: This work was supported by PTDC/MED-PAT/29395/2017 financially supported by Fundação para a Ciência e a Tecnologia (FCT), through national funds and co-funded by FEDER under the PT2020 Partnership. ND was a holder of PhD fellowship from the FCT (Ref. No.: SFRH/BD/51877/2012), attributed by the Inter-University Doctoral Programme in Ageing and Chronic Disease (PhDOC). LA was a holder of a FCT PhD fellowship (PD/BD/114254/2016), attributed by the ProRegem Doctoral Programme in 2016. Funding Information: This work was supported by PTDC/MED-PAT/29395/2017 financially supported by Funda??o para a Ci?ncia e a Tecnologia (FCT), through national funds and co-funded by FEDER under the PT2020 Partnership. ND was a holder of PhD fellowship from the FCT (Ref. No.: SFRH/BD/51877/2012), attributed by the Inter-University Doctoral Programme in Ageing and Chronic Disease (PhDOC). LA was a holder of a FCT PhD fellowship (PD/BD/114254/2016), attributed by the ProRegem Doctoral Programme in 2016. Anonymized data described in the manuscript are available in supplementary materials. Appendix. Supplementary materials, All data and R analysis is provided in the link: https://github.com/ruma1974/lipidProfillingRM/tree/master. It includes an R package with additional plotting functionality. Publisher Copyright: © 2021 The Author(s) |
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Shotgun mass spectrometry-based lipid profiling identifies and distinguishes between chronic inflammatory diseasesLipid profiling of chronic diseasesDyslipidemiaLipid biomarkerLipid profilingSystemic lupus erythematosusVascular diseasesBiochemistry, Genetics and Molecular Biology(all)Funding Information: This work was supported by PTDC/MED-PAT/29395/2017 financially supported by Fundação para a Ciência e a Tecnologia (FCT), through national funds and co-funded by FEDER under the PT2020 Partnership. ND was a holder of PhD fellowship from the FCT (Ref. No.: SFRH/BD/51877/2012), attributed by the Inter-University Doctoral Programme in Ageing and Chronic Disease (PhDOC). LA was a holder of a FCT PhD fellowship (PD/BD/114254/2016), attributed by the ProRegem Doctoral Programme in 2016. Funding Information: This work was supported by PTDC/MED-PAT/29395/2017 financially supported by Funda??o para a Ci?ncia e a Tecnologia (FCT), through national funds and co-funded by FEDER under the PT2020 Partnership. ND was a holder of PhD fellowship from the FCT (Ref. No.: SFRH/BD/51877/2012), attributed by the Inter-University Doctoral Programme in Ageing and Chronic Disease (PhDOC). LA was a holder of a FCT PhD fellowship (PD/BD/114254/2016), attributed by the ProRegem Doctoral Programme in 2016. Anonymized data described in the manuscript are available in supplementary materials. Appendix. Supplementary materials, All data and R analysis is provided in the link: https://github.com/ruma1974/lipidProfillingRM/tree/master. It includes an R package with additional plotting functionality. Publisher Copyright: © 2021 The Author(s)Background: Localized stress and cell death in chronic inflammatory diseases may release tissue-specific lipids into the circulation causing the blood plasma lipidome to reflect the type of inflammation. However, deep lipid profiles of major chronic inflammatory diseases have not been compared. Methods: Plasma lipidomes of patients suffering from two etiologically distinct chronic inflammatory diseases, atherosclerosis-related vascular disease, including cardiovascular (CVD) and ischemic stroke (IS), and systemic lupus erythematosus (SLE), were screened by a top-down shotgun mass spectrometry-based analysis without liquid chromatographic separation and compared to each other and to age-matched controls. Lipid profiling of 596 lipids was performed on a cohort of 427 individuals. Machine learning classifiers based on the plasma lipidomes were used to distinguish the two chronic inflammatory diseases from each other and from the controls. Findings: Analysis of the lipidomes enabled separation of the studied chronic inflammatory diseases from controls based on independent validation test set classification performance (CVD vs control - Sensitivity: 0.94, Specificity: 0.88; IS vs control - Sensitivity: 1.0, Specificity: 1.0; SLE vs control – Sensitivity: 1, Specificity: 0.93) and from each other (SLE vs CVD ‒ Sensitivity: 0.91, Specificity: 1; IS vs SLE - Sensitivity: 1, Specificity: 0.82). Preliminary linear discriminant analysis plots using all data clearly separated the clinical groups from each other and from the controls, and partially separated CVD severities, as classified into five clinical groups. Dysregulated lipids are partially but not fully counterbalanced by statin treatment. Interpretation: Dysregulation of the plasma lipidome is characteristic of chronic inflammatory diseases. Lipid profiling accurately identifies the diseases and in the case of CVD also identifies sub-classes. Funding: Full list of funding sources at the end of the manuscript.iNOVA4Health - pólo NMSNOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM)Centro de Estudos de Doenças Crónicas (CEDOC)RUNMatthiesen, RuneLauber, ChrisSampaio, Julio L.Domingues, NeuzaAlves, LilianaGerl, Mathias J.Almeida, Manuel S.Rodrigues, GustavoAraújo Gonçalves, PedroFerreira, JorgeBorbinha, CláudiaPedro Marto, JoãoNeves, MarisaBatista, FredericoViana-Baptista, MiguelAlves, JoseSimons, KaiVaz, Winchil L.C.Vieira, Otilia V.2021-09-24T02:25:05Z2021-082021-08-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10362/125096eng2352-3964PURE: 33723646https://doi.org/10.1016/j.ebiom.2021.103504info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:06:18Zoai:run.unl.pt:10362/125096Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:45:39.811842Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Shotgun mass spectrometry-based lipid profiling identifies and distinguishes between chronic inflammatory diseases Lipid profiling of chronic diseases |
title |
Shotgun mass spectrometry-based lipid profiling identifies and distinguishes between chronic inflammatory diseases |
spellingShingle |
Shotgun mass spectrometry-based lipid profiling identifies and distinguishes between chronic inflammatory diseases Matthiesen, Rune Dyslipidemia Lipid biomarker Lipid profiling Systemic lupus erythematosus Vascular diseases Biochemistry, Genetics and Molecular Biology(all) |
title_short |
Shotgun mass spectrometry-based lipid profiling identifies and distinguishes between chronic inflammatory diseases |
title_full |
Shotgun mass spectrometry-based lipid profiling identifies and distinguishes between chronic inflammatory diseases |
title_fullStr |
Shotgun mass spectrometry-based lipid profiling identifies and distinguishes between chronic inflammatory diseases |
title_full_unstemmed |
Shotgun mass spectrometry-based lipid profiling identifies and distinguishes between chronic inflammatory diseases |
title_sort |
Shotgun mass spectrometry-based lipid profiling identifies and distinguishes between chronic inflammatory diseases |
author |
Matthiesen, Rune |
author_facet |
Matthiesen, Rune Lauber, Chris Sampaio, Julio L. Domingues, Neuza Alves, Liliana Gerl, Mathias J. Almeida, Manuel S. Rodrigues, Gustavo Araújo Gonçalves, Pedro Ferreira, Jorge Borbinha, Cláudia Pedro Marto, João Neves, Marisa Batista, Frederico Viana-Baptista, Miguel Alves, Jose Simons, Kai Vaz, Winchil L.C. Vieira, Otilia V. |
author_role |
author |
author2 |
Lauber, Chris Sampaio, Julio L. Domingues, Neuza Alves, Liliana Gerl, Mathias J. Almeida, Manuel S. Rodrigues, Gustavo Araújo Gonçalves, Pedro Ferreira, Jorge Borbinha, Cláudia Pedro Marto, João Neves, Marisa Batista, Frederico Viana-Baptista, Miguel Alves, Jose Simons, Kai Vaz, Winchil L.C. Vieira, Otilia V. |
author2_role |
author author author author author author author author author author author author author author author author author author |
dc.contributor.none.fl_str_mv |
iNOVA4Health - pólo NMS NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM) Centro de Estudos de Doenças Crónicas (CEDOC) RUN |
dc.contributor.author.fl_str_mv |
Matthiesen, Rune Lauber, Chris Sampaio, Julio L. Domingues, Neuza Alves, Liliana Gerl, Mathias J. Almeida, Manuel S. Rodrigues, Gustavo Araújo Gonçalves, Pedro Ferreira, Jorge Borbinha, Cláudia Pedro Marto, João Neves, Marisa Batista, Frederico Viana-Baptista, Miguel Alves, Jose Simons, Kai Vaz, Winchil L.C. Vieira, Otilia V. |
dc.subject.por.fl_str_mv |
Dyslipidemia Lipid biomarker Lipid profiling Systemic lupus erythematosus Vascular diseases Biochemistry, Genetics and Molecular Biology(all) |
topic |
Dyslipidemia Lipid biomarker Lipid profiling Systemic lupus erythematosus Vascular diseases Biochemistry, Genetics and Molecular Biology(all) |
description |
Funding Information: This work was supported by PTDC/MED-PAT/29395/2017 financially supported by Fundação para a Ciência e a Tecnologia (FCT), through national funds and co-funded by FEDER under the PT2020 Partnership. ND was a holder of PhD fellowship from the FCT (Ref. No.: SFRH/BD/51877/2012), attributed by the Inter-University Doctoral Programme in Ageing and Chronic Disease (PhDOC). LA was a holder of a FCT PhD fellowship (PD/BD/114254/2016), attributed by the ProRegem Doctoral Programme in 2016. Funding Information: This work was supported by PTDC/MED-PAT/29395/2017 financially supported by Funda??o para a Ci?ncia e a Tecnologia (FCT), through national funds and co-funded by FEDER under the PT2020 Partnership. ND was a holder of PhD fellowship from the FCT (Ref. No.: SFRH/BD/51877/2012), attributed by the Inter-University Doctoral Programme in Ageing and Chronic Disease (PhDOC). LA was a holder of a FCT PhD fellowship (PD/BD/114254/2016), attributed by the ProRegem Doctoral Programme in 2016. Anonymized data described in the manuscript are available in supplementary materials. Appendix. Supplementary materials, All data and R analysis is provided in the link: https://github.com/ruma1974/lipidProfillingRM/tree/master. It includes an R package with additional plotting functionality. Publisher Copyright: © 2021 The Author(s) |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-09-24T02:25:05Z 2021-08 2021-08-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10362/125096 |
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http://hdl.handle.net/10362/125096 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2352-3964 PURE: 33723646 https://doi.org/10.1016/j.ebiom.2021.103504 |
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info:eu-repo/semantics/openAccess |
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openAccess |
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