Dynamic Cellular Proteome Remodeling during SARS-CoV-2 Infection. Identification of Plasma Protein Readouts

The outbreak of COVID-19, led to an ongoing pandemic with devastating consequences for the global economy and human health. With the global spread of SARS-CoV-2, multidisciplinary initiatives were launched to explore new diagnostic, therapeutic, and vaccination strategies. From this perspective, pro...

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Autores: Dos Santos, Fátima Milhano, Vindel-Alfageme, Jorge, Ciordia, Sergio, Castro, Victoria, Orera, Irene, Garaigorta, Urtzi, Gastaminza, Pablo, Corrales, Fernando J.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/375788
Acceso en línea:http://hdl.handle.net/10261/375788
https://api.elsevier.com/content/abstract/scopus_id/85210290299
Access Level:acceso abierto
Palabra clave:COVID-19
SARS-CoV-2
Machine learning
Proteomics
http://metadata.un.org/sdg/3
Ensure healthy lives and promote well-being for all at all ages
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spelling Dynamic Cellular Proteome Remodeling during SARS-CoV-2 Infection. Identification of Plasma Protein ReadoutsDos Santos, Fátima MilhanoVindel-Alfageme, JorgeCiordia, SergioCastro, VictoriaOrera, IreneGaraigorta, UrtziGastaminza, PabloCorrales, Fernando J.COVID-19SARS-CoV-2Machine learningProteomicshttp://metadata.un.org/sdg/3Ensure healthy lives and promote well-being for all at all agesThe outbreak of COVID-19, led to an ongoing pandemic with devastating consequences for the global economy and human health. With the global spread of SARS-CoV-2, multidisciplinary initiatives were launched to explore new diagnostic, therapeutic, and vaccination strategies. From this perspective, proteomics could help to understand the mechanisms associated with SARS-CoV-2 infection and to identify new therapeutic options. A TMT-based quantitative proteomics and phosphoproteomics analysis was performed to study the proteome remodeling of human lung alveolar cells expressing human ACE2 (A549-ACE2) after infection with SARS-CoV-2. Detectability and the prognostic value of selected proteins was analyzed by targeted PRM. A total of 6802 proteins and 6428 phospho-sites were identified in A549-ACE2 cells after infection with SARS-CoV-2. The differential proteins here identified revealed that A549-ACE2 cells undergo a time-dependent regulation of essential processes, delineating the precise intervention of the cellular machinery by the viral proteins. From this mechanistic background and by applying machine learning modeling, 29 differential proteins were selected and detected in the serum of COVID-19 patients, 14 of which showed promising prognostic capacity. Targeting these proteins and the protein kinases responsible for the reported phosphorylation changes may provide efficient alternative strategies for the clinical management of COVID-19.The CNB was supported by Grant CEX2023−001386 S funded by MICIU/AEI/10.13,039/501100011033. Comunidad de Madrid Grants B2017/BMD-3817 and 2022/BMD-7232. Intramural CSIC PIE/COVID-19 projects 202020 × 1079 and 202020E108. MICIN PID2021−127496NB-100. This research work was also funded by the European Commission NextGenerationEU (Regulation EU 2020/2094), through CSIC’s Global Health Platform (PTI Salud Global) and Conexión Cancer.Peer reviewedAmerican Chemical SocietyEuropean CommissionMinisterio de Ciencia, Innovación y Universidades (España)Comunidad de MadridDos Santos, Fátima Milhano [0000-0002-4041-1504]Castro, Victoria [0000-0001-9151-5138]Gastaminza, Pablo [0000-0002-7873-5491]Corrales, Fernando J. [0000-0002-0231-5159]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202520252025info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/375788https://api.elsevier.com/content/abstract/scopus_id/85210290299reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#B2017/BMD-38172022/BMD-7232Journal of proteome researchapplication/pdfhttps://doi.org/10.1021/acs.jproteome.4c00566Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3757882026-05-22T06:33:51Z
dc.title.none.fl_str_mv Dynamic Cellular Proteome Remodeling during SARS-CoV-2 Infection. Identification of Plasma Protein Readouts
title Dynamic Cellular Proteome Remodeling during SARS-CoV-2 Infection. Identification of Plasma Protein Readouts
spellingShingle Dynamic Cellular Proteome Remodeling during SARS-CoV-2 Infection. Identification of Plasma Protein Readouts
Dos Santos, Fátima Milhano
COVID-19
SARS-CoV-2
Machine learning
Proteomics
http://metadata.un.org/sdg/3
Ensure healthy lives and promote well-being for all at all ages
title_short Dynamic Cellular Proteome Remodeling during SARS-CoV-2 Infection. Identification of Plasma Protein Readouts
title_full Dynamic Cellular Proteome Remodeling during SARS-CoV-2 Infection. Identification of Plasma Protein Readouts
title_fullStr Dynamic Cellular Proteome Remodeling during SARS-CoV-2 Infection. Identification of Plasma Protein Readouts
title_full_unstemmed Dynamic Cellular Proteome Remodeling during SARS-CoV-2 Infection. Identification of Plasma Protein Readouts
title_sort Dynamic Cellular Proteome Remodeling during SARS-CoV-2 Infection. Identification of Plasma Protein Readouts
dc.creator.none.fl_str_mv Dos Santos, Fátima Milhano
Vindel-Alfageme, Jorge
Ciordia, Sergio
Castro, Victoria
Orera, Irene
Garaigorta, Urtzi
Gastaminza, Pablo
Corrales, Fernando J.
author Dos Santos, Fátima Milhano
author_facet Dos Santos, Fátima Milhano
Vindel-Alfageme, Jorge
Ciordia, Sergio
Castro, Victoria
Orera, Irene
Garaigorta, Urtzi
Gastaminza, Pablo
Corrales, Fernando J.
author_role author
author2 Vindel-Alfageme, Jorge
Ciordia, Sergio
Castro, Victoria
Orera, Irene
Garaigorta, Urtzi
Gastaminza, Pablo
Corrales, Fernando J.
author2_role author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv European Commission
Ministerio de Ciencia, Innovación y Universidades (España)
Comunidad de Madrid
Dos Santos, Fátima Milhano [0000-0002-4041-1504]
Castro, Victoria [0000-0001-9151-5138]
Gastaminza, Pablo [0000-0002-7873-5491]
Corrales, Fernando J. [0000-0002-0231-5159]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv COVID-19
SARS-CoV-2
Machine learning
Proteomics
http://metadata.un.org/sdg/3
Ensure healthy lives and promote well-being for all at all ages
topic COVID-19
SARS-CoV-2
Machine learning
Proteomics
http://metadata.un.org/sdg/3
Ensure healthy lives and promote well-being for all at all ages
description The outbreak of COVID-19, led to an ongoing pandemic with devastating consequences for the global economy and human health. With the global spread of SARS-CoV-2, multidisciplinary initiatives were launched to explore new diagnostic, therapeutic, and vaccination strategies. From this perspective, proteomics could help to understand the mechanisms associated with SARS-CoV-2 infection and to identify new therapeutic options. A TMT-based quantitative proteomics and phosphoproteomics analysis was performed to study the proteome remodeling of human lung alveolar cells expressing human ACE2 (A549-ACE2) after infection with SARS-CoV-2. Detectability and the prognostic value of selected proteins was analyzed by targeted PRM. A total of 6802 proteins and 6428 phospho-sites were identified in A549-ACE2 cells after infection with SARS-CoV-2. The differential proteins here identified revealed that A549-ACE2 cells undergo a time-dependent regulation of essential processes, delineating the precise intervention of the cellular machinery by the viral proteins. From this mechanistic background and by applying machine learning modeling, 29 differential proteins were selected and detected in the serum of COVID-19 patients, 14 of which showed promising prognostic capacity. Targeting these proteins and the protein kinases responsible for the reported phosphorylation changes may provide efficient alternative strategies for the clinical management of COVID-19.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/375788
https://api.elsevier.com/content/abstract/scopus_id/85210290299
url http://hdl.handle.net/10261/375788
https://api.elsevier.com/content/abstract/scopus_id/85210290299
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
B2017/BMD-3817
2022/BMD-7232
Journal of proteome research
application/pdf
https://doi.org/10.1021/acs.jproteome.4c00566
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv American Chemical Society
publisher.none.fl_str_mv American Chemical Society
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
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