Article ; Online: Uncovering a neurological protein signature for severe COVID-19.
2023 Volume 182, Page(s) 106147
Abstract: Coronavirus disease of 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has sparked a global pandemic with severe complications and high morbidity rate. Neurological symptoms in COVID-19 patients, and neurological ... ...
Abstract | Coronavirus disease of 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has sparked a global pandemic with severe complications and high morbidity rate. Neurological symptoms in COVID-19 patients, and neurological sequelae post COVID-19 recovery have been extensively reported. Yet, neurological molecular signature and signaling pathways that are affected in the central nervous system (CNS) of COVID-19 severe patients remain still unknown and need to be identified. Plasma samples from 49 severe COVID-19 patients, 50 mild COVID-19 patients, and 40 healthy controls were subjected to Olink proteomics analysis of 184 CNS-enriched proteins. By using a multi-approach bioinformatics analysis, we identified a 34-neurological protein signature for COVID-19 severity and unveiled dysregulated neurological pathways in severe cases. Here, we identified a new neurological protein signature for severe COVID-19 that was validated in different independent cohorts using blood and postmortem brain samples and shown to correlate with neurological diseases and pharmacological drugs. This protein signature could potentially aid the development of prognostic and diagnostic tools for neurological complications in post-COVID-19 convalescent patients with long term neurological sequelae. |
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MeSH term(s) | Humans ; COVID-19/complications ; SARS-CoV-2 ; Nervous System Diseases/etiology ; Central Nervous System ; Brain |
Language | English |
Publishing date | 2023-05-11 |
Publishing country | United States |
Document type | Journal Article ; Research Support, Non-U.S. Gov't |
ZDB-ID | 1211786-9 |
ISSN | 1095-953X ; 0969-9961 |
ISSN (online) | 1095-953X |
ISSN | 0969-9961 |
DOI | 10.1016/j.nbd.2023.106147 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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