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Proteomics Analysis of Serum from COVID-19 Patients

[Image: see text] Coronavirus disease 2019 (COVID-19) is a worldwide pandemic. To understand the changes in plasma proteomics upon SARS-CoV-2 infection, we analyzed the protein profiles of plasma samples from 10 COVID-19 patients and 10 healthy volunteers by using the DIA quantitative proteomics tec...

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Autores principales: Liu, Xiaoling, Cao, Yinghao, Fu, Hongmei, Wei, Jie, Chen, Jianhong, Hu, Jun, Liu, Bende
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2021
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7992154/
https://www.ncbi.nlm.nih.gov/pubmed/33778306
http://dx.doi.org/10.1021/acsomega.1c00616
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author Liu, Xiaoling
Cao, Yinghao
Fu, Hongmei
Wei, Jie
Chen, Jianhong
Hu, Jun
Liu, Bende
author_facet Liu, Xiaoling
Cao, Yinghao
Fu, Hongmei
Wei, Jie
Chen, Jianhong
Hu, Jun
Liu, Bende
author_sort Liu, Xiaoling
collection PubMed
description [Image: see text] Coronavirus disease 2019 (COVID-19) is a worldwide pandemic. To understand the changes in plasma proteomics upon SARS-CoV-2 infection, we analyzed the protein profiles of plasma samples from 10 COVID-19 patients and 10 healthy volunteers by using the DIA quantitative proteomics technology. We compared and identified differential proteins whose abundance changed upon SARS-CoV-2 infection. Bioinformatic analyses were then conducted for these identified differential proteins. The GO/KEEG database was used for functional annotation and enrichment analysis. The interaction relationship of differential proteins was evaluated with the STRING database, and Cytoscape software was used to conduct network analysis of the obtained data. A total of 323 proteins were detected in all samples. Difference between patients and healthy donors was found in 44 plasma proteins, among which 36 proteins were up-regulated and 8 proteins were down-regulated. GO functional annotation showed that these proteins mostly composed of cellular anatomical entities and proteins involved in biological regulation, cellular processes, transport, and other processes. KEEG functional annotation further showed that these proteins were mainly involved in complement system activation and infectious disease processes. Importantly, a KEEG pathway (natural killer cell-mediated cytotoxicity) was enriched, with three important activators of this pathway, ICAM1/2 and IgG, being up-regulated. Protein–protein interaction (PPI) statistics indicated that, among these 44 proteins, 6 were the most significantly up-regulated (DBH, SHGB, TF, ICAM2, THBS1, and C1RL) while 2 were the most significantly down-regulated (APCS and ORM1). Results from this study showed that a few proteins associated with immune activation were up-regulated in patient plasma. In addition, this study established a method for extraction and quantitative determination of plasma components in convalescent plasma from COVID-19 patients.
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spelling pubmed-79921542021-03-26 Proteomics Analysis of Serum from COVID-19 Patients Liu, Xiaoling Cao, Yinghao Fu, Hongmei Wei, Jie Chen, Jianhong Hu, Jun Liu, Bende ACS Omega [Image: see text] Coronavirus disease 2019 (COVID-19) is a worldwide pandemic. To understand the changes in plasma proteomics upon SARS-CoV-2 infection, we analyzed the protein profiles of plasma samples from 10 COVID-19 patients and 10 healthy volunteers by using the DIA quantitative proteomics technology. We compared and identified differential proteins whose abundance changed upon SARS-CoV-2 infection. Bioinformatic analyses were then conducted for these identified differential proteins. The GO/KEEG database was used for functional annotation and enrichment analysis. The interaction relationship of differential proteins was evaluated with the STRING database, and Cytoscape software was used to conduct network analysis of the obtained data. A total of 323 proteins were detected in all samples. Difference between patients and healthy donors was found in 44 plasma proteins, among which 36 proteins were up-regulated and 8 proteins were down-regulated. GO functional annotation showed that these proteins mostly composed of cellular anatomical entities and proteins involved in biological regulation, cellular processes, transport, and other processes. KEEG functional annotation further showed that these proteins were mainly involved in complement system activation and infectious disease processes. Importantly, a KEEG pathway (natural killer cell-mediated cytotoxicity) was enriched, with three important activators of this pathway, ICAM1/2 and IgG, being up-regulated. Protein–protein interaction (PPI) statistics indicated that, among these 44 proteins, 6 were the most significantly up-regulated (DBH, SHGB, TF, ICAM2, THBS1, and C1RL) while 2 were the most significantly down-regulated (APCS and ORM1). Results from this study showed that a few proteins associated with immune activation were up-regulated in patient plasma. In addition, this study established a method for extraction and quantitative determination of plasma components in convalescent plasma from COVID-19 patients. American Chemical Society 2021-03-09 /pmc/articles/PMC7992154/ /pubmed/33778306 http://dx.doi.org/10.1021/acsomega.1c00616 Text en © 2021 The Authors. Published by American Chemical Society Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Liu, Xiaoling
Cao, Yinghao
Fu, Hongmei
Wei, Jie
Chen, Jianhong
Hu, Jun
Liu, Bende
Proteomics Analysis of Serum from COVID-19 Patients
title Proteomics Analysis of Serum from COVID-19 Patients
title_full Proteomics Analysis of Serum from COVID-19 Patients
title_fullStr Proteomics Analysis of Serum from COVID-19 Patients
title_full_unstemmed Proteomics Analysis of Serum from COVID-19 Patients
title_short Proteomics Analysis of Serum from COVID-19 Patients
title_sort proteomics analysis of serum from covid-19 patients
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7992154/
https://www.ncbi.nlm.nih.gov/pubmed/33778306
http://dx.doi.org/10.1021/acsomega.1c00616
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