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image of Comparative Clinical, Proteomic, and Serologic Evaluation in Non-Hospitalized COVID-19 Patients and Healthy Individuals

Abstract

Introduction

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has had a significant global impact since its declaration as a public health emergency in January 2020. Symptoms of COVID-19 can range from mild to severe, including fever, cough, fatigue, and shortness of breath. This study aimed to investigate the clinical symptoms and proteomic differences between non-hospitalized COVID-19 patients and healthy individuals.

Method

Clinical data of 6231 COVID-19 patients of different age groups and sexes were collected and analyzed. Proteins were separated by SDS-PAGE and identified by MALDI-TOF. 900 serum samples were collected, with 100 samples per patient group and one healthy control group.

Result

In the control group of healthy individuals, five proteins (HAPTO, IGKC, FUT10, CO3, SESQ2) were expressed with a score of 1+, serving as a reference for the other groups. Group 9, consisting of individuals who had recovered (IgG positive), showed negative results for all five proteins due to anti-IgG antibody production in memory cells. The significant differences in protein expression compared to the control group indicated up-regulation and down-regulation of these proteins. Positive PCR or IgG and IgM results led to notable differences in protein expression across all studied groups.

Conclusion

The altered protein expression in infected individuals compared to healthy controls may suggest the potential for these proteins to serve as biomarkers for disease diagnosis and prognosis.

© 2024 Bentham Science Publishers
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2024-10-14
2025-03-07

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Comparative Clinical, Proteomic, and Serologic Evaluation in Non-Hospitalized COVID-19 Patients and Healthy Individuals
Bentham Science Publishers ; https://doi.org/10.2174/0115701646312544240924204420
/content/journals/cp/10.2174/0115701646312544240924204420
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  • Article Type:     Research Article
Keywords: SDS_PAGE ; MALDI-TOF ; Covid-19 ; proteomics ; biomarker discovery ; bioinformatic

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