Please use this identifier to cite or link to this item: http://dx.doi.org/10.25673/111386
Title: Detection of antibodies against endemic and SARS-CoV-2 coronaviruses with short peptide epitopes
Author(s): Szardenings, MichaelLook up in the Integrated Authority File of the German National Library
Delaroque, Nicolas
Kern, KarolinLook up in the Integrated Authority File of the German National Library
Ramirez-Caballero, Lisbeth
Puder, Marcus
Ehrentreich-Förster, Eva
Beige, Joachim
Zürner, Sebastian
Popp, Georg
Wolf, Johannes
Borte, Stephan M.Look up in the Integrated Authority File of the German National Library
Issue Date: 2023
Type: Article
Language: English
Abstract: (1) Background: Coronavirus proteins are quite conserved amongst endemic strains (eCoV) and SARS-CoV-2. We aimed to evaluate whether peptide epitopes might serve as useful diagnostic biomarkers to stratify previous infections and COVID-19. (2) Methods: Peptide epitopes were identified at an amino acid resolution that applied a novel statistical approach to generate data sets of potential antibody binding peptides. (3) Results: Data sets from more than 120 COVID-19 or eCoV-infected patients, as well as vaccinated persons, have been used to generate data sets that have been used to search in silico for potential epitopes in proteins of SARS-CoV-2 and eCoV. Peptide epitopes were validated with >300 serum samples in synthetic peptide micro arrays and epitopes specific for different viruses, in addition to the identified cross reactive epitopes. (4) Conclusions: Most patients develop antibodies against non-structural proteins, which are useful general markers for recent infections. However, there are differences in the epitope patterns of COVID-19, and eCoV, and the S-protein vaccine, which can only be explained by a high degree of cross-reactivity between the viruses, a pre-existing immune response against some epitopes, and even an alternate processing of the vaccine proteins.
URI: https://opendata.uni-halle.de//handle/1981185920/113340
http://dx.doi.org/10.25673/111386
Open Access: Open access publication
License: (CC BY 4.0) Creative Commons Attribution 4.0(CC BY 4.0) Creative Commons Attribution 4.0
Journal Title: Vaccines
Publisher: MDPI
Publisher Place: Basel
Volume: 11
Issue: 9
Original Publication: 10.3390/vaccines11091403
Page Start: 1
Page End: 18
Appears in Collections:Open Access Publikationen der MLU

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