Bioinformatical Design and Performance Evaluation of a Nucleocapsid- and an RBD-Based Particle Enhanced Turbidimetric Immunoassay (PETIA) to Quantify the Wild Type and Variants of Concern-Derived Immunoreactivity of SARS-CoV-2.
Leoni WeyMasetto ThomasAlexander SpaethJessica BrehmChristian KochemMarco ReinhartHolger MüllerUwe KempinFranziska LorenzChristoph PeterMatthias GrimmlerPublished in: Biomedicines (2023)
Since SARS-CoV-2 emerged in December 2019 in Wuhan, the resulting pandemic has paralyzed the economic and cultural life of the world. Variants of concern (VOC) strongly increase pressure on public health systems. Rapid, easy-to-use, and cost-effective assays are essential to manage the pandemic. Here we present a bioinformatical approach for the fast and efficient design of two innovative serological Particle Enhanced Turbidimetric Immunoassays (PETIA) to quantify the SARS-CoV-2 immunoresponse. To confirm bioinformatical assumptions, an S-RBD- and a Nucleocapsid-based PETIA were produced. Sensitivity and specificity were compared for 95 patient samples using a BioMajesty™ fully automated analyzer. The S-RBD-based PETIA showed necessary specificity (98%) over the N protein-based PETIA (21%). Further, the reactivity and cross-reactivity of the RBD-based PETIA towards variant-derived antibodies of SARS-CoV-2 were assessed by a quenching inhibition test. The inhibition kinetics of the S-RBD variants Alpha , Beta , Delta , Gamma , Kappa , and Omicron were evaluated. In summary, we showed that specific and robust PETIA immunoassays can be rapidly designed and developed. The quantification of the SARS-CoV-2-related immunoresponse of variants ( Alpha to Kappa ) is possible using specific RBD assays. In contrast, Omicron revealed lower cross-reactivity (approx. 50%). To ensure the quantification of the Omicron variant, modified immunoassays appear to be necessary.
Keyphrases
- sars cov
- respiratory syndrome coronavirus
- copy number
- high throughput
- nuclear factor
- wild type
- magnetic resonance
- mental health
- machine learning
- deep learning
- gene expression
- small molecule
- case report
- toll like receptor
- dna methylation
- computed tomography
- inflammatory response
- genome wide
- single cell
- loop mediated isothermal amplification
- structural basis