Lower Levels of ABO Anti-A and Anti-B of IgM, IgG and IgA Isotypes in the Serum but Not the Saliva of COVID-19 Convalescents.

Individuals with ABO type O, naturally possessing anti-A and anti-B antibodies in their serum, are underrepresented among patients infected with SARS-CoV-2 compared with healthy controls. The ABO antibodies might play a role in the viral transmission. Therefore, we aimed to quantify anti-A/anti-B, including their subclasses IgM, IgG and IgA, in the serum and saliva of Caucasians (n = 187) after mild COVID-19 to compare them with individuals who had never been infected with SARS-CoV-2. Two samples were collected within two months after the diagnosis (median days: 44) and two months later. ABO antibodies were determined by flow cytometry. Additionally, total IgA in saliva and antibodies specific to SARS-CoV-2 were tested by ELISA. COVID-19 convalescents had significantly lower levels of anti-A/anti-B IgM, IgG and IgA in their serum than control subjects (p < 0.001). Interestingly, no significant differences were observed in saliva. ABO antibody levels remained stable over the period considered. No relation of ABO to the level of SARS-CoV-2-specific antibodies was observed. Total IgA was lower in convalescents than in controls (p = 0.038). Whereas ABO antibodies in the saliva may not contribute to the pathogenesis of COVID-19, individual pre-existing high serum concentrations of anti-A/anti-B may have a protective effect against SARS-CoV-2 infection.

Development of a Rapid Live SARS-CoV-2 Neutralization Assay Based on a qPCR Readout.

Measuring SARS-CoV-2 neutralizing antibodies after vaccination or natural infection remains a priority in the ongoing COVID-19 pandemic to determine immunity, especially against newly emerging variants. The gold standard for assessing antibody-mediated immunity against SARS-CoV-2 are cell-based live virus neutralization assays. These assays usually take several days, thereby limiting test capacities and the availability of rapid results. In this study, therefore, we developed a faster live virus assay, which detects neutralizing antibodies through the early measurement of antibody-mediated intracellular virus reduction by SARS-CoV-2 qRT-PCR. In our assay, Vero E6 cells are infected with virus isolates preincubated with patient sera and controls. After 24 h, the intracellular viral load is determined by qRT-PCR using a standard curve to calculate percent neutralization. Utilizing COVID-19 convalescent-phase sera, we show that our novel assay generates results with high sensitivity and specificity as we detected antiviral activity for all tested convalescent-phase sera, but no antiviral activity in prepandemic sera. The assay showed a strong correlation with a conventional virus neutralization assay (rS = 0.8910), a receptor-binding domain ELISA (rS = 0.8485), and a surrogate neutralization assay (rS = 0.8373), proving that quantifying intracellular viral RNA can be used to measure seroneutralization. Our assay can be adapted easily to new variants, as demonstrated by our cross-neutralization experiments. This characteristic is key for rapidly determining immunity against newly emerging variants. Taken together, the novel assay presented here reduces turnaround time significantly while making use of a highly standardized and sensitive SARS-CoV-2 qRT-PCR method as a readout.

Open-Source, Adaptable, All-in-One Smartphone-Based System for Quantitative Analysis of Point-of-Care Diagnostics.

Point-of-care (POC) diagnostics, in particular lateral flow assays (LFA), represent a great opportunity for rapid, precise, low-cost and accessible diagnosis of disease. Especially with the ongoing coronavirus disease 2019 (COVID-19) pandemic, rapid point-of-care tests are becoming everyday tools for identification and prevention. Using smartphones as biosensors can enhance POC devices as portable, low-cost platforms for healthcare and medicine, food and environmental monitoring, improving diagnosis and documentation in remote, low-resource locations. We present an open-source, all-in-one smartphone-based system for quantitative analysis of LFAs. It consists of a 3D-printed photo box, a smartphone for image acquisition, and an R Shiny software package with modular, customizable analysis workflow for image editing, analysis, data extraction, calibration and quantification of the assays. This system is less expensive than commonly used hardware and software, so it could prove very beneficial for diagnostic testing in the context of pandemics, as well as in low-resource countries.

A Novel High-Throughput Nanopore-Sequencing-Based Strategy for Rapid and Automated S-Protein Typing of SARS-CoV-2 Variants.

Rapid molecular surveillance of SARS-CoV-2 S-protein variants leading to immune escape and/or increased infectivity is of utmost importance. Among global bottlenecks for variant monitoring in diagnostic settings are sequencing and bioinformatics capacities. In this study, we aimed to establish a rapid and user-friendly protocol for high-throughput S-gene sequencing and subsequent automated identification of variants. We designed two new primer pairs to amplify only the immunodominant part of the S-gene for nanopore sequencing. Furthermore, we developed an automated "S-Protein-Typer" tool that analyzes and reports S-protein mutations on the amino acid level including a variant of concern indicator. Validation of our primer panel using SARS-CoV-2-positive respiratory specimens covering a broad Ct range showed successful amplification for 29/30 samples. Restriction to the region of interest freed sequencing capacity by a factor of 12-13, compared with whole-genome sequencing. Using either the MinION or Flongle flow cell, our sequencing strategy reduced the time required to identify SARS-CoV-2 variants accordingly. The S-Protein-Typer tool identified all mutations correctly when challenged with our sequenced samples and 50 deposited sequences covering all VOCs (December 2021). Our proposed S-protein variant screening offers a simple, more rapid, and low-cost entry into NGS-based SARS-CoV-2 analysis, compared with current whole-genome approaches.