Autoantibodies targeting GPCRs and RAS-related molecules associate with COVID-19 severity
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Abstract
COVID-19 shares the feature of autoantibody production with systemic autoimmune diseases. In order to understand the role of these immune globulins in the pathogenesis of the disease, it is important to explore the autoantibody spectra. Here we show, by a cross-sectional study of 246 individuals, that autoantibodies targeting G protein-coupled receptors (GPCR) and RAS-related molecules associate with the clinical severity of COVID-19. Patients with moderate and severe disease are characterized by higher autoantibody levels than healthy controls and those with mild COVID-19 disease. Among the anti-GPCR autoantibodies, machine learning classification identifies the chemokine receptor CXCR3 and the RAS-related molecule AGTR1 as targets for antibodies with the strongest association to disease severity. Besides antibody levels, autoantibody network signatures are also changing in patients with intermediate or high disease severity. Although our current and previous studies identify anti-GPCR antibodies as natural components of human biology, their production is deregulated in COVID-19 and their level and pattern alterations might predict COVID-19 disease severity.
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SciScore for 10.1101/2021.08.24.21262385: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics Consent: All healthy controls and patients provided written consent to participate in the study, which was performed in accordance with the Declaration of Helsinki and approved by the IntegReview institutional review board.
IRB: All healthy controls and patients provided written consent to participate in the study, which was performed in accordance with the Declaration of Helsinki and approved by the IntegReview institutional review board.Sex as a biological variable not detected. Randomization Details about the survey study, patient demographics and symptoms have been previously described77,78. 77 randomly selected age- and sex-matched healthy controls (SARS-CoV-2 negative and without … SciScore for 10.1101/2021.08.24.21262385: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics Consent: All healthy controls and patients provided written consent to participate in the study, which was performed in accordance with the Declaration of Helsinki and approved by the IntegReview institutional review board.
IRB: All healthy controls and patients provided written consent to participate in the study, which was performed in accordance with the Declaration of Helsinki and approved by the IntegReview institutional review board.Sex as a biological variable not detected. Randomization Details about the survey study, patient demographics and symptoms have been previously described77,78. 77 randomly selected age- and sex-matched healthy controls (SARS-CoV-2 negative and without symptoms of COVID-19) were included in this study and their autoantibody data were compared to 169 individuals who were SARS-CoV-2 positive (determined by positive nasopharyngeal swab). Blinding not detected. Power Analysis Principal Component Analysis: Principal Component Analysis (PCA) with spectral decomposition38,39 was used to measure the stratification power of the 17 autoantibodies to distinguish between COVID-19 (mild, moderate and severe patients) and healthy controls. Table 2: Resources
Antibodies Sentences Resources Detection of IgG autoantibodies: Human IgG autoantibodies against 14 different GPCRs (AT1R, AT2R, MASR, Brady-R1, alpha1-adr-R, beta1-adr-R, beta2-adr-R, M3R, M4R, M5R, CXCR3, PAR1, C5a-R, N1R), 2 molecules serving as entry for SARS-CoV-2 (ACE-II, neuropilin), as well as antibodies against the transmembrane receptor STAB-1 were detected from frozen serum using commercial ELISA Kits (CellTrend, Germany) according to manufacturer’s instructions, as previously described79. AT1Rsuggested: NoneBrady-R1suggested: Nonealpha1-adr-Rsuggested: Nonebeta1-adr-Rsuggested: Nonebeta2-adr-Rsuggested: NoneCXCR3suggested: NonePAR1suggested: NoneSoftware and Algorithms Sentences Resources Gene ontology (GO) enrichment analysis of the 17 autoantibody targets was performed using GO Biological Process 2021 analysis through the Enrichr webtool81–83 GO Biologicalsuggested: NoneEnrichrsuggested: (Enrichr, RRID:SCR_001575)Circos Plot of antibody targets and pathway association was built using Circos online tool84. Circossuggested: (Circos, RRID:SCR_011798)Differences in autoantibody levels: Box plots showing the different expression levels of 17 anti-GPCR-autoantibodies from COVID-19 patients (mild, severe and oxygen-dependent groups) and healthy controls were generated using the R version 4.0.5 (The R Project for Statistical Computing. R Project for Statisticalsuggested: (R Project for Statistical Computing, RRID:SCR_001905)We trained a Random Forest model using the functionalities of the R package randomForest (version 4.6.14)85. randomForestsuggested: (RandomForest Package in R, RRID:SCR_015718)In addition, multilinear regression analysis of relationships between different variables (auto-antibodies) was performed using the R packages ggpubr, ggplot2 and ggExtra. ggplot2suggested: (ggplot2, RRID:SCR_014601)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:However, this also represents a limitation of our work that demand future investigations. Although we have previously assessed how these autoantibodies act in the context of systemic autoimmune diseases13,41,52,68–72, mechanistic investigations are missing to characterize how all these autoantibodies can simultaneously affect (i.e., stimulating or blocking) their targets in the context of COVID-19. For instance, future evaluation will be necessary to determine if they have synergistic effects in the presence of endogenous ligands as maybe the case with anti-CXCR3 autoantibodies and CXCL9/CXCL10/CXCL11. Since GPCRs comprise the largest superfamily of integral membrane proteins in humans14, it is also possibly that several additional anti-GPCR autoantibodies remain to be discovered. Likewise, several SARS-CoV-2 strains have been identified73 and it will be important to investigate whether they induce different autoantibody patterns that may contribute to disease outcomes. Of note, autoantibodies are present in healthy individuals and immunization with GPCR-overexpressing membranes can induce the production of autoantibodies targeting GPCRs41. Thus, another important issue to be addressed is whether the recently developed vaccines against COVID-1974 could induce the production of anti-GPCR autoantibodies. In conclusion, this study identifies new autoantibodies which are dysregulated by SARS-CoV-2. Our data also indicates that anti-GPCR antibodies represent potential new clinical...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
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- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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