key: cord-0302489-29opurlw
authors: Marques, O. C.; Halpert, G.; Schimke, L. F.; Ostrinski, Y.; Zyskind, I.; Lattin, M. T.; Tran, F.; Schreiber, S.; Marques, A. H. C.; Filgueiras, I. S.; Placa, D. R.; Baiocchi, G. C.; Freire, P. P.; Fonseca, D. L. M.; Humrich, J. Y.; Lange, T.; Muller, A.; Giil, L. M.; Grasshoff, H.; Schumann, A.; Hackel, A. M.; Junker, J.; Meyer, C.; Ochs, H. D.; Lavi, Y. B.; Schulze-Forster, K.; Silvergerg, J. I.; Amital, H.; Zimmerman, J.; Heidecke, H.; Rosenberg, A. Z.; Riemekasten, G.; Shoenfeld, Y.
title: The relationship between autoantibodies targeting GPCRs and the renin-angiotensin system associates with COVID-19 severity
date: 2021-08-26
journal: nan
DOI: 10.1101/2021.08.24.21262385
sha: ce968843d9f77fccbe7cbe7a85decabbe33e4457
doc_id: 302489
cord_uid: 29opurlw

The coronavirus disease 2019 (COVID-19) can evolve to clinical manifestations resembling systemic autoimmune diseases, with the presence of autoantibodies that are still poorly characterized. To address this issue, we performed a cross-sectional study of 246 individuals to determine whether autoantibodies targeting G protein-coupled receptors (GPCRs) and renin-angiotensin system (RAS)-related molecules were associated with COVID-19-related clinical outcomes. Moderate and severe patients exhibited the highest autoantibody levels, relative to both healthy controls and patients with mild COVID-19 symptoms. Random Forest, a machine learning model, ranked anti-GPCR autoantibodies targeting downstream molecules in the RAS signaling pathway such as the angiotensin II type 1 and Mas receptor, and the chemokine receptor CXCR3 as the three strongest predictors of severe disease. Moreover, while the autoantibody network signatures were relatively conserved in patients with mild COVID-19 compared to healthy controls, they were disrupted in moderate and most perturbed in severe patients. Our data indicate that the relationship between autoantibodies targeting GPCRs and RAS-related molecules associates with the clinical severity of COVID-19, suggesting novel molecular pathways for therapeutic interventions.

Autoantibodies have been identified in patients with coronavirus disease 2019 (COVID- 19) , suggesting that the infection by severe acute respiratory syndrome virus 2 (SARS- can evolve to a systemic autoimmune disease [1] [2] [3] [4] [5] . For instance, high levels of antiphospholipid autoantibodies have been linked to severe respiratory disease by inducing neutrophil extracellular traps (NETs) and venous thrombosis 4, [6] [7] [8] [9] . High titers of neutralizing immunoglobulin G (IgG) autoantibodies against type I interferons (IFNs) have been reported in patients with life-threatening COVID-19 10 . Most recently, a wide range of autoantibodies in patients with COVID-19 have been characterized using rapid extracellular antigen profiling (REAP) 11 , a technology for comprehensive and high-throughput identification of autoantibodies recognizing 2,770 extracellular and secreted protein components of the exoproteome (extracellular protein epitopes) 12 These results are in line with our previous report 13 on autoantibodies targeting the largest superfamily of integral membrane proteins in humans 14 , i.e., the G protein-coupled receptors (GPCRs) suggesting that these autoantibodies are natural components of human biology that become dysregulated in autoimmune diseases. Likewise, recent studies have detected functional antibodies against GPCRs in the sera of patients with COVID-19 and indicate that they may be associated with disease severity [15] [16] [17] . However, these investigations were not systemic, focusing only on two types of anti-GPCR autoantibodies and did not investigate their relationship with the . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted August 26, 2021. ; https://doi.org/10.1101/2021.08.24.21262385 doi: medRxiv preprint potential presence of autoantibodies targeting molecules of the immune and the reninangiotensin systems (RAS), which play a central role in the development of severe COVID-19.

Here, we investigated the serum levels of autoantibodies targeting molecules belonging to the RAS (including GPCRs: MASR, AT1R, and AT2R as well as the entry receptor for the SARS-CoV-2, Angiotensin-converting enzyme II [ACE-II]) [18] [19] [20] [21] . Furthermore, we assessed the concentrations of autoantibodies against GPCRs involved in chemotaxis and inflammation (CXCR3 22,23 and C5aR 24 ), coagulation (PAR1 25 ), and neuronal receptors (ADRA1A, ADRB1, and ADRB2, ACHRMs) 26-30 , which have been implicated in the development of COVID-19 disease (see Supp. Table 1 for abbreviations of autoantibodies and their targets). In addition, we investigated autoantibodies targeting receptors facilitating the infectivity of SARS-CoV-2, and its entrance into host cells (neuropilin-Ab) 31 . Finally, we explored the potential presence of autoantibodies against STAB1 (STAB-1-Ab) as a potential new candidate involved in COVID- 19 infectivity, which is a scavenger receptor still not investigated for any role in COVID-19.

However, its multifunctionality during leukocyte trafficking, tissue homeostasis, and resolution of inflammation suggests it could be relevant for disease severity 32,33 . Figure 1A and Figure 1B represent how these autoantibody targets are interconnected by protein-protein interaction (PPI) or gene ontology (GO) relationships, respectively.

We found significantly higher levels of autoantibodies directed against eleven receptors ( Figure 1C) , which are involved in the modulation of inflammation and the RAS (Figure 1D) . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted August 26, 2021. ; https://doi.org/10.1101/2021.08.24.21262385 doi: medRxiv preprint suggesting an association between autoantibodies against GPCRs and RAS-related molecules with COVID-19 severity. In contrast, both controls and all COVID-19 disease groups were found to have a number of other autoantibodies at similar levels, most of them targeting neuronal receptors (ADRA1R, ADRB1, ADRB2, CHRM3, and CHRM4) 34-36 , but also against the receptor for complement C5a (C5aR), a potent anaphylatoxin chemotactic receptor 37 , suggesting that severe COVID-19 is specifically associated with autoantibodies towards certain groups of GPCRs (Supp. Fig 1) .

Next, we carried out principal component analysis (PCA) using a spectral decomposition approach 38,39 , to examine the correlations between variables (autoantibodies) and observations (individuals) while stratifying groups based on the autoantibody levels. This approach indicated that autoantibodies stratify COVID-19 patients according to disease severity (mild, moderate, and severe patients) (Figure 2A and 2C ). While healthy controls and patients with mild COVID-19 present a closer autoantibody pattern, moderate and severe COVID-19 patients clustered together. In this context, autoantibodies such as ACE-II-Ab, AT2R-Ab, Brady-R1, CXCR3-Ab, MASR-Ab, M5R, neuropilin-Ab, PAR1-Ab, STAB-1Ab appeared to play a major role in stratifying COVID-19 by disease burden (Figure 2B-2D) . Altogether, these results indicate that the association between autoantibodies against GPCRs and COVID-19-related molecules can be used as biomarkers for COVID-19 burden.

. CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

The copyright holder for this preprint this version posted August 26, 2021. ; https://doi.org/10.1101/2021.08.24.21262385 doi: medRxiv preprint

To further explore the potential of autoantibodies to predict COVID-19 outcomes, we performed Random Forest modelling, which is a machine learning approach that establishes outcomes based on predictions of decision trees 40 . The receiver operating characteristics (ROC) curve indicated a high false-positive rate for the classification of severe patients with the stable curve showing the highest error rate (out-of-bag or OOB) for this group (Figure 3A and 3B) .

I.e., in accordance with the PCA analysis, Random Forest classification of COVID-19 groups showed a higher error rate (low accuracy) when distinguishing moderate patients from those with severe COVID-19.

Thus, we assigned moderate and severe COVID-19 patients to the same group to identify the most relevant autoantibody predictors of COVID-19 burden. Using this approach, the merged moderate/severe patient group showed the lowest error rate compared to healthy controls and mild COVID-19 patients. This model resulted in an OOB error rate of 22,95% for all groups and an area under the ROC curve of 0.93, 0.87, and 0.96 for healthy controls, COVID-19 mild, and COVID-19 moderate/severe groups, respectively ( Figure 3C and 3D) . Moreover, the Random Forest model ranked these 17 autoantibodies based on their ability to discriminate between healthy controls and COVID-19 disease severity groups. Follow-up analysis indicated CXCR3-Ab, AT1R-Ab, MASR-Ab, M5R-Ab, and Brady-R1-Ab as the five most significant predictors of COVID-19 classification based on the number of nodes and gini-decrease criteria for measuring variable importance (Figure 3E and 3F) . However, other autoantibodies such as PAR1-Ab and STAB-1-Ab were also strong predictors of COVID-19 severity. The interaction between anti-CXCR-3 and anti-AT1R autoantibodies was the most frequent interaction occurring in the decision trees obtained by the Random Forest model (Supp. Figure 2A and 2B) . Altogether, . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

The copyright holder for this preprint this version posted August 26, 2021. ; https://doi.org/10.1101/2021.08.24.21262385 doi: medRxiv preprint these results show that autoantibodies targeting GPCRs and COVID-19 associated molecules perform well as predictors of COVID-19 disease severity and raises the question of whether these autoantibodies against key functional molecules play a significant role in COVID-19 pathophysiology.

We have recently reported that hierarchical clustering signatures of anti-GPCR autoantibody correlations are associated with physiological and pathological immune homeostasis 41 . Based on this concept, we investigated the correlation signatures in healthy controls and patients with COVID-19 to explore if changes in autoantibody relationships correlate with disease burden. Bivariate correlation analysis revealed a progressive loss of normal correlation signatures from mild to oxygen-dependent COVID-19 patients. In other words, patients with mild COVID-19 exhibited only minimal differences in the autoantibody correlation signatures when compared to healthy controls ( Figure 4A ). Patients with moderate COVID-19 started to clearly exhibit new relationships among autoantibodies while the severe group displayed the most different topological correlation pattern. Topologically, a positive correlation predominated among the autoantibodies. Of note, autoantibodies targeting nine different molecules presented significant changes in the total correlation distribution, which was determined by the distribution of a pairwise correlation between autoantibodies ( Figure 4B ). In summary, while the autoantibody network signatures were relatively conserved in patients with mild COVID-19 compared to healthy controls, these were disrupted in moderate and most perturbed in severe patients (Supp. Figure 3) . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

The copyright holder for this preprint this version posted August 26, 2021. ; https://doi.org/10.1101/2021.08.24.21262385 doi: medRxiv preprint

To better understand these changes in autoantibody correlation signatures, we performed canonical-correlation analysis (CCA), which is a multivariate statistical method to determine the linear relationship between two groups of variables 42 . CCA was carried out splitting autoantibodies into two groups: those against molecules belonging or influencing the RAS (Dataset X) compared with those autoantibodies targeting other GPCRs, neuropilin, and STAB1 (Dataset Y). This approach confirmed the changes in autoantibody relationship patterns revealed by Bivariate correlation analysis. In addition, the CCA indicated changes based on COVID-19 severity are in agreement with the Random Forest model. For instance, in this multivariate correlation approach autoantibodies targeting CXCR3 showed Spearman's rank correlation coefficient >0.6 only in the moderate and severe groups ( Figure 4C ). In this context, while Brady-Ab only appeared in the severe group, AT1R-Ab, MASR-Ab, and M5R-Ab exhibited changes in their correlation patterns that were only observed in the severe group.

The precise mechanisms by which the SARS-CoV-2 infection triggers the production of autoantibodies remains unknown. However, a potential antigenic cross-reactivity (molecular mimicry) between SARS-CoV-2 and human tissues has been hypothesized 43-48 . Furthermore, the hyperinflammatory reaction triggered by this virus results in tissue damage causing systemic immune-related manifestations that have been reported in patients with COVID-19 49 . When compared with patients manifesting mild disease, those with moderate COVID-19 symptoms, present with strong antibody production and high titers of neutralizing antibody 50 , but, as shown here, also with an increased production of autoantibodies. Thus, our work reinforces the concept that SARS-CoV-2 infection may trigger a life-threatening autoimmune disease, suggesting that . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

The copyright holder for this preprint this version posted August 26, 2021. ; https://doi.org/10.1101/2021.08.24.21262385 doi: medRxiv preprint this occurs against multiple functional molecules with key functions in immune and vascular homeostasis [1] [2] [3] 51 . While we found no differences in the levels of autoantibodies against neuronal receptors such as ADRA1A, ADRB1, ADRB2, CHRM3, and CHRM4, those targeting other GPCRs and RAS-related molecules were significantly dysregulated when comparing controls with moderate and severely affected COVID-19 patients. In this context, while we have previously reported that anti-AT1R has agonist properties 13, 41, 52, 53 , the mechanistic action of the other autoantibodies we identified remains to be investigated. For instance, we hypothesize that antibodies against CXCR3 might block the migration of immune cells that express CXCR3, such as natural killer cells, as well as CD4+ and CD8+ T cells that are critical for the killing of viruses in the lung 54-57 . However, if antibodies against CXCR3 have agonistic properties, potentializing CXCL9/CXCL10/CXCL11 signaling, they could exacerbate deleterious hyperinflammation.

Anyhow, the results of our work underscores recent studies 3,6,10,12 that report the generation of autoantibodies following SARS-CoV-2 infection. Importantly, our data indicate that an additional immunological layer is present where autoantibodies targeting GPCRs and RAS- The Random Forest model revealed the interaction between autoantibodies targeting CXCR3 and AT1R as the most important predictors of COVID-19 severity. There is an essential biological connection between CXCR3 and AT1R. Blocking AT1R impairs the release of several chemokines, including CXCL10, the ligand for CXCR3 59 , a chemokine receptor highly . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

The copyright holder for this preprint this version posted August 26, 2021. ; https://doi.org/10.1101/2021.08.24.21262385 doi: medRxiv preprint expressed by effector T cells controlling the traffic and function of CD4+ and CD8+ T cells during inflammation 60-63 . Further, CXCR3 has been strongly associated with both autoimmune and inflammatory diseases 64 . Meanwhile, increased levels of Ang II together with the hyperactivation of its receptor (AT1R) have been associated with unfavorable COVID-19 disease 16, 65 . This pathological mechanism has been explored as a therapeutic approach for COVID-19 by clinical trials with Losartan, an AT1R antagonist 8, 66 . AT1R orchestrates several important immunological functions and Losartan treatment has been previously demonstrated to have immunomodulatory properties. Ang II is the main effector molecule of RAS that upon binding to AT1R promotes vasoconstriction, inflammation, oxidative stress, coagulation, and fibrosis, all playing an important pathological role during SARS-CoV-2 infection 19 .

Furthermore, our work indicates a change in the relationship between autoantibodies targeting GPCRs and RAS that associate with COVID-19 severity, which was shown by increasing disruption of autoantibody correlations according disease burden. This observation provides new insights into the biology of autoantibodies, which is in line with our previous observation that GPCR-specific autoantibody signatures associate with physiologic and pathologic immune homeostasis 41 . Further, as several epitopes on highly interrelated GPCRs are likely overlapping 67 , a change in the correlation structure might indicate that new epitopes are targeted in severe COVID-19. These epitopes could have different functional properties.

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 diseases 13,41,52,68-72 , 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 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted August 26, 2021. ; https://doi.org/10.1101/2021.08.24.21262385 doi: medRxiv preprint 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 humans 14 , it is also possibly that several additional anti-GPCR autoantibodies remain to be discovered. Likewise, several SARS-CoV-2 strains have been identified 73 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 GPCRs 41 . Thus, another important issue to be addressed is whether the recently developed vaccines against COVID-19 74 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 clinically . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. 

. CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted August 26, 2021. ; https://doi.org/10.1101/2021.08.24.21262385 doi: medRxiv preprint 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 described 79 .

Briefly, duplicate samples of a 1:100 serum dilution were incubated at 4 °C for 2 h. The autoantibody concentrations were calculated as arbitrary units (U) by extrapolation from a standard curve of five standards ranging from 2.5 to 40 U/ml. The ELISAs were validated according to the Food and Drug Administration's Guidance for Industry: Bioanalytical Method Validation.

The interaction network of 17 GPCR-autoantibody targets was built using the online tool string 80 (https://string-db.org/). Gene ontology (GO) enrichment analysis of the 17 autoantibody targets was performed using GO Biological Process 2021 analysis through the Enrichr webtool 81-83 .

Circos Plot of antibody targets and pathway association was built using Circos online tool 84 .

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. https://www.r-project.org/), R . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted August 26, 2021. ; https://doi.org/10.1101/2021.08.24.21262385 doi: medRxiv preprint studio Version 1.4.1106 (R-Studio. https://www.rstudio.com/), and the R packages ggpubr, lemon, and ggplot2. Statistical differences of autoantibody levels were assessed using t-test.

Principal Component Analysis (PCA) with spectral decomposition 38,39 was used to measure the stratification power of the 17 autoantibodies to distinguish between COVID-19 (mild, moderate and severe patients) and healthy controls. PCA was performed using the R functions prcomp and princomp, through factoextra package (Principal Component Analysis in R: prcomp vs princomp.

http://www.sthda.com/english/articles/31-principal-component-methods-in-rpractical-guide/118-principal-component-analysis-in-r-prcomp-vs-princomp/).

We employed Random Forest model to construct a classifier able to discriminate between controls, mild, severe, and oxygen-dependent COVID-19 patients. This approach aimed to identify the most significant predictors for severe COVID-19. We trained a Random Forest model using the functionalities of the R package randomForest (version 4.6.14) 85 . Five thousand trees were used, and the number of variables resampled were equal to three. Follow-up analysis used the Gini decrease, number of nodes, and mean minimum depth as criteria to determine variable importance. The adequacy of the Random Forest model as a classifier was assessed . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted August 26, 2021. ; https://doi.org/10.1101/2021.08.24.21262385 doi: medRxiv preprint through out of bags error rate and ROC curve. For cross-validation, we split the dataset in training and testing samples, using 75% of the observations for training and 25% for testing.

Bivariate correlation analysis of autoantibodies for each group (controls, mild, severe, oxygendependent COVID-19 patients) was performed using the corrgram, psych, and inlmisc R packages. In addition, multilinear regression analysis of relationships between different variables (auto-antibodies) was performed using the R packages ggpubr, ggplot2 and ggExtra. Circle plots were also build using the R packages qgraph, ggplot2, psych, inlmisc to visualize the patterns of Spearman's rank correlation coefficients between autoantibodies. CCA 86 of autoantibodies against molecules associated with RAS, other GPCRs and SARS-CoV-2 entry molecules was performed using the R packages CCA and whitening 86 . CCA is a classic statistical tool to perform multivariate correlation analysis. We used log-transformed antibody levels to carry out both bivariate correlation and CCA analysis.

. CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) 

The authors declare that H.H. and K.S.F. are CellTrend managing directors and that GR is an advisor of the company CellTrend and earned an honorarium for her advice between 2011 and 2015. The other authors declare no competing interests.

. CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted August 26, 2021. ; https://doi.org/10.1101/2021.08.24.21262385 doi: medRxiv preprint . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted August 26, 2021. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted August 26, 2021. Table 1 . Thickness of rectangles in each small outer circles is proportional to their involvement of autoantibody targets and in multiple pathways. The inner circle represents genes and datasets with more connections to each other. The colors, numbers and percentage on the outer circles denote how pleiotropic and the respective each gene/pathway association. (C) Box plots of 11 antibodies with significantly different expression levels (illustrated in D with functional associations of their targets) in at least one group of COVID-19 patients (mild, moderate or severe group) compared to healthy controls. Significant differences between groups are indicated by asterisks (* p ≤ 0.05, ** p ≤ 0.01 and *** p ≤ 0.001).

A Renin-angiotensin system B Adrenergic signaling in cardiomyocytes C Calcium signaling D Renin secretion E GP130_JAK_STAT F Toll-Like receptor signaling network G Complement and coagulation cascades H Inflammatory mediator regulation of TRP channels I Regulation of actin cytoskeleton J Inflammation mediated by chemokine and cytokine signaling K Immune system L . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity (which was not certified by peer review) Autoantibodies corrrelations are plotted based on their corrrelation (that range from -1 to 1) with the first 2 canonical variates (x-CV1 and xCV2 or y-CV1 and y-CV2). Note, that this values ranging from -1 to 1 are not the same as the Spearman's rank correlation coefficient. Only autoantibodies with a correlation  0.6 of Spearman's rank correlation coefficient are shown while those with a correlation < 0.6 (grey points) have their names omitted. severe . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity (which was not certified by peer review) severe . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity (which was not certified by peer review)

Rogue antibodies could be driving severe COVID-19

Covid-19 and autoimmunity

Anti-body antibodies in COVID-19

The SARS-CoV-2 as an instrumental trigger of autoimmunity

SARS-CoV-2, the autoimmune virus

Prothrombotic autoantibodies in serum from patients hospitalized with COVID-19

Autoimmunity to the Lung Protective Phospholipid-Binding Protein Annexin A2 Predicts Mortality Among Hospitalized

Losartan for Patients With COVID-19 Requiring Hospitalization -Full Text View -ClinicalTrials

Entangling COVID-19 associated thrombosis into a secondary antiphospholipid antibody syndrome: Diagnostic and therapeutic perspectives (Review)

Autoantibodies against type I IFNs in patients with life-threatening COVID-19. Science (80-. )

Diverse functional autoantibodies in patients with COVID-19. Nat

REAP: A platform to identify autoantibodies that target the human exoproteome

Functional autoantibodies targeting G proteincoupled receptors in rheumatic diseases

Crystal structure of the β 2 adrenergic receptor-Gs protein complex

Autoantibodies against ACE2 and angiotensin type-1 receptors increase severity of COVID-19

Antibodies Against Angiotensin II Receptor Type 1 and Endothelin A Receptor Are Associated With an Unfavorable COVID19 Disease Course

Functional autoantibodies against G-protein coupled receptors in patients with persistent Long-COVID-19 symptoms

A historical perspective on ACE2 in the COVID-19 era

Angiotensin-(1-7)-A Potential Remedy for AKI: Insights Derived from the COVID-19 Pandemic

SARS-CoV-2 receptor is co-expressed with elements of the kinin-kallikrein, renin-angiotensin and coagulation systems in alveolar cells

We acknowledge the patients for the participation in this study. We also thank the São Paulo Research Foundation (FAPESP grants 2018/18886-9, 2020/01688-0, and 2020/07069-0 to OCM; 2020/09146-1 to PPF) for financial support. Computational analysis was supported by FAPESP. **

is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity (which was not certified by peer review) MASR.Ab