Distinct circulating autoantibodies are associated with COVID-19 hospitalization and SARS-CoV-2 neutralization activity

Distinct circulating autoantibodies are associated with COVID-19 hospitalization and SARS-CoV-2 neutralization activity | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Distinct circulating autoantibodies are associated with COVID-19 hospitalization and SARS-CoV-2 neutralization activity Rajesh Abraham Jacob, Hannah Ajoge, Michael D'Agonstino, Altynay Shigayeva, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6647603/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract SARS-CoV-2 infection disrupts the host’s immune system, leading to altered autoimmune responses. This study investigated host autoreactivities in SARS-CoV-2 infections and their association with severe COVID-19 and the neutralizing antibody response. A magnetic bead based multiplex assay was employed to detect 20 clinically relevant circulating autoantibodies in convalescent serum samples from 38 unvaccinated, SARS-CoV-2 infected patients, 14 of whom were hospitalized. Respiratory symptoms and co-morbidities were recorded for all patients. Clustering, correlation analysis, principal component analysis and neural network modeling were used to explore the relationship between autoantibodies, hospitalization and SARS-CoV-2 neutralization. The presence of one autoantibody correlated with the detection of multiple others. Although anti-IFNα antibodies were detected in 11% of the cohort and strongly associated with elevated levels of anti-ENAs, there was no significant association with clinical outcome. COVID-19 hospitalization was significantly associated with the collective expression of autoantibodies targeting three extractable-nuclear antigens (ENAs): SSA/Ro52, Jo-1 and RNP. In contrast, a separate set of autoantibodies targeting three ENAs: RNP/Sm, PCNA and Scl-70 along with the aminoacyl t-RNA synthetase PL-12, was strongly associated with the antiviral humoral immune response. In summary, this study has identified self-antigens targeted in hospitalized COVID-19 patients. Furthermore, we establish a novel link between the host autoantibody response and the humoral immune response, which plays a crucial role in neutralizing SARS-CoV-2 variants. Biological sciences/Immunology/Vaccines/Rna vaccines Biological sciences/Microbiology/Virology/Sars cov 2 Biological sciences/Immunology/Adaptive immunity/Humoral immunity/Antibodies SARS-CoV-2 Autoantibodies Type I Interferon Neutralizing Antibodies Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Autoantibodies that target various cellular components are linked to several autoimmune diseases (ADs). Over 100 ADs have been described and numerous autoepitopes identified, since the discovery of anti-DNA antibodies in systemic lupus erythematosus (SLE) patients in the 1940s ( 1 – 3 ). Common targets of autoantibodies include antinuclear antibodies (ANA) targeting DNA and histones, extractable nuclear antigens (ENA) present in the nucleus and cytoplasm, phospholipids and antineutrophil cytoplasmic antibodies (ANCA) directed against the cytoplasmic components of neutrophils. Preexisting autoantibodies can influence the course of infectious diseases, with viruses potentially triggering autoimmune conditions ( 2 , 4 – 7 ). Elevated autoantibody levels are observed in severe SARS-CoV-2 infected patients, although it remains unclear if autoantibodies are the cause or consequence of severe COVID-19 ( 8 – 11 ). During SARS-CoV-2 infection, autoantibodies are likely triggered by virus-induced cytokine storm ( 12 , 13 ), molecular mimicry between viral and host proteins ( 14 , 15 ), virus induced T cell exhaustion ( 16 , 17 ) and dysfunctional regulatory T cells ( 18 , 19 ). While ANAs can be elevated in long COVID-19 patients ( 20 , 21 ), ANA positivity has also been shown to be substantially more prevalent than in the general population ( 22 ). Furthermore, COVID-19 outpatients had an increased risk of developing inflammatory arthritis, connective tissue diseases and intestinal-related AD in compared to inpatients, highlighting the complex relationship between autoantibodies and COVID-19 ( 23 ). Type I interferon (IFN) induced innate immune responses are crucial for protective immunity as they reduce virus replication. Inborn IFN pathway genetic disorders predispose individuals to severe infections. Deficiencies in Toll-like receptor 3 (TLR3) and melanoma differentiation-associated protein 5 (MDA5), which detect double-stranded RNA are linked to severe viral infections, including influenza pneumonitis ( 24 , 25 ), herpes simplex virus-1 (HSV-1) encephalitis ( 26 – 28 ), enterovirus infection ( 29 ) and higher susceptibility to rhinovirus infections ( 30 ). Genetic defects in interferon regulatory factor 3 (IRF3), IRF7 and IRF9 can exacerbate conditions like HSV-1 encephalitis ( 31 ) influenza pneumonitis ( 32 , 33 ) and respiratory syncytial virus infection ( 32 ). Individuals with severe COVID-19 often harbor defects in TLR3, IRF3 and IRF7, critical for IFN responses ( 34 – 37 ). Consistent with inborn genetic disorders, anti-IFN autoantibodies which block IFN function contribute to poorer clinical outcomes. These anti-IFN autoantibodies were first detected in patients treated with IFNβ for nasopharyngeal carcinoma in the early 1980s and are prevalent in autoimmune conditions like SLE and autoimmune polyendocrine syndrome type 1 (APS-1) ( 38 – 40 ). Neutralizing anti-IFN autoantibodies are linked to a subset of critically ill COVID-19 patients with low levels of IFN ( 41 , 42 ). Anti-IFN autoantibodies are found in ~ 10% of patients with critical COVID-19, are linked to multi-organ failure and compromise antiviral defenses in the nasal mucosa enabling virus dissemination ( 43 – 47 ). In contrast, persistent IFN and associated interferon stimulated gene (ISG) responses can worsen symptoms by inducing hyperinflammation during viral infections in humans and animal models ( 48 – 50 ). Accordingly, IFN driven signatures led to TNF and IL1β-driven inflammation and worsened COVID-19 outcomes, resulting in upregulated ISG levels in postmortem lung tissues ( 51 ). Surprisingly, APS-1 patients demonstrated mild COVID-19 symptoms despite having anti-IFN neutralizing autoantibodies that suppressed signal transducer and activator of transcription 1 (STAT1) phosphorylation and reduced downstream IFN signaling ( 52 ). A recent study demonstrated a homeostatic role for anti-IFN neutralizing autoantibodies which reduced excessive IFN and inflammation in the nasal mucosa of SARS-CoV-2 infected patients with autoantibodies appearing within weeks of infection and associated with nasal IFNα secretion and efficient recovery ( 53 ). Here, we detected 20 common autoantibody signatures, along with anti-IFNα autoantibodies using convalescent serum samples from unvaccinated, SARS-CoV-2 infected patients and examined their relationship to disease severity. We further explored the correlation between circulating autoantibodies in the context of SARS-CoV-2 infection and identified a subset of anti-ENAs driving hospitalization. Finally, we establish a new link between autoantibodies and the humoral immune response targeting the spike region of SARS-CoV-2 variants, mediating its neutralization. Methods Human donors Informed consent was obtained for the collection of convalescent serum from 38 patients with laboratory confirmed SARS-CoV-2 infection. This study was approved by Sunnybrook Research Institute (REB#2218) and Sinai Health System (REB# 02-0118-U and 05-0016-C) ethics boards. Cells and viruses Human A549 lung and monkey Vero E6 epithelial cells were cultured as described previously ( 54 ). Vesicular stomatitis virus expressing green fluorescent protein (VSV-GFP) was used for IFNα neutralization assays at MOI 1.0. SB3, an ancestral SARS-CoV-2 variant and R.1 645, a variant under monitoring (VuM) were isolated and purified as described ( 54 , 55 ). The B.1.351, (beta), B.1.617.2 (delta) and BA.5 (omicron) was obtained from BEI Resources (Manassas, VA, United States). Experiments with SARS-CoV-2 were performed in a biosafety containment level 3 facility as approved by the institutional biosafety committee at McMaster University. Detection of autoantibodies Autoantibodies were detected using a multiplex panel (MilliporeSigma, Catalog # HAIAB-10K) following manufacturer’s guidelines. Fluorescence from labeled magnetic beads was measured with a MAGPIX instrument using xPONENT software. Fold increases in median fluorescent intensity (MFI) were calculated by dividing the MFI of each autoantibody with the MFI of sham-conjugated controls. Interferon treatment A549 cells were left untreated or treated with ten-fold serially diluted IFNα (Catalog # I4276, Sigma-Aldrich). Cells were pre-treated for 6 hours before infection with VSV-GFP at MOI 1.0. GFP was measured 24 hours post-infection to determine initiation of virus replication. Anti-IFNα neutralization assay The anti-IFNα neutralizing monoclonal antibody MMHA-2 (Invitrogen, Catalog # 211002) or convalescent serum samples were serially diluted and incubated with 0.1 and 1.0 ng/mL IFNα (Catalog # I4276, Sigma-Aldrich) for two hours at 37˚C. A549 cells were pre-treated with the antibody:IFNα mixture for six hours before infection with VSV-GFP at MOI 1.0. GFP was measured 24 hours post infection to determine initiation of virus replication. Enzyme-linked immunosorbent assay (ELISA) Anti-IFNα binding antibodies (catalog # 5018034, Invitrogen,) and total IgG antibodies (catalog # BMS2091, Invitrogen,) were determined using ELISA following manufacturer’s guidelines. SARS-CoV-2 neutralization assay Serially diluted serum samples were incubated with SARS-CoV-2 (150 PFU/well) at 37ºC for 1 hour before adding to pre-plated Vero E6 cells. Five days post-infection, luminescence was quantified with CellTiter-Glo 2.0 Reagent (catalog # G9243, Promega) using a BioTek Synergy H1 microplate reader. The luminescent signal, which was proportional to the amount of ATP present, was measured in Relative Light Units (RLU). Bioinformatics and statistical analysis Correlation was executed in R using ‘rcorr’ function from the ‘Hmisc’ package. Heatmaps were generated using ‘pheatmap’ package. ‘symnum’, an in-built function in R, was used to replace correlation coefficients with symbols based on the degree of relation. Principal component analysis (PCA) was executed in R using 'prcomp' function from the 'stats' package and visualized using ‘ggfortify’ package. Neural network was executed in R using ‘neuralnet’ package using 80% of the data for training and 20% for testing. Circa plots ( https://omgenomics.com/circa ) was used to visualize association of the level of autoantibodies with SARS-CoV-2 neutralization. Unpaired t test with Welch’s correction was used to calculate differences between hospitalized and non-hospitalized groups. The correlation between autoantibody levels and anti-IFNα antibody or SARS-CoV-2 neutralization was determined using the Pearson r product. Neutralization score was determined using a non-linear regression curve fit model. An unpaired t test was used to compare the neutralization of the SARS-CoV-2 isolates. GraphPad Prism 10 was used for the above statistical tests. Results Study population The clinical summary is outlined in Table 1 . The study cohort included 38 unvaccinated individuals infected with SARS-CoV-2. Serum samples were collected during the first and third Canadian pandemic waves in 2020 and 2021. Of the 38 patients, 63% (24/38) were managed as outpatients, while 37% (14/38) required hospitalization (Fig. 1 A). The median age was 56 and 66.5 years for non-hospitalized and hospitalized groups, respectively, with a higher proportion of females in the non-hospitalized group. Serum samples were collected at a median of 37 and 82 days following the first positive COVID-19 test for non-hospitalized and hospitalized groups, respectively. For the hospitalized group, the median time from onset of symptoms to hospital admission was six days. Common symptoms included fever, cough and shortness of breath, with shortness of breath being more prevalent in hospitalized group. The hospitalized group had a higher prevalence of co-morbidities, including cardiac, vascular, pulmonary and renal illnesses as well as cancer and mental health conditions. The ICU group had four patients, two of whom required intubation. Table 1 Clinical summary of patients from this study Summary Non-hospitalized Hospitalized ICU Samples, no (%) 24 ( 63 ) 14 ( 37 ) 4 (10.5) Age, median (IQR) 56 (34.3 to 63.0) 66.5 (56.5 to 85.3) 60 (55.5 to 64.5) Sex, female/male 14/10 4/10 0/4 Days to sampling from first COVID-19 positive test, median (IQR) 37 (24.0 to 48.5) 82 (38.5 to 93.8) 62.5 (15.3 to 91.8) Days to admission from onset of symptoms, median no (IQR) N/A 6 (2 to 9.8) 2.5 (0.5 to 5.3) Symptom, no (%) Asymptomatic 1 (4.2) 0 (0) 0 (0) Fever 9 (37.5) 7 ( 50 ) 3 (75) Cough 10 (41.7) 9 (64.3) 2 ( 50 ) Shortness of breath 1 (4.2) 7 ( 50 ) 2 ( 50 ) Co-morbidities, no (%) Diabetes 3 (12.5) 0 (0) 0 (0) Cardiac illnesses 2 (8.3) 2 (14.3) 0 (0) Vascular illnesses 3 (12.5) 10 (71.4) 3 (75) Pulmonary illnesses 1 (4.2) 2 (14.3) 0 (0) Renal illnesses 0 (0) 2 (14.3) 1 ( 25 ) Neuro-muscular illnesses 0 (0) 0 (0) 0 (0) Liver illnesses 0 (0) 0 (0) 0 (0) Gastro-intestinal illnesses 0 (0) 0 (0) 0 (0) Cancer conditions 1 (4.2) 2 (14.3) 0 (0) Rheumatologic illnesses 1 (4.2) 0 (0) 0 (0) Mental health diagnosis 0 (0) 3 (21.4) 0 (0) Immuno-compromising condition 0 (0) 0 (0) 0 (0) Intubation N/A 2 (14.3) 2 ( 50 ) Detection of autoimmune antibodies in convalescent sera We examined the relationship between autoimmune antibodies and hospitalization in SARS-CoV-2 infection by measuring autoantibody levels against 20 self-antigens linked to various clinical phenotypes, utilizing a magnetic bead-based multiplex approach. Of these, 75% (15 of 20) were ANAs targeting various nuclear components including DNA and RNA. All ANAs targeted ENAs, a subgroup comprising non-chromatin nuclear proteins. The panel also included two ANCAs, one anti-aminoacyl tRNA synthetase (ARS) antibody, one antiphospholipid antibody, and one C1q antibody targeting the complement C1 complex. This profiling enabled a comprehensive understanding of autoimmune response in SARS-CoV-2 infected convalescent patients. We observed a substantial variation in the prevalence of autoantibodies in our cohort with a 2.8- to 465.7-fold increase in autoantibody levels compared to sham-control beads (Fig. 1 B). To determine whether variations in autoantibody levels were related to differences in IgG, we measured the total polyclonal IgG concentration (Fig. 1 B). The mean IgG level was 1337 mg/dL (range, 1279–1397). There was no significant association between IgG levels and autoantibodies, except for CENP-B ( p = 0.0357), suggesting that the variation in autoantibodies is largely independent of total IgG levels. Correlation matrix and clustering of autoantibodies We then evaluated the association between each of the 20 autoantibodies and their clustering patterns. Correlation matrix grouped the autoantibodies into four clusters (Fig. 1 C). The first cluster (from the top) included five ANAs targeting various nuclear components (Ku, CENP-A, RNP/Sm, PCNA and Scl-70) and an anti-ARS antibody, PL-12. Each of the six autoantibodies in this cluster strongly correlated with each other. The second cluster contained two ANAs, Sm and PM/Scl-100, which significantly correlated with each other. The third cluster contained four ANAs (Ribosomal P, SSA/Ro52, RNP and Jo-1) and an anti-phospholipid antibody, β2-Glycoprotein, all of which significantly correlated with each other. The final cluster had seven autoantibodies: four ANAs (SSA/Ro60, SSB/La, Mi-2 and CENP-B), two ACNAs (Myeloperoxidase and Proteinase 3) and one anti-C1q antibody targeting the complement C1 component. The intra-cluster correlation was weakest for this cluster. In addition to the intra-cluster correlation, we observed significant inter cluster correlations (Fig. 1 C). Autoantibodies from cluster 1 were strongly associated ( p < 0.001) with those in cluster 2 and weakly with cluster 4; however, no association was found between cluster 1 and cluster 3. The two ANAs in cluster 2 significantly correlated with the four ANAs in cluster 3, while eliciting a weak correlation with cluster 4. Finally, there was significant association between autoantibodies in cluster 3 and cluster 4. Our clustering data reveal that the presence of one autoantibody correlates with the detection of multiple other autoantibodies, suggesting a complex and heterogenous autoimmune response in this cohort. Association of autoantibodies with COVID-19 progression Next, we performed principal component analysis (PCA) to reduce the dimensionality of the dataset and identify autoantibodies that drive COVID-19 associated hospitalization (Fig. 1 D). Dimension 1 (PC1) 1 and PC2 accounted for 40.2% and 21.2% of the total variance, respectively. However, PCA did not reveal any distinct separation between non-hospitalized, hospitalized and ICU groups indicating that autoantibodies by themselves are not associated with hospitalization in this cohort. Next, we focused on identifying signature patterns linked to disease progression using heatmap analysis (Fig. 1 E-H). This enabled visualization of fold-changes in autoantibody levels across samples and identification of clustering patterns that might mediate hospitalization. Heatmap analyses were performed on four groups: non-hospitalized, hospitalized, non-ICU hospitalized, and ICU. While the non-hospitalized and non-ICU hospitalized groups showed interspersed clustering, there was a distinct separation of autoantibody clusters in the hospitalized and ICU groups. Notably, three ENAs from the third cluster (Fig. 1 C), SSA/Ro52, Jo-1 and RNP, clustered in the hospitalized and ICU groups (Fig. 1 F, H). We noticed 3.8-, 3.5- and 1.9-fold increases in SSA/Ro52, Jo-1 and RNP levels, respectively, in hospitalized versus non-hospitalized groups (Table 2 ), suggesting that these antigens could be targets in COVID-19-associated hospitalized patients and may be linked to ICU admission. Next, we investigated whether the systemic autoantibody response was associated with hospitalization (Fig. 2 ). The mean fold increase in autoantibody levels compared to sham conjugated controls for the hospitalized and non-hospitalized groups are shown for each of the 20 autoantibodies (Table 2 ). There was no significant difference in the fold-change of autoantibody levels while comparing the non-hospitalized group to the hospitalized group. However, the collective presence of SSA/Ro52, Jo-1 and RNP autoantibodies predicted hospitalization status with 62.5% accuracy using a neural network model (Fig. 2 B). These findings indicate that this subset of autoantibodies is synergistically associated with hospitalization in this cohort. Table 2 Autoantibody levels in non-hospitalized and hospitalized category Autoantibodies Mean fold change (non-hosp) Mean fold change (hosp) p -value Anti-neutrophil cytoplasmic antibody Myeloperoxidase 3.4 1.8 ns Proteinase 3 46.1 23.1 ns Anti-aminoacyl tRNA synthetase (ARS) antibody PL-12 288.1 143.3 ns Anti-phospholipid antibody β2-Glycoprotein 21.0 19.5 ns Anti-complement antibody C1q 312.5 125.7 ns Extractable nuclear antigen antibody Centromere protein A (CENP-A) 141.5 92.6 ns Centromere protein B (CENP-B) 181.2 83.0 ns Histidyl tRNA synthetase (Jo-1) 242.6 848.2 ns Ku 299.1 163.8 ns Mi-2 421.0 161.3 ns Proliferating cell nuclear antigen (PCNA) 387.2 94.7 ns Ribonucleoprotein (RNP) 302.5 563.8 ns DNA topoisomerase I (Scl-70) 410.8 143.0 ns Smith (Sm) 74.6 53.2 ns Robert-Antigen/Sjogren’s A (SSA/Ro52) 135.7 509.7 ns Robert-Antigen/Sjogren’s A (SSA/Ro60) 40.4 29.1 ns Sjögren syndrome antigen B (SSB/La) 303.5 67.8 ns RNP/Sm 20.6 8.3 ns Ribosomal protein P 4.5 4.1 ns PM/Scl-100 2.8 3.8 ns Detection of binding and neutralizing anti-IFNα antibodies and their association with hospitalization Next, we investigated the role of circulating anti-IFNα autoantibodies on COVID-19-related hospitalization. A549 cells were pre-treated with serial ten-fold dilutions of IFNα and assayed for VSV-GFP replication to determine the minimal concentration required to maintain an antiviral state (Fig. 3 A). IFNα at concentrations of 0.01 ng/mL or higher resulted in a partial to complete antiviral state, with an IC 50 of 0.0125 ng/mL (95% CI of 0.01009 to 0.01580; Fig. 3 B). We next incubated A549 cells with 0.1 and 1 ng/mL IFNα in the presence of serially diluted control anti-human mouse IgG1 monoclonal antibody (MMHA-2) that binds and neutralizes human IFNα followed by VSV-GFP infection (Fig. 3 C). Treatment of A549 cells with IFNα in the absence of MMHA-2 reduced VSV-GFP infection compared to untreated control (Fig. 3 C lower panel). Incubation of MMHA-2 with IFNα resulted in a concentration-dependent increase in VSV-GFP infection for both IFNα treatments (Fig. 3 C upper and middle panels), with an IC 50 of 16.6 and 119.7 ng/mL of MMHA-2 for neutralizing 0.1 and 1.0 ng/mL of IFNα, respectively (Fig. 3 D). We next evaluated if IFNα autoantibodies in serum samples could dampen IFNα-induced antiviral activity, with ID 50 values, referred to as a “neutralization score”, derived for each serum sample. Two patient samples had a substantial neutralization score with autoantibodies functionally neutralizing IFNα. We further detected IFNα binding antibodies using ELISA in 11% (4/38) of the cohort. Of these, three were in the non-hospitalized category, while one was admitted to the ICU. We did not notice an association between the presence of anti-IFNα neutralizing or binding antibodies with hospitalization status (Fig. 3 E-G). Corroborating these findings, PCA analysis showed no distinct clustering between serum samples with and without anti-IFNα binding autoantibodies (Fig. 3 H). Our data provide evidence that a subset of SARS-CoV-2 patients have binding IFNα autoantibodies that are also capable of restricting an IFNα-induced antiviral state. Anti-IFNα binding autoantibodies are strongly associated with ENA autoantibodies Next, we examined associations between binding anti-IFNα autoantibodies and the other 20 autoantibodies (Fig. 3 I). Notably, a positive correlation was noticed between binding anti-IFNα autoantibodies and seven other autoantibodies. Among them, four (SSA/Ro52, RNP, PM/Scl-100, Jo-1) showed a strong correlation indicating that the presence of anti-IFNα autoantibodies is associated with a higher frequency of other autoantibodies. Circulating autoantibodies are associated with SARS-CoV-2 neutralization Last, we tested whether autoantibodies were associated with an increased humoral immune response against SARS-CoV-2. Five live SARS-CoV-2 isolates were used to determine neutralization activity: SB3 (ancestral), B.1.351 (beta), R.1 645 (R.1), B.1.617.2 (delta), and BA.5 (omicron) ( 54 ). SB3, R.1 645 and B.1.617.2 were neutralized by 63% (24/38), 76% (29/38) and 61% (23/38) of samples respectively at an ID 50 > 25 (Fig. 4 A). In contrast, B.1.351 and BA.5 were neutralized by only 16% (6/38) and 13% (5/38) of samples respectively (Fig. 4 A). B.1.351 was significantly more resistant to neutralization than SB3 ( p = 0.0012), R.1 645 ( p < 0.0001) and B.1.617.2 ( p = 0.0199). Similarly, BA.5 was significantly more resistant to neutralization than SB3 ( p = 0.0006), R.1 645 ( p < 0.0001) and B.1.617.2 ( p = 0.0181). To explore potential links between autoantibody levels and neutralization capacity, a correlation analysis was performed (Fig. 4 B). Strikingly, 12 of the 20 autoantibodies were significantly correlated with B.1.351 neutralization, suggesting that autoantibodies may enhance the neutralization activity against resistant SARS-CoV-2 isolates. Six ENAs (PCNA, Scl-70, RNP/Sm, CENP-A, Sm and Ku) along with an aminoacyl-tRNA synthetase (PL-12), showed the strongest correlation ( p < 0.0001), suggesting that targeting of specific autoantigens is associated with significantly greater B.1.351 neutralization. Similarly, 10 autoantibodies were significantly correlated with B.1.617.2 neutralization, with RNP/Sm, PCNA, Scl-70 and PL-12 showing the strongest correlations ( p < 0.0001). Interestingly, the four autoantibodies strongly associated with both B.1.351 and B.1.617.2 neutralization were grouped in cluster 1 (Fig. 1 C). Only two autoantibodies, proteinase 3 and CENP-B, were associated with BA.5 neutralization. There was no association observed for the other two sensitive SARS-CoV-2 isolates, SB3 and R.1 645. To further explore the relationship, we conducted PCA by categorizing samples as “neutralizers” (ID 50 > 25) or “non-neutralizers” (ID 50 < 25). There was no clustering between the two groups, indicating that autoantibodies collectively do not influence SARS-CoV-2 neutralization (Fig. 4 C-G). These findings imply that specific autoreactivities correlates with SARS-CoV-2 neutralization activity. Discussion We detected levels of 20 clinically relevant, circulating autoantibodies frequently found in ADs using serum samples from unvaccinated, SARS-CoV-2 infected, recovered individuals and determined if autoreactivity was associated with severe COVID-19 using hospitalization as the primary criterion (Fig. 1 , 2 ). Although we observed the presence of a diverse repertoire of autoantibodies in acute SARS-CoV-2, there was no association between individual autoantibody frequency and hospitalization. However, the presence of three autoantibodies targeting SSA/Ro52, Jo-1 and RNP, were collectively associated with hospitalization (Fig. 2 ). Our data indicates that the disruption of immunological tolerance is associated with hospitalization in SARS-CoV-2 acute infections and may serve as a link between infectious diseases and autoimmunity ( 2 , 4 , 6 ). Furthermore, 11% and 5% of our cohort had binding and neutralizing anti-IFNα autoantibodies, respectively, with anti-IFNα antibodies significantly associated with six ENAs and β2-glycoprotein (Fig. 3 ). Finally, the presence of a subset of autoantibodies was linked to enhanced neutralization of B.1.351, B.1.617.2 and BA.5.(Fig. 4 ). Transcriptomic and proteomic analyses from our group and others have highlighted the enrichment of antiviral responses and type I IFN signaling pathways leading to the expression of ISGs during SARS-CoV-2 infection ( 54 , 56 , 57 ). Here, we demonstrate the presence of anti-IFNα neutralizing antibodies in acute SARS-CoV-2 infection. The prevalence of these antibodies is slightly higher than the 1.5% observed in a blood bank study ( 58 ) and lower than the 10% observed in individuals with life-threatening COVID-19 pneumonia, of whom 94% were men ( 43 ). While systemic anti-IFN autoantibodies have been linked to higher viral loads and severe COVID-19, they serve to reduce excessive inflammation in APS-1 patients, leading to milder COVID-19 symptoms ( 43 , 44 , 52 ). The homeostatic function of anti-IFNα neutralizing autoantibodies was demonstrated in the nasal mucosa of SARS-CoV-2 infected patients, where these autoantibodies appeared within weeks of infection, reduced excessive nasal IFNα secretion and inflammation, thereby contributing to an efficient recovery ( 53 ). Our data show that neutralizing anti-IFNα antibodies are not driving hospitalization and may in turn be balancing the IFN activity to prevent excessive inflammation. Finally, we establish a novel link between SARS-CoV-2 neutralization and autoantibody response (Fig. 4 ). Although previous studies show an association between autoantibodies and anti-SARS-CoV-2 IgG targeting the receptor-binding domain (RBD), non-structural protein 1 (NSP1) and nucleocapsid protein ( 8 , 59 ), it remained unclear if autoantibodies in COVID-19 patients associate with SARS-CoV-2 neutralization. Of the 20 autoantibodies examined, 12 were significantly associated with the neutralization of B.1.351, and 10 were significantly associated with B.1.617.2 neutralization, suggesting that the autoimmune response might modulate the anti-viral humoral immune response. A higher frequency of autoantibodies correlates with the ability to neutralize multiple human immunodeficiency virus-1 (HIV-1) subtypes, thereby broadening the neutralization spectrum in some HIV-1 infected individuals ( 60 , 61 ). Similarly, anti-influenza antibodies from SLE patients have a higher avidity and neutralization capacity ( 62 ). The development of neutralizing antibodies may be controlled by immune tolerance mechanisms, as observed in HIV-1 and influenza virus infections, where antibodies develop long complementarity determining region 3 (CDR3) loops and undergo extensive somatic hypermutation ( 63 , 64 ). SARS-CoV-2 infections associate with the development of new-onset IgG autoantibodies targeting numerous self-protein targets across a wide range of tissues ( 8 , 9 , 11 ). Furthermore, autoimmunity is a key feature of post-acute sequelae of COVID-19 (PASC), observed in approximately 10% of SARS-CoV-2 infections with persistent symptoms over 3 months after the initial infection ( 65 ). Polyautoimmunity occurs in 62% of patients with PASC, characterized by the presence of ANAs detected over 12 months post-COVID-19 and correlating with neurological disorder intensity ( 20 , 59 , 66 ). Studies using health record data suggest an increased risk of AD in patients between 3 and 15-months post-SARS-CoV-2 infection with vaccination reducing risk ( 23 , 67 ). Although this observation suggests that autoantibodies could serve as biomarkers of PASC, limited evidence supports that targeting specific autoantibodies could reverse it. Additionally, infections with human cytomegalovirus, influenza virus, Epstein Barr virus, and chikungunya virus have also been linked to autoimmunity, highlighting that the immunological mechanisms that drive virus-induced ADs are not exclusive to SARS-CoV-2 ( 68 – 71 ). A limitation of our study is the absence of longitudinal samples that would have been valuable to explore whether autoantibody levels were transient. Additionally, we did not have a direct comparison between our SARS-CoV-2 infection to an uninfected control group. However, seroprevalence data suggest that 76% of the Canadian population had infection-acquired antibodies from prior SARS-CoV-2 infections ( 72 ), making the establishment of a clear control group for comparison difficult. Some of our interpretations may also be influenced by the fact that hospitalized patients were on average 10 years older and had more males compared to the non-hospitalized group. Another limitation is that we focused exclusively on the IFNα2 subtype. Finally, limitations in serum sample availability precluded us from performing an IgG depletion in IFN-neutralizing patients. In conclusion, our findings suggest that the presence of autoantibodies in an acute setting may not independently lead to a pathological clinical phenotype; however, the presence of multiple, distinct autoantibodies could contribute to severe COVID-19. Identifying specific autoantibodies associated with severe COVID-19, provides valuable insights into biomarkers. Notably, we observed that the generation of anti-IFNα antibodies is correlated with the presence of several other autoantibodies indicating a common feature governing the disruption of immune tolerance mechanism. Finally, our data showing a correlation between autoantibodies and SARS-CoV-2 neutralization in COVID-19 infections emphasize the need of further research to explore cross-reactivity of SARS-CoV-2 proteins with other autoepitopes and to determine if SARS-CoV-2 infected patients with AD develop a broader neutralizing antibody response. Declarations Declaration of interests The authors declare that they have no relevant conflicts of interest. Submission declaration The article is not under consideration for publication elsewhere. Author Contribution RAJ and KM conceptualized and designed the study. RAJ, AS, AJM, and SM collected the data. MRD, AB, MSM, AHM, SM contributed resources. RAJ, HOA analyzed and interpreted the data. RAJ and KM drafted the manuscript. All authors revised and approved the final manuscript. Acknowledgments This work was supported by a COVID-19 response grant to KM from the Canadian Institutes of Health Research (CIHR #OV4-170645). MSM is supported by a CIHR COVID-19 rapid response grant, a CIHR new investigator award, and an Ontario early researcher award. Convalescent serum sample collection was supported by a COVID-19 response grant to AM and SM from the CIHR (#439999). Data Availability All data are available in the main text and can be made available by request to the corresponding author. References Robbins WC, Holman HR, Deicher H, Kunkel HG. Complement fixation with cell nuclei and DNA in lupus erythematosus. Proc Soc Exp Biol Med. 1957;96(3):575–9. Davidson A, Diamond B. Autoimmune diseases. N Engl J Med. 2001;345(5):340–50. Mackay IR, Rowley MJ. Autoimmune epitopes: autoepitopes. Autoimmun Rev. 2004;3(7–8):487–92. Mobasheri L, Nasirpour MH, Masoumi E, Azarnaminy AF, Jafari M, Esmaeili SA. SARS-CoV-2 triggering autoimmune diseases. Cytokine. 2022;154:155873. Smatti MK, Cyprian FS, Nasrallah GK, Al Thani AA, Almishal RO, Yassine HM. Viruses and Autoimmunity: A Review on the Potential Interaction and Molecular Mechanisms. Viruses. 2019;11(8). Puel A, Bastard P, Bustamante J, Casanova JL. Human autoantibodies underlying infectious diseases. J Exp Med. 2022;219(4). Abdel-Wahab N, Talathi S, Lopez-Olivo MA, Suarez-Almazor ME. Risk of developing antiphospholipid antibodies following viral infection: a systematic review and meta-analysis. Lupus. 2018;27(4):572–83. Chang SE, Feng A, Meng W, Apostolidis SA, Mack E, Artandi M, et al. New-onset IgG autoantibodies in hospitalized patients with COVID-19. Nat Commun. 2021;12(1):5417. Zuo Y, Estes SK, Ali RA, Gandhi AA, Yalavarthi S, Shi H, et al. Prothrombotic autoantibodies in serum from patients hospitalized with COVID-19. Sci Transl Med. 2020;12(570). Woodruff MC, Ramonell RP, Haddad NS, Anam FA, Rudolph ME, Walker TA, et al. Dysregulated naive B cells and de novo autoreactivity in severe COVID-19. Nature. 2022;611(7934):139–47. Wang EY, Mao T, Klein J, Dai Y, Huck JD, Jaycox JR, et al. Diverse functional autoantibodies in patients with COVID-19. Nature. 2021;595(7866):283–8. Hu B, Huang S, Yin L. The cytokine storm and COVID-19. J Med Virol. 2021;93(1):250–6. Song P, Li W, Xie J, Hou Y, You C. Cytokine storm induced by SARS-CoV-2. Clin Chim Acta. 2020;509:280–7. Adiguzel Y. Molecular mimicry between SARS-CoV-2 and human proteins. Autoimmun Rev. 2021;20(4):102791. Arevalo-Cortes A, Rodriguez-Pinto D, Aguilar-Ayala L. Evidence for Molecular Mimicry between SARS-CoV-2 and Human Antigens: Implications for Autoimmunity in COVID-19. Autoimmune Dis. 2024;2024:8359683. Diao B, Wang C, Tan Y, Chen X, Liu Y, Ning L, et al. Reduction and Functional Exhaustion of T Cells in Patients With Coronavirus Disease 2019 (COVID-19). Front Immunol. 2020;11:827. Kusnadi A, Ramirez-Suastegui C, Fajardo V, Chee SJ, Meckiff BJ, Simon H, et al. Severely ill COVID-19 patients display impaired exhaustion features in SARS-CoV-2-reactive CD8(+) T cells. Sci Immunol. 2021;6(55). Goncalves-Pereira MH, Santiago L, Ravetti CG, Vassallo PF, de Andrade MVM, Vieira MS, et al. Dysfunctional phenotype of systemic and pulmonary regulatory T cells associate with lethal COVID-19 cases. Immunology. 2023;168(4):684–96. Meckiff BJ, Ramirez-Suastegui C, Fajardo V, Chee SJ, Kusnadi A, Simon H, et al. Imbalance of Regulatory and Cytotoxic SARS-CoV-2-Reactive CD4(+) T Cells in COVID-19. Cell. 2020;183(5):1340-53 e16. Son K, Jamil R, Chowdhury A, Mukherjee M, Venegas C, Miyasaki K, et al. Circulating anti-nuclear autoantibodies in COVID-19 survivors predict long COVID symptoms. Eur Respir J. 2023;61(1). Seessle J, Waterboer T, Hippchen T, Simon J, Kirchner M, Lim A, et al. Persistent Symptoms in Adult Patients 1 Year After Coronavirus Disease 2019 (COVID-19): A Prospective Cohort Study. Clin Infect Dis. 2022;74(7):1191–8. Peluso MJ, Thomas IJ, Munter SE, Deeks SG, Henrich TJ. Lack of Antinuclear Antibodies in Convalescent Coronavirus Disease 2019 Patients With Persistent Symptoms. Clin Infect Dis. 2022;74(11):2083–4. Chang R, Yen-Ting Chen T, Wang SI, Hung YM, Chen HY, Wei CJ. Risk of autoimmune diseases in patients with COVID-19: A retrospective cohort study. EClinicalMedicine. 2023;56:101783. Lim HK, Huang SXL, Chen J, Kerner G, Gilliaux O, Bastard P, et al. Severe influenza pneumonitis in children with inherited TLR3 deficiency. J Exp Med. 2019;216(9):2038–56. Bucciol G, Desmet L, Leuven Laboratory of Inborn Errors of I, Corveleyn A, Meyts I. Pathogenic P554S Variant in TLR3 in a Patient with Severe Influenza Pneumonia. J Clin Immunol. 2022;42(2):430-2. Zhang SY. Herpes simplex virus encephalitis of childhood: inborn errors of central nervous system cell-intrinsic immunity. Hum Genet. 2020;139(6–7):911–8. Lim HK, Seppanen M, Hautala T, Ciancanelli MJ, Itan Y, Lafaille FG, et al. TLR3 deficiency in herpes simplex encephalitis: high allelic heterogeneity and recurrence risk. Neurology. 2014;83(21):1888–97. Guo Y, Audry M, Ciancanelli M, Alsina L, Azevedo J, Herman M, et al. Herpes simplex virus encephalitis in a patient with complete TLR3 deficiency: TLR3 is otherwise redundant in protective immunity. J Exp Med. 2011;208(10):2083–98. Chen J, Jing H, Martin-Nalda A, Bastard P, Riviere JG, Liu Z, et al. Inborn errors of TLR3- or MDA5-dependent type I IFN immunity in children with enterovirus rhombencephalitis. J Exp Med. 2021;218(12). Lamborn IT, Jing H, Zhang Y, Drutman SB, Abbott JK, Munir S, et al. Recurrent rhinovirus infections in a child with inherited MDA5 deficiency. J Exp Med. 2017;214(7):1949–72. Andersen LL, Mork N, Reinert LS, Kofod-Olsen E, Narita R, Jorgensen SE, et al. Functional IRF3 deficiency in a patient with herpes simplex encephalitis. J Exp Med. 2015;212(9):1371–9. Hernandez N, Melki I, Jing H, Habib T, Huang SSY, Danielson J, et al. Life-threatening influenza pneumonitis in a child with inherited IRF9 deficiency. J Exp Med. 2018;215(10):2567–85. Ciancanelli MJ, Huang SX, Luthra P, Garner H, Itan Y, Volpi S, et al. Infectious disease. Life-threatening influenza and impaired interferon amplification in human IRF7 deficiency. Science. 2015;348(6233):448–53. Zhang Q, Bastard P, Liu Z, Le Pen J, Moncada-Velez M, Chen J, et al. Inborn errors of type I IFN immunity in patients with life-threatening COVID-19. Science. 2020;370(6515). Matuozzo D, Talouarn E, Marchal A, Zhang P, Manry J, Seeleuthner Y, et al. Rare predicted loss-of-function variants of type I IFN immunity genes are associated with life-threatening COVID-19. Genome Med. 2023;15(1):22. Saheb Sharif-Askari N, Hafezi S, Saheb Sharif-Askari F, Ali Hussain Alsayed H, S BMA, Alsafar HS, et al. Multiple inborn errors of type I IFN immunity in a 33-year-old male with a fatal case of COVID-19. Heliyon. 2024;10(8):e29338. Kholaiq H, Abdelmoumen Y, Moundir A, El Kettani A, Ailal F, Benhsaien I, et al. Human Genetic and Immunological Determinants of SARS-CoV-2 Infection and Multisystem Inflammatory Syndrome in Children. Clin Exp Immunol. 2024. Panem S, Check IJ, Henriksen D, Vilcek J. Antibodies to alpha-interferon in a patient with systemic lupus erythematosus. J Immunol. 1982;129(1):1–3. Levin M. Anti-interferon auto-antibodies in autoimmune polyendocrinopathy syndrome type 1. PLoS Med. 2006;3(7):e292. Vallbracht A, Treuner J, Flehmig B, Joester KE, Niethammer D. Interferon-neutralizing antibodies in a patient treated with human fibroblast interferon. Nature. 1981;289(5797):496–7. Hadjadj J, Yatim N, Barnabei L, Corneau A, Boussier J, Smith N, et al. Impaired type I interferon activity and inflammatory responses in severe COVID-19 patients. Science. 2020;369(6504):718–24. Trouillet-Assant S, Viel S, Gaymard A, Pons S, Richard JC, Perret M, et al. Type I IFN immunoprofiling in COVID-19 patients. J Allergy Clin Immunol. 2020;146(1):206–8 e2. Bastard P, Rosen LB, Zhang Q, Michailidis E, Hoffmann HH, Zhang Y, et al. Autoantibodies against type I IFNs in patients with life-threatening COVID-19. Science. 2020;370(6515). Bastard P, Gervais A, Le Voyer T, Rosain J, Philippot Q, Manry J, et al. Autoantibodies neutralizing type I IFNs are present in ~ 4% of uninfected individuals over 70 years old and account for ~ 20% of COVID-19 deaths. Sci Immunol. 2021;6(62). Koning R, Bastard P, Casanova JL, Brouwer MC, van de Beek D, with the Amsterdam UMCC-BI. Autoantibodies against type I interferons are associated with multi-organ failure in COVID-19 patients. Intensive Care Med. 2021;47(6):704–6. Goncalves D, Mezidi M, Bastard P, Perret M, Saker K, Fabien N, et al. Antibodies against type I interferon: detection and association with severe clinical outcome in COVID-19 patients. Clin Transl Immunology. 2021;10(8):e1327. Lopez J, Mommert M, Mouton W, Pizzorno A, Brengel-Pesce K, Mezidi M, et al. Early nasal type I IFN immunity against SARS-CoV-2 is compromised in patients with autoantibodies against type I IFNs. J Exp Med. 2021;218(10). Cameron MJ, Ran L, Xu L, Danesh A, Bermejo-Martin JF, Cameron CM, et al. Interferon-mediated immunopathological events are associated with atypical innate and adaptive immune responses in patients with severe acute respiratory syndrome. J Virol. 2007;81(16):8692–706. Teijaro JR, Ng C, Lee AM, Sullivan BM, Sheehan KC, Welch M, et al. Persistent LCMV infection is controlled by blockade of type I interferon signaling. Science. 2013;340(6129):207–11. Channappanavar R, Fehr AR, Vijay R, Mack M, Zhao J, Meyerholz DK, et al. Dysregulated Type I Interferon and Inflammatory Monocyte-Macrophage Responses Cause Lethal Pneumonia in SARS-CoV-Infected Mice. Cell Host Microbe. 2016;19(2):181–93. Lee JS, Park S, Jeong HW, Ahn JY, Choi SJ, Lee H, et al. Immunophenotyping of COVID-19 and influenza highlights the role of type I interferons in development of severe COVID-19. Sci Immunol. 2020;5(49). Meisel C, Akbil B, Meyer T, Lankes E, Corman VM, Staudacher O, et al. Mild COVID-19 despite autoantibodies against type I IFNs in autoimmune polyendocrine syndrome type 1. J Clin Invest. 2021;131(14). Babcock BR, Kosters A, Eddins DJ, Donaire MSB, Sarvadhavabhatla S, Pae V, et al. Transient anti-interferon autoantibodies in the airways are associated with recovery from COVID-19. Sci Transl Med. 2024;16(772):eadq1789. Jacob RA, Zhang A, Ajoge HO, D'Agostino MR, Nirmalarajah K, Shigayeva A, et al. Sensitivity to Neutralizing Antibodies and Resistance to Type I Interferons in SARS-CoV-2 R.1 Lineage Variants, Canada. Emerg Infect Dis. 2023;29(7):1386–96. Banerjee A, Nasir JA, Budylowski P, Yip L, Aftanas P, Christie N, et al. Isolation, Sequence, Infectivity, and Replication Kinetics of Severe Acute Respiratory Syndrome Coronavirus 2. Emerg Infect Dis. 2020;26(9):2054–63. Banerjee A, El-Sayes N, Budylowski P, Jacob RA, Richard D, Maan H, et al. Experimental and natural evidence of SARS-CoV-2-infection-induced activation of type I interferon responses. iScience. 2021;24(5):102477. Hatton CF, Botting RA, Duenas ME, Haq IJ, Verdon B, Thompson BJ, et al. Delayed induction of type I and III interferons mediates nasal epithelial cell permissiveness to SARS-CoV-2. Nat Commun. 2021;12(1):7092. Vazquez SE, Bastard P, Kelly K, Gervais A, Norris PJ, Dumont LJ, et al. Neutralizing Autoantibodies to Type I Interferons in COVID-19 Convalescent Donor Plasma. J Clin Immunol. 2021;41(6):1169–71. Rojas M, Rodriguez Y, Acosta-Ampudia Y, Monsalve DM, Zhu C, Li QZ, et al. Autoimmunity is a hallmark of post-COVID syndrome. J Transl Med. 2022;20(1):129. Borrow P, Moody MA. Immunologic characteristics of HIV-infected individuals who make broadly neutralizing antibodies. Immunol Rev. 2017;275(1):62–78. Haynes BF, Wiehe K, Borrow P, Saunders KO, Korber B, Wagh K, et al. Strategies for HIV-1 vaccines that induce broadly neutralizing antibodies. Nat Rev Immunol. 2023;23(3):142–58. Kaur K, Zheng NY, Smith K, Huang M, Li L, Pauli NT, et al. High Affinity Antibodies against Influenza Characterize the Plasmablast Response in SLE Patients After Vaccination. PLoS One. 2015;10(5):e0125618. Krammer F. The human antibody response to influenza A virus infection and vaccination. Nat Rev Immunol. 2019;19(6):383–97. Landais E, Moore PL. Development of broadly neutralizing antibodies in HIV-1 infected elite neutralizers. Retrovirology. 2018;15(1):61. Altmann DM, Whettlock EM, Liu S, Arachchillage DJ, Boyton RJ. The immunology of long COVID. Nat Rev Immunol. 2023;23(10):618–34. Seibert FS, Stervbo U, Wiemers L, Skrzypczyk S, Hogeweg M, Bertram S, et al. Severity of neurological Long-COVID symptoms correlates with increased level of autoantibodies targeting vasoregulatory and autonomic nervous system receptors. Autoimmun Rev. 2023;22(11):103445. Tesch F, Ehm F, Vivirito A, Wende D, Batram M, Loser F, et al. Incident autoimmune diseases in association with SARS-CoV-2 infection: a matched cohort study. Clin Rheumatol. 2023;42(10):2905–14. Maek ANW, Silachamroon U. Presence of autoimmune antibody in chikungunya infection. Case Rep Med. 2009;2009:840183. Loza-Tulimowska M, Semkow R, Michalak T, Nowoslawski A. Autoantibodies in sera of influenza patients. Acta Virol. 1976;20(3):202–7. Houen G, Trier NH. Epstein-Barr Virus and Systemic Autoimmune Diseases. Front Immunol. 2020;11:587380. Gugliesi F, Pasquero S, Griffante G, Scutera S, Albano C, Pacheco SFC, et al. Human Cytomegalovirus and Autoimmune Diseases: Where Are We? Viruses. 2021;13(2). Murphy TJ, Swail H, Jain J, Anderson M, Awadalla P, Behl L, et al. The evolution of SARS-CoV-2 seroprevalence in Canada: a time-series study, 2020–2023. CMAJ. 2023;195(31):E1030-E7. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 16 Jul, 2025 Reviews received at journal 15 Jul, 2025 Reviewers agreed at journal 06 Jun, 2025 Reviewers agreed at journal 03 Jun, 2025 Reviews received at journal 19 May, 2025 Reviewers agreed at journal 19 May, 2025 Reviewers invited by journal 16 May, 2025 Editor assigned by journal 16 May, 2025 Submission checks completed at journal 15 May, 2025 First submitted to journal 12 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6647603","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":458980800,"identity":"5e3e8f76-d77c-454e-954f-4b17803acd1d","order_by":0,"name":"Rajesh Abraham Jacob","email":"","orcid":"","institution":"McMaster University","correspondingAuthor":false,"prefix":"","firstName":"Rajesh","middleName":"Abraham","lastName":"Jacob","suffix":""},{"id":458980801,"identity":"59d618b1-4b0e-41a1-aaac-73fb998ad5e4","order_by":1,"name":"Hannah Ajoge","email":"","orcid":"","institution":"McMaster University","correspondingAuthor":false,"prefix":"","firstName":"Hannah","middleName":"","lastName":"Ajoge","suffix":""},{"id":458980802,"identity":"474bd16b-6a95-4a43-8e31-ae5607110fcb","order_by":2,"name":"Michael D'Agonstino","email":"","orcid":"","institution":"McMaster University","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"D'Agonstino","suffix":""},{"id":458980803,"identity":"3b98e91c-ecf0-4045-a25e-b8eb05d2cddb","order_by":3,"name":"Altynay Shigayeva","email":"","orcid":"","institution":"University of Toronto","correspondingAuthor":false,"prefix":"","firstName":"Altynay","middleName":"","lastName":"Shigayeva","suffix":""},{"id":458980804,"identity":"993a4024-bbce-474d-b649-ad587eedb547","order_by":4,"name":"Arinjay Banerjee","email":"","orcid":"","institution":"University of Saskatchewan","correspondingAuthor":false,"prefix":"","firstName":"Arinjay","middleName":"","lastName":"Banerjee","suffix":""},{"id":458980805,"identity":"cbce63a4-782c-487a-aa20-ccd8f7069544","order_by":5,"name":"Matthew Miller","email":"","orcid":"","institution":"McMaster University","correspondingAuthor":false,"prefix":"","firstName":"Matthew","middleName":"","lastName":"Miller","suffix":""},{"id":458980806,"identity":"cc635f53-105d-4777-86ed-631b19cfe560","order_by":6,"name":"Allison McGeer","email":"","orcid":"","institution":"University of Toronto","correspondingAuthor":false,"prefix":"","firstName":"Allison","middleName":"","lastName":"McGeer","suffix":""},{"id":458980807,"identity":"61549c46-f78e-497d-a0b5-20588401d770","order_by":7,"name":"Samira Mubareka","email":"","orcid":"","institution":"University of Toronto","correspondingAuthor":false,"prefix":"","firstName":"Samira","middleName":"","lastName":"Mubareka","suffix":""},{"id":458980808,"identity":"b13eceaf-3318-4542-a640-afb301b96ca0","order_by":8,"name":"Karen Mossman","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIiWNgGAWjYJACxoYK0rWcgdLEa2lsI0WLbnv7448z522T120//vzBxz0M8vyENJqdOWMmuXHbbcNtZ3IMG2c8YzCccYCQlhs5bIwPt91OMDuQw9jMc4AhgYGwlvTHHx/OAWo5//xh8x+gFnnCWhIMJDc2ALXcSDBsBtqQYEBQC8gvM44B/XLjjeHMngMShhsJajkODLGemtvyZufTH3z4ccBGXo6QFnQgQaL6UTAKRsEoGAVYAQBfyEx2rdG6KwAAAABJRU5ErkJggg==","orcid":"","institution":"McMaster University","correspondingAuthor":true,"prefix":"","firstName":"Karen","middleName":"","lastName":"Mossman","suffix":""}],"badges":[],"createdAt":"2025-05-12 14:23:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6647603/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6647603/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83283586,"identity":"c886a672-ace4-4413-8fe5-f7036799977c","added_by":"auto","created_at":"2025-05-22 10:51:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":384404,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDetection of autoantibodies in the cohort and clustering analysis. \u003c/strong\u003e(A) Overview of cohort used in this study. (B) Fold increase in the levels of 20 clinically relevant autoantibodies and total IgG levels. Anti-ENA (black), anti-ANCA (red), anti-phospholipid (blue), anti-aminoacyl tRNA synthetase (green), anti-complement (brown) and total IgG (purple). (C) Clustered heatmap showing the clustering of autoantibodies. Clusters are horizontally separated using dashed lines. (D) Principal component analysis in ICU, hospitalized and non-hospitalized groups. (red, ICU; green, hospitalized; blue, non-hospitalized) (E-H) Heatmap analysis on non-hospitalized, hospitalized, non-ICU hospitalized, and ICU groups.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6647603/v1/ee2e13c3daca7f9f1446be20.png"},{"id":83284664,"identity":"04723eb6-5fca-474d-8e0b-03141b2da821","added_by":"auto","created_at":"2025-05-22 11:07:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":102754,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation of autoantibodies to COVID-19 hospitalization. \u003c/strong\u003e(A)\u003cstrong\u003e \u003c/strong\u003eMean autoantibody levels for each of the 20 autoantibodies between hospitalized and non-hospitalized groups.\u003cstrong\u003e \u003c/strong\u003eStatistical significance calculated using unpaired t test (ns - not significant). (B) SSA/Ro52, Jo-1 and RNP autoantibodies collectively predicted hospitalization status in a neural network model.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6647603/v1/2b04ab3cf57b4df5fee80410.png"},{"id":83284297,"identity":"00c031c0-4840-4d15-8726-17d0d6273bde","added_by":"auto","created_at":"2025-05-22 10:59:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":596505,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDetection of anti-IFNα antibodies and their association to hospitalization\u003c/strong\u003e (A) Activity of IFNα on VSV-GFP replication in human A549 lung epithelial cells. (B) IC\u003csub\u003e50\u003c/sub\u003e determination of IFNa activity from (A). (C) Anti-IFNα neutralization assay using a control anti-IFNα monoclonal neutralizing antibody (nAb), MMHA-2. (D) IC\u003csub\u003e50\u003c/sub\u003e determination of neutralization activity from (C). (E-F) Association of anti-IFNα neutralizing antibodies to COVID-19 hospitalization.\u003cstrong\u003e \u003c/strong\u003e(G) Association of anti-IFNα binding antibodies to COVID-19 hospitalization\u003cstrong\u003e. \u003c/strong\u003e(H) Principal component analysis in anti-IFNα binding antibody positive and negative groups. (red, anti-IFNα pos; blue, anti-IFNα neg) (I) Heatmap analysis showing association between 20 autoantibodies and anti-IFNα binding antibody. Statistical significance was calculated using an unpaired t test (E-G) or Pearson coefficient (I).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6647603/v1/bf78ffff21417deff086709d.png"},{"id":83283593,"identity":"abaca2f3-774f-41e8-86e7-a23929c3af13","added_by":"auto","created_at":"2025-05-22 10:51:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":170735,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation of autoantibodies to SARS-CoV-2 neutralization. \u003c/strong\u003e(A) Sensitivity of SB3, B.1.351, R.1 645, B.1.617.2 and BA.5 variants to neutralizing antibodies (brown, SB3; green, B.1.351; blue, R.1 645; red, B.1.617.2; purple, BA.5). Statistical significance was calculated using an unpaired t test (*\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.001 and ****\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.0001). (B) Circa plots to visualize association of the level of autoantibodies to SARS-CoV-2 neutralization (brown, SB3; green, B.1.351; blue, R.1 645; red, B.1.617.2; purple, BA.5). Statistical significance was calculated using Pearson coefficient (*\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.001 and ****\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.0001) (C-G) Principal component analysis in neutralizer and non-neutralizer groups (red, neutralizer; blue, non-neutralizer).\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6647603/v1/98827363b46461c6e15a8f16.png"},{"id":83285212,"identity":"3e537173-6f53-48e9-a8c4-d1f1d11bbe88","added_by":"auto","created_at":"2025-05-22 11:15:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2314606,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6647603/v1/e4734998-e43c-4734-a053-42236a6112de.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Distinct circulating autoantibodies are associated with COVID-19 hospitalization and SARS-CoV-2 neutralization activity","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAutoantibodies that target various cellular components are linked to several autoimmune diseases (ADs). Over 100 ADs have been described and numerous autoepitopes identified, since the discovery of anti-DNA antibodies in systemic lupus erythematosus (SLE) patients in the 1940s (\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Common targets of autoantibodies include antinuclear antibodies (ANA) targeting DNA and histones, extractable nuclear antigens (ENA) present in the nucleus and cytoplasm, phospholipids and antineutrophil cytoplasmic antibodies (ANCA) directed against the cytoplasmic components of neutrophils. Preexisting autoantibodies can influence the course of infectious diseases, with viruses potentially triggering autoimmune conditions (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Elevated autoantibody levels are observed in severe SARS-CoV-2 infected patients, although it remains unclear if autoantibodies are the cause or consequence of severe COVID-19 (\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). During SARS-CoV-2 infection, autoantibodies are likely triggered by virus-induced cytokine storm (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), molecular mimicry between viral and host proteins (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), virus induced T cell exhaustion (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) and dysfunctional regulatory T cells (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). While ANAs can be elevated in long COVID-19 patients (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), ANA positivity has also been shown to be substantially more prevalent than in the general population (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Furthermore, COVID-19 outpatients had an increased risk of developing inflammatory arthritis, connective tissue diseases and intestinal-related AD in compared to inpatients, highlighting the complex relationship between autoantibodies and COVID-19 (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eType I interferon (IFN) induced innate immune responses are crucial for protective immunity as they reduce virus replication. Inborn IFN pathway genetic disorders predispose individuals to severe infections. Deficiencies in Toll-like receptor 3 (TLR3) and melanoma differentiation-associated protein 5 (MDA5), which detect double-stranded RNA are linked to severe viral infections, including influenza pneumonitis (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), herpes simplex virus-1 (HSV-1) encephalitis (\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), enterovirus infection (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) and higher susceptibility to rhinovirus infections (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Genetic defects in interferon regulatory factor 3 (IRF3), IRF7 and IRF9 can exacerbate conditions like HSV-1 encephalitis (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) influenza pneumonitis (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) and respiratory syncytial virus infection (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Individuals with severe COVID-19 often harbor defects in TLR3, IRF3 and IRF7, critical for IFN responses (\u003cspan additionalcitationids=\"CR35 CR36\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Consistent with inborn genetic disorders, anti-IFN autoantibodies which block IFN function contribute to poorer clinical outcomes. These anti-IFN autoantibodies were first detected in patients treated with IFNβ for nasopharyngeal carcinoma in the early 1980s and are prevalent in autoimmune conditions like SLE and autoimmune polyendocrine syndrome type 1 (APS-1) (\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Neutralizing anti-IFN autoantibodies are linked to a subset of critically ill COVID-19 patients with low levels of IFN (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Anti-IFN autoantibodies are found in ~\u0026thinsp;10% of patients with critical COVID-19, are linked to multi-organ failure and compromise antiviral defenses in the nasal mucosa enabling virus dissemination (\u003cspan additionalcitationids=\"CR44 CR45 CR46\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). In contrast, persistent IFN and associated interferon stimulated gene (ISG) responses can worsen symptoms by inducing hyperinflammation during viral infections in humans and animal models (\u003cspan additionalcitationids=\"CR49\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). Accordingly, IFN driven signatures led to TNF and IL1β-driven inflammation and worsened COVID-19 outcomes, resulting in upregulated ISG levels in postmortem lung tissues (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). Surprisingly, APS-1 patients demonstrated mild COVID-19 symptoms despite having anti-IFN neutralizing autoantibodies that suppressed signal transducer and activator of transcription 1 (STAT1) phosphorylation and reduced downstream IFN signaling (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). A recent study demonstrated a homeostatic role for anti-IFN neutralizing autoantibodies which reduced excessive IFN and inflammation in the nasal mucosa of SARS-CoV-2 infected patients with autoantibodies appearing within weeks of infection and associated with nasal IFNα secretion and efficient recovery (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHere, we detected 20 common autoantibody signatures, along with anti-IFNα autoantibodies using convalescent serum samples from unvaccinated, SARS-CoV-2 infected patients and examined their relationship to disease severity. We further explored the correlation between circulating autoantibodies in the context of SARS-CoV-2 infection and identified a subset of anti-ENAs driving hospitalization. Finally, we establish a new link between autoantibodies and the humoral immune response targeting the spike region of SARS-CoV-2 variants, mediating its neutralization.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eHuman donors\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eInformed consent\u003c/strong\u003e \u003cp\u003ewas obtained for the collection of convalescent serum from 38 patients with laboratory confirmed SARS-CoV-2 infection. This study was approved by Sunnybrook Research Institute (REB#2218) and Sinai Health System (REB# 02-0118-U and 05-0016-C) ethics boards.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCells and viruses\u003c/h3\u003e\n\u003cp\u003eHuman A549 lung and monkey Vero E6 epithelial cells were cultured as described previously (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). Vesicular stomatitis virus expressing green fluorescent protein (VSV-GFP) was used for IFNα neutralization assays at MOI 1.0. SB3, an ancestral SARS-CoV-2 variant and R.1 645, a variant under monitoring (VuM) were isolated and purified as described (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). The B.1.351, (beta), B.1.617.2 (delta) and BA.5 (omicron) was obtained from BEI Resources (Manassas, VA, United States). Experiments with SARS-CoV-2 were performed in a biosafety containment level 3 facility as approved by the institutional biosafety committee at McMaster University.\u003c/p\u003e\n\u003ch3\u003eDetection of autoantibodies\u003c/h3\u003e\n\u003cp\u003e Autoantibodies were detected using a multiplex panel (MilliporeSigma, Catalog # HAIAB-10K) following manufacturer\u0026rsquo;s guidelines. Fluorescence from labeled magnetic beads was measured with a MAGPIX instrument using xPONENT software. Fold increases in median fluorescent intensity (MFI) were calculated by dividing the MFI of each autoantibody with the MFI of sham-conjugated controls.\u003c/p\u003e\n\u003ch3\u003eInterferon treatment\u003c/h3\u003e\n\u003cp\u003eA549 cells were left untreated or treated with ten-fold serially diluted IFNα (Catalog # I4276, Sigma-Aldrich). Cells were pre-treated for 6 hours before infection with VSV-GFP at MOI 1.0. GFP was measured 24 hours post-infection to determine initiation of virus replication.\u003c/p\u003e\n\u003ch3\u003eAnti-IFNα neutralization assay\u003c/h3\u003e\n\u003cp\u003eThe anti-IFNα neutralizing monoclonal antibody MMHA-2 (Invitrogen, Catalog # 211002) or convalescent serum samples were serially diluted and incubated with 0.1 and 1.0 ng/mL IFNα (Catalog # I4276, Sigma-Aldrich) for two hours at 37˚C. A549 cells were pre-treated with the antibody:IFNα mixture for six hours before infection with VSV-GFP at MOI 1.0. GFP was measured 24 hours post infection to determine initiation of virus replication.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEnzyme-linked immunosorbent assay (ELISA)\u003c/h2\u003e \u003cp\u003e Anti-IFNα binding antibodies (catalog # 5018034, Invitrogen,) and total IgG antibodies (catalog # BMS2091, Invitrogen,) were determined using ELISA following manufacturer\u0026rsquo;s guidelines.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSARS-CoV-2 neutralization assay\u003c/h3\u003e\n\u003cp\u003eSerially diluted serum samples were incubated with SARS-CoV-2 (150 PFU/well) at 37\u0026ordm;C for 1 hour before adding to pre-plated Vero E6 cells. Five days post-infection, luminescence was quantified with CellTiter-Glo 2.0 Reagent (catalog # G9243, Promega) using a BioTek Synergy H1 microplate reader. The luminescent signal, which was proportional to the amount of ATP present, was measured in Relative Light Units (RLU).\u003c/p\u003e\n\u003ch3\u003eBioinformatics and statistical analysis\u003c/h3\u003e\n\u003cp\u003eCorrelation was executed in R using \u0026lsquo;rcorr\u0026rsquo; function from the \u0026lsquo;Hmisc\u0026rsquo; package. Heatmaps were generated using \u0026lsquo;pheatmap\u0026rsquo; package. \u0026lsquo;symnum\u0026rsquo;, an in-built function in R, was used to replace correlation coefficients with symbols based on the degree of relation. Principal component analysis (PCA) was executed in R using 'prcomp' function from the 'stats' package and visualized using \u0026lsquo;ggfortify\u0026rsquo; package. Neural network was executed in R using \u0026lsquo;neuralnet\u0026rsquo; package using 80% of the data for training and 20% for testing. Circa plots (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://omgenomics.com/circa\u003c/span\u003e\u003cspan address=\"https://omgenomics.com/circa\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to visualize association of the level of autoantibodies with SARS-CoV-2 neutralization.\u003c/p\u003e \u003cp\u003eUnpaired t test with Welch\u0026rsquo;s correction was used to calculate differences between hospitalized and non-hospitalized groups. The correlation between autoantibody levels and anti-IFNα antibody or SARS-CoV-2 neutralization was determined using the Pearson r product. Neutralization score was determined using a non-linear regression curve fit model. An unpaired t test was used to compare the neutralization of the SARS-CoV-2 isolates. GraphPad Prism 10 was used for the above statistical tests.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThe clinical summary is outlined in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The study cohort included 38 unvaccinated individuals infected with SARS-CoV-2. Serum samples were collected during the first and third Canadian pandemic waves in 2020 and 2021. Of the 38 patients, 63% (24/38) were managed as outpatients, while 37% (14/38) required hospitalization (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). The median age was 56 and 66.5 years for non-hospitalized and hospitalized groups, respectively, with a higher proportion of females in the non-hospitalized group. Serum samples were collected at a median of 37 and 82 days following the first positive COVID-19 test for non-hospitalized and hospitalized groups, respectively. For the hospitalized group, the median time from onset of symptoms to hospital admission was six days. Common symptoms included fever, cough and shortness of breath, with shortness of breath being more prevalent in hospitalized group. The hospitalized group had a higher prevalence of co-morbidities, including cardiac, vascular, pulmonary and renal illnesses as well as cancer and mental health conditions. The ICU group had four patients, two of whom required intubation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical summary of patients from this study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSummary\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-hospitalized\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHospitalized\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eICU\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSamples, no (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (10.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56 (34.3 to 63.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.5 (56.5 to 85.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60 (55.5 to 64.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, female/male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14/10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4/10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0/4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDays to sampling from first COVID-19 positive test, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (24.0 to 48.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82 (38.5 to 93.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.5 (15.3 to 91.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDays to admission from onset of symptoms, median no (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (2 to 9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.5 (0.5 to 5.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSymptom, no (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsymptomatic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCough\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (41.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (64.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShortness of breath\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCo-morbidities, no (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiac illnesses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVascular illnesses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (71.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulmonary illnesses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenal illnesses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeuro-muscular illnesses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver illnesses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastro-intestinal illnesses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer conditions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRheumatologic illnesses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMental health diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (21.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmuno-compromising condition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntubation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDetection of autoimmune antibodies in convalescent sera\u003c/h2\u003e \u003cp\u003eWe examined the relationship between autoimmune antibodies and hospitalization in SARS-CoV-2 infection by measuring autoantibody levels against 20 self-antigens linked to various clinical phenotypes, utilizing a magnetic bead-based multiplex approach. Of these, 75% (15 of 20) were ANAs targeting various nuclear components including DNA and RNA. All ANAs targeted ENAs, a subgroup comprising non-chromatin nuclear proteins. The panel also included two ANCAs, one anti-aminoacyl tRNA synthetase (ARS) antibody, one antiphospholipid antibody, and one C1q antibody targeting the complement C1 complex. This profiling enabled a comprehensive understanding of autoimmune response in SARS-CoV-2 infected convalescent patients. We observed a substantial variation in the prevalence of autoantibodies in our cohort with a 2.8- to 465.7-fold increase in autoantibody levels compared to sham-control beads (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). To determine whether variations in autoantibody levels were related to differences in IgG, we measured the total polyclonal IgG concentration (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). The mean IgG level was 1337 mg/dL (range, 1279\u0026ndash;1397). There was no significant association between IgG levels and autoantibodies, except for CENP-B (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0357), suggesting that the variation in autoantibodies is largely independent of total IgG levels.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation matrix and clustering of autoantibodies\u003c/h2\u003e \u003cp\u003eWe then evaluated the association between each of the 20 autoantibodies and their clustering patterns. Correlation matrix grouped the autoantibodies into four clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). The first cluster (from the top) included five ANAs targeting various nuclear components (Ku, CENP-A, RNP/Sm, PCNA and Scl-70) and an anti-ARS antibody, PL-12. Each of the six autoantibodies in this cluster strongly correlated with each other. The second cluster contained two ANAs, Sm and PM/Scl-100, which significantly correlated with each other. The third cluster contained four ANAs (Ribosomal P, SSA/Ro52, RNP and Jo-1) and an anti-phospholipid antibody, β2-Glycoprotein, all of which significantly correlated with each other. The final cluster had seven autoantibodies: four ANAs (SSA/Ro60, SSB/La, Mi-2 and CENP-B), two ACNAs (Myeloperoxidase and Proteinase 3) and one anti-C1q antibody targeting the complement C1 component. The intra-cluster correlation was weakest for this cluster.\u003c/p\u003e \u003cp\u003eIn addition to the intra-cluster correlation, we observed significant inter cluster correlations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Autoantibodies from cluster 1 were strongly associated (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) with those in cluster 2 and weakly with cluster 4; however, no association was found between cluster 1 and cluster 3. The two ANAs in cluster 2 significantly correlated with the four ANAs in cluster 3, while eliciting a weak correlation with cluster 4. Finally, there was significant association between autoantibodies in cluster 3 and cluster 4. Our clustering data reveal that the presence of one autoantibody correlates with the detection of multiple other autoantibodies, suggesting a complex and heterogenous autoimmune response in this cohort.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eAssociation of autoantibodies with COVID-19 progression\u003c/h2\u003e \u003cp\u003eNext, we performed principal component analysis (PCA) to reduce the dimensionality of the dataset and identify autoantibodies that drive COVID-19 associated hospitalization (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Dimension 1 (PC1) 1 and PC2 accounted for 40.2% and 21.2% of the total variance, respectively. However, PCA did not reveal any distinct separation between non-hospitalized, hospitalized and ICU groups indicating that autoantibodies by themselves are not associated with hospitalization in this cohort.\u003c/p\u003e \u003cp\u003eNext, we focused on identifying signature patterns linked to disease progression using heatmap analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE-H). This enabled visualization of fold-changes in autoantibody levels across samples and identification of clustering patterns that might mediate hospitalization. Heatmap analyses were performed on four groups: non-hospitalized, hospitalized, non-ICU hospitalized, and ICU. While the non-hospitalized and non-ICU hospitalized groups showed interspersed clustering, there was a distinct separation of autoantibody clusters in the hospitalized and ICU groups. Notably, three ENAs from the third cluster (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC), SSA/Ro52, Jo-1 and RNP, clustered in the hospitalized and ICU groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF, H). We noticed 3.8-, 3.5- and 1.9-fold increases in SSA/Ro52, Jo-1 and RNP levels, respectively, in hospitalized versus non-hospitalized groups (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), suggesting that these antigens could be targets in COVID-19-associated hospitalized patients and may be linked to ICU admission. Next, we investigated whether the systemic autoantibody response was associated with hospitalization (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The mean fold increase in autoantibody levels compared to sham conjugated controls for the hospitalized and non-hospitalized groups are shown for each of the 20 autoantibodies (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). There was no significant difference in the fold-change of autoantibody levels while comparing the non-hospitalized group to the hospitalized group. However, the collective presence of SSA/Ro52, Jo-1 and RNP autoantibodies predicted hospitalization status with 62.5% accuracy using a neural network model (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). These findings indicate that this subset of autoantibodies is synergistically associated with hospitalization in this cohort.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAutoantibody levels in non-hospitalized and hospitalized category\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAutoantibodies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean fold change\u003c/p\u003e \u003cp\u003e(non-hosp)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean fold change\u003c/p\u003e \u003cp\u003e(hosp)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-neutrophil cytoplasmic antibody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyeloperoxidase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProteinase 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-aminoacyl tRNA synthetase (ARS) antibody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePL-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e288.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e143.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-phospholipid antibody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ2-Glycoprotein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-complement antibody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC1q\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e312.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e125.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtractable nuclear antigen antibody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentromere protein A (CENP-A)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e141.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e92.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentromere protein B (CENP-B)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e181.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e83.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistidyl tRNA synthetase (Jo-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e242.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e848.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e299.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e163.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMi-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e421.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e161.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProliferating cell nuclear antigen (PCNA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e387.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e94.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRibonucleoprotein (RNP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e302.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e563.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDNA topoisomerase I (Scl-70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e410.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e143.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmith (Sm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRobert-Antigen/Sjogren\u0026rsquo;s A (SSA/Ro52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e135.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e509.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRobert-Antigen/Sjogren\u0026rsquo;s A (SSA/Ro60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSj\u0026ouml;gren syndrome antigen B (SSB/La)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e303.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRNP/Sm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRibosomal protein P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePM/Scl-100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eDetection of binding and neutralizing anti-IFNα antibodies and their association with hospitalization\u003c/h2\u003e \u003cp\u003eNext, we investigated the role of circulating anti-IFNα autoantibodies on COVID-19-related hospitalization. A549 cells were pre-treated with serial ten-fold dilutions of IFNα and assayed for VSV-GFP replication to determine the minimal concentration required to maintain an antiviral state (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). IFNα at concentrations of 0.01 ng/mL or higher resulted in a partial to complete antiviral state, with an IC\u003csub\u003e50\u003c/sub\u003e of 0.0125 ng/mL (95% CI of 0.01009 to 0.01580; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). We next incubated A549 cells with 0.1 and 1 ng/mL IFNα in the presence of serially diluted control anti-human mouse IgG1 monoclonal antibody (MMHA-2) that binds and neutralizes human IFNα followed by VSV-GFP infection (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Treatment of A549 cells with IFNα in the absence of MMHA-2 reduced VSV-GFP infection compared to untreated control (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC lower panel). Incubation of MMHA-2 with IFNα resulted in a concentration-dependent increase in VSV-GFP infection for both IFNα treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC upper and middle panels), with an IC\u003csub\u003e50\u003c/sub\u003e of 16.6 and 119.7 ng/mL of MMHA-2 for neutralizing 0.1 and 1.0 ng/mL of IFNα, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). We next evaluated if IFNα autoantibodies in serum samples could dampen IFNα-induced antiviral activity, with ID\u003csub\u003e50\u003c/sub\u003e values, referred to as a \u0026ldquo;neutralization score\u0026rdquo;, derived for each serum sample. Two patient samples had a substantial neutralization score with autoantibodies functionally neutralizing IFNα. We further detected IFNα binding antibodies using ELISA in 11% (4/38) of the cohort. Of these, three were in the non-hospitalized category, while one was admitted to the ICU. We did not notice an association between the presence of anti-IFNα neutralizing or binding antibodies with hospitalization status (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE-G). Corroborating these findings, PCA analysis showed no distinct clustering between serum samples with and without anti-IFNα binding autoantibodies (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH). Our data provide evidence that a subset of SARS-CoV-2 patients have binding IFNα autoantibodies that are also capable of restricting an IFNα-induced antiviral state.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eAnti-IFNα binding autoantibodies are strongly associated with ENA autoantibodies\u003c/h2\u003e \u003cp\u003eNext, we examined associations between binding anti-IFNα autoantibodies and the other 20 autoantibodies (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eI). Notably, a positive correlation was noticed between binding anti-IFNα autoantibodies and seven other autoantibodies. Among them, four (SSA/Ro52, RNP, PM/Scl-100, Jo-1) showed a strong correlation indicating that the presence of anti-IFNα autoantibodies is associated with a higher frequency of other autoantibodies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eCirculating autoantibodies are associated with SARS-CoV-2 neutralization\u003c/h2\u003e \u003cp\u003eLast, we tested whether autoantibodies were associated with an increased humoral immune response against SARS-CoV-2. Five live SARS-CoV-2 isolates were used to determine neutralization activity: SB3 (ancestral), B.1.351 (beta), R.1 645 (R.1), B.1.617.2 (delta), and BA.5 (omicron) (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). SB3, R.1 645 and B.1.617.2 were neutralized by 63% (24/38), 76% (29/38) and 61% (23/38) of samples respectively at an ID\u003csub\u003e50\u003c/sub\u003e\u0026thinsp;\u0026gt;\u0026thinsp;25 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). In contrast, B.1.351 and BA.5 were neutralized by only 16% (6/38) and 13% (5/38) of samples respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). B.1.351 was significantly more resistant to neutralization than SB3 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0012), R.1 645 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and B.1.617.2 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0199). Similarly, BA.5 was significantly more resistant to neutralization than SB3 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0006), R.1 645 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and B.1.617.2 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0181).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo explore potential links between autoantibody levels and neutralization capacity, a correlation analysis was performed (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Strikingly, 12 of the 20 autoantibodies were significantly correlated with B.1.351 neutralization, suggesting that autoantibodies may enhance the neutralization activity against resistant SARS-CoV-2 isolates. Six ENAs (PCNA, Scl-70, RNP/Sm, CENP-A, Sm and Ku) along with an aminoacyl-tRNA synthetase (PL-12), showed the strongest correlation (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), suggesting that targeting of specific autoantigens is associated with significantly greater B.1.351 neutralization. Similarly, 10 autoantibodies were significantly correlated with B.1.617.2 neutralization, with RNP/Sm, PCNA, Scl-70 and PL-12 showing the strongest correlations (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Interestingly, the four autoantibodies strongly associated with both B.1.351 and B.1.617.2 neutralization were grouped in cluster 1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Only two autoantibodies, proteinase 3 and CENP-B, were associated with BA.5 neutralization. There was no association observed for the other two sensitive SARS-CoV-2 isolates, SB3 and R.1 645. To further explore the relationship, we conducted PCA by categorizing samples as \u0026ldquo;neutralizers\u0026rdquo; (ID\u003csub\u003e50\u003c/sub\u003e\u0026thinsp;\u0026gt;\u0026thinsp;25) or \u0026ldquo;non-neutralizers\u0026rdquo; (ID\u003csub\u003e50\u003c/sub\u003e\u0026thinsp;\u0026lt;\u0026thinsp;25). There was no clustering between the two groups, indicating that autoantibodies collectively do not influence SARS-CoV-2 neutralization (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC-G). These findings imply that specific autoreactivities correlates with SARS-CoV-2 neutralization activity.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe detected levels of 20 clinically relevant, circulating autoantibodies frequently found in ADs using serum samples from unvaccinated, SARS-CoV-2 infected, recovered individuals and determined if autoreactivity was associated with severe COVID-19 using hospitalization as the primary criterion (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Although we observed the presence of a diverse repertoire of autoantibodies in acute SARS-CoV-2, there was no association between individual autoantibody frequency and hospitalization. However, the presence of three autoantibodies targeting SSA/Ro52, Jo-1 and RNP, were collectively associated with hospitalization (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Our data indicates that the disruption of immunological tolerance is associated with hospitalization in SARS-CoV-2 acute infections and may serve as a link between infectious diseases and autoimmunity (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Furthermore, 11% and 5% of our cohort had binding and neutralizing anti-IFNα autoantibodies, respectively, with anti-IFNα antibodies significantly associated with six ENAs and β2-glycoprotein (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Finally, the presence of a subset of autoantibodies was linked to enhanced neutralization of B.1.351, B.1.617.2 and BA.5.(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTranscriptomic and proteomic analyses from our group and others have highlighted the enrichment of antiviral responses and type I IFN signaling pathways leading to the expression of ISGs during SARS-CoV-2 infection (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). Here, we demonstrate the presence of anti-IFNα neutralizing antibodies in acute SARS-CoV-2 infection. The prevalence of these antibodies is slightly higher than the 1.5% observed in a blood bank study (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e) and lower than the 10% observed in individuals with life-threatening COVID-19 pneumonia, of whom 94% were men (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). While systemic anti-IFN autoantibodies have been linked to higher viral loads and severe COVID-19, they serve to reduce excessive inflammation in APS-1 patients, leading to milder COVID-19 symptoms (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). The homeostatic function of anti-IFNα neutralizing autoantibodies was demonstrated in the nasal mucosa of SARS-CoV-2 infected patients, where these autoantibodies appeared within weeks of infection, reduced excessive nasal IFNα secretion and inflammation, thereby contributing to an efficient recovery (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). Our data show that neutralizing anti-IFNα antibodies are not driving hospitalization and may in turn be balancing the IFN activity to prevent excessive inflammation.\u003c/p\u003e \u003cp\u003eFinally, we establish a novel link between SARS-CoV-2 neutralization and autoantibody response (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Although previous studies show an association between autoantibodies and anti-SARS-CoV-2 IgG targeting the receptor-binding domain (RBD), non-structural protein 1 (NSP1) and nucleocapsid protein (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e), it remained unclear if autoantibodies in COVID-19 patients associate with SARS-CoV-2 neutralization. Of the 20 autoantibodies examined, 12 were significantly associated with the neutralization of B.1.351, and 10 were significantly associated with B.1.617.2 neutralization, suggesting that the autoimmune response might modulate the anti-viral humoral immune response. A higher frequency of autoantibodies correlates with the ability to neutralize multiple human immunodeficiency virus-1 (HIV-1) subtypes, thereby broadening the neutralization spectrum in some HIV-1 infected individuals (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e). Similarly, anti-influenza antibodies from SLE patients have a higher avidity and neutralization capacity (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e). The development of neutralizing antibodies may be controlled by immune tolerance mechanisms, as observed in HIV-1 and influenza virus infections, where antibodies develop long complementarity determining region 3 (CDR3) loops and undergo extensive somatic hypermutation (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSARS-CoV-2 infections associate with the development of new-onset IgG autoantibodies targeting numerous self-protein targets across a wide range of tissues (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Furthermore, autoimmunity is a key feature of post-acute sequelae of COVID-19 (PASC), observed in approximately 10% of SARS-CoV-2 infections with persistent symptoms over 3 months after the initial infection (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e). Polyautoimmunity occurs in 62% of patients with PASC, characterized by the presence of ANAs detected over 12 months post-COVID-19 and correlating with neurological disorder intensity (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e). Studies using health record data suggest an increased risk of AD in patients between 3 and 15-months post-SARS-CoV-2 infection with vaccination reducing risk (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e). Although this observation suggests that autoantibodies could serve as biomarkers of PASC, limited evidence supports that targeting specific autoantibodies could reverse it. Additionally, infections with human cytomegalovirus, influenza virus, Epstein Barr virus, and chikungunya virus have also been linked to autoimmunity, highlighting that the immunological mechanisms that drive virus-induced ADs are not exclusive to SARS-CoV-2 (\u003cspan additionalcitationids=\"CR69 CR70\" citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA limitation of our study is the absence of longitudinal samples that would have been valuable to explore whether autoantibody levels were transient. Additionally, we did not have a direct comparison between our SARS-CoV-2 infection to an uninfected control group. However, seroprevalence data suggest that 76% of the Canadian population had infection-acquired antibodies from prior SARS-CoV-2 infections (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e), making the establishment of a clear control group for comparison difficult. Some of our interpretations may also be influenced by the fact that hospitalized patients were on average 10 years older and had more males compared to the non-hospitalized group. Another limitation is that we focused exclusively on the IFNα2 subtype. Finally, limitations in serum sample availability precluded us from performing an IgG depletion in IFN-neutralizing patients.\u003c/p\u003e \u003cp\u003eIn conclusion, our findings suggest that the presence of autoantibodies in an acute setting may not independently lead to a pathological clinical phenotype; however, the presence of multiple, distinct autoantibodies could contribute to severe COVID-19. Identifying specific autoantibodies associated with severe COVID-19, provides valuable insights into biomarkers. Notably, we observed that the generation of anti-IFNα antibodies is correlated with the presence of several other autoantibodies indicating a common feature governing the disruption of immune tolerance mechanism. Finally, our data showing a correlation between autoantibodies and SARS-CoV-2 neutralization in COVID-19 infections emphasize the need of further research to explore cross-reactivity of SARS-CoV-2 proteins with other autoepitopes and to determine if SARS-CoV-2 infected patients with AD develop a broader neutralizing antibody response.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eDeclaration of interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no relevant conflicts of interest.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eSubmission declaration\u003c/h2\u003e \u003cp\u003eThe article is not under consideration for publication elsewhere.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eRAJ and KM conceptualized and designed the study. RAJ, AS, AJM, and SM collected the data. MRD, AB, MSM, AHM, SM contributed resources. RAJ, HOA analyzed and interpreted the data. RAJ and KM drafted the manuscript. All authors revised and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThis work was supported by a COVID-19 response grant to KM from the Canadian Institutes of Health Research (CIHR #OV4-170645). MSM is supported by a CIHR COVID-19 rapid response grant, a CIHR new investigator award, and an Ontario early researcher award. Convalescent serum sample collection was supported by a COVID-19 response grant to AM and SM from the CIHR (#439999).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data are available in the main text and can be made available by request to the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRobbins WC, Holman HR, Deicher H, Kunkel HG. Complement fixation with cell nuclei and DNA in lupus erythematosus. Proc Soc Exp Biol Med. 1957;96(3):575\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavidson A, Diamond B. Autoimmune diseases. N Engl J Med. 2001;345(5):340\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMackay IR, Rowley MJ. Autoimmune epitopes: autoepitopes. Autoimmun Rev. 2004;3(7\u0026ndash;8):487\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMobasheri L, Nasirpour MH, Masoumi E, Azarnaminy AF, Jafari M, Esmaeili SA. SARS-CoV-2 triggering autoimmune diseases. Cytokine. 2022;154:155873.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmatti MK, Cyprian FS, Nasrallah GK, Al Thani AA, Almishal RO, Yassine HM. Viruses and Autoimmunity: A Review on the Potential Interaction and Molecular Mechanisms. Viruses. 2019;11(8).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePuel A, Bastard P, Bustamante J, Casanova JL. Human autoantibodies underlying infectious diseases. J Exp Med. 2022;219(4).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbdel-Wahab N, Talathi S, Lopez-Olivo MA, Suarez-Almazor ME. Risk of developing antiphospholipid antibodies following viral infection: a systematic review and meta-analysis. Lupus. 2018;27(4):572\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChang SE, Feng A, Meng W, Apostolidis SA, Mack E, Artandi M, et al. New-onset IgG autoantibodies in hospitalized patients with COVID-19. Nat Commun. 2021;12(1):5417.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZuo Y, Estes SK, Ali RA, Gandhi AA, Yalavarthi S, Shi H, et al. Prothrombotic autoantibodies in serum from patients hospitalized with COVID-19. Sci Transl Med. 2020;12(570).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWoodruff MC, Ramonell RP, Haddad NS, Anam FA, Rudolph ME, Walker TA, et al. Dysregulated naive B cells and de novo autoreactivity in severe COVID-19. Nature. 2022;611(7934):139\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang EY, Mao T, Klein J, Dai Y, Huck JD, Jaycox JR, et al. Diverse functional autoantibodies in patients with COVID-19. Nature. 2021;595(7866):283\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu B, Huang S, Yin L. The cytokine storm and COVID-19. J Med Virol. 2021;93(1):250\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong P, Li W, Xie J, Hou Y, You C. Cytokine storm induced by SARS-CoV-2. Clin Chim Acta. 2020;509:280\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdiguzel Y. Molecular mimicry between SARS-CoV-2 and human proteins. Autoimmun Rev. 2021;20(4):102791.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArevalo-Cortes A, Rodriguez-Pinto D, Aguilar-Ayala L. Evidence for Molecular Mimicry between SARS-CoV-2 and Human Antigens: Implications for Autoimmunity in COVID-19. Autoimmune Dis. 2024;2024:8359683.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiao B, Wang C, Tan Y, Chen X, Liu Y, Ning L, et al. Reduction and Functional Exhaustion of T Cells in Patients With Coronavirus Disease 2019 (COVID-19). Front Immunol. 2020;11:827.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKusnadi A, Ramirez-Suastegui C, Fajardo V, Chee SJ, Meckiff BJ, Simon H, et al. Severely ill COVID-19 patients display impaired exhaustion features in SARS-CoV-2-reactive CD8(+) T cells. Sci Immunol. 2021;6(55).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoncalves-Pereira MH, Santiago L, Ravetti CG, Vassallo PF, de Andrade MVM, Vieira MS, et al. Dysfunctional phenotype of systemic and pulmonary regulatory T cells associate with lethal COVID-19 cases. Immunology. 2023;168(4):684\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeckiff BJ, Ramirez-Suastegui C, Fajardo V, Chee SJ, Kusnadi A, Simon H, et al. Imbalance of Regulatory and Cytotoxic SARS-CoV-2-Reactive CD4(+) T Cells in COVID-19. Cell. 2020;183(5):1340-53 e16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSon K, Jamil R, Chowdhury A, Mukherjee M, Venegas C, Miyasaki K, et al. Circulating anti-nuclear autoantibodies in COVID-19 survivors predict long COVID symptoms. Eur Respir J. 2023;61(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeessle J, Waterboer T, Hippchen T, Simon J, Kirchner M, Lim A, et al. Persistent Symptoms in Adult Patients 1 Year After Coronavirus Disease 2019 (COVID-19): A Prospective Cohort Study. Clin Infect Dis. 2022;74(7):1191\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeluso MJ, Thomas IJ, Munter SE, Deeks SG, Henrich TJ. Lack of Antinuclear Antibodies in Convalescent Coronavirus Disease 2019 Patients With Persistent Symptoms. Clin Infect Dis. 2022;74(11):2083\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChang R, Yen-Ting Chen T, Wang SI, Hung YM, Chen HY, Wei CJ. Risk of autoimmune diseases in patients with COVID-19: A retrospective cohort study. EClinicalMedicine. 2023;56:101783.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLim HK, Huang SXL, Chen J, Kerner G, Gilliaux O, Bastard P, et al. Severe influenza pneumonitis in children with inherited TLR3 deficiency. J Exp Med. 2019;216(9):2038\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBucciol G, Desmet L, Leuven Laboratory of Inborn Errors of I, Corveleyn A, Meyts I. Pathogenic P554S Variant in TLR3 in a Patient with Severe Influenza Pneumonia. J Clin Immunol. 2022;42(2):430-2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang SY. Herpes simplex virus encephalitis of childhood: inborn errors of central nervous system cell-intrinsic immunity. Hum Genet. 2020;139(6\u0026ndash;7):911\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLim HK, Seppanen M, Hautala T, Ciancanelli MJ, Itan Y, Lafaille FG, et al. TLR3 deficiency in herpes simplex encephalitis: high allelic heterogeneity and recurrence risk. Neurology. 2014;83(21):1888\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo Y, Audry M, Ciancanelli M, Alsina L, Azevedo J, Herman M, et al. Herpes simplex virus encephalitis in a patient with complete TLR3 deficiency: TLR3 is otherwise redundant in protective immunity. J Exp Med. 2011;208(10):2083\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen J, Jing H, Martin-Nalda A, Bastard P, Riviere JG, Liu Z, et al. Inborn errors of TLR3- or MDA5-dependent type I IFN immunity in children with enterovirus rhombencephalitis. J Exp Med. 2021;218(12).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLamborn IT, Jing H, Zhang Y, Drutman SB, Abbott JK, Munir S, et al. Recurrent rhinovirus infections in a child with inherited MDA5 deficiency. J Exp Med. 2017;214(7):1949\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndersen LL, Mork N, Reinert LS, Kofod-Olsen E, Narita R, Jorgensen SE, et al. Functional IRF3 deficiency in a patient with herpes simplex encephalitis. J Exp Med. 2015;212(9):1371\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHernandez N, Melki I, Jing H, Habib T, Huang SSY, Danielson J, et al. Life-threatening influenza pneumonitis in a child with inherited IRF9 deficiency. J Exp Med. 2018;215(10):2567\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCiancanelli MJ, Huang SX, Luthra P, Garner H, Itan Y, Volpi S, et al. Infectious disease. Life-threatening influenza and impaired interferon amplification in human IRF7 deficiency. Science. 2015;348(6233):448\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Q, Bastard P, Liu Z, Le Pen J, Moncada-Velez M, Chen J, et al. Inborn errors of type I IFN immunity in patients with life-threatening COVID-19. Science. 2020;370(6515).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatuozzo D, Talouarn E, Marchal A, Zhang P, Manry J, Seeleuthner Y, et al. Rare predicted loss-of-function variants of type I IFN immunity genes are associated with life-threatening COVID-19. Genome Med. 2023;15(1):22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaheb Sharif-Askari N, Hafezi S, Saheb Sharif-Askari F, Ali Hussain Alsayed H, S BMA, Alsafar HS, et al. Multiple inborn errors of type I IFN immunity in a 33-year-old male with a fatal case of COVID-19. Heliyon. 2024;10(8):e29338.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKholaiq H, Abdelmoumen Y, Moundir A, El Kettani A, Ailal F, Benhsaien I, et al. Human Genetic and Immunological Determinants of SARS-CoV-2 Infection and Multisystem Inflammatory Syndrome in Children. Clin Exp Immunol. 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePanem S, Check IJ, Henriksen D, Vilcek J. Antibodies to alpha-interferon in a patient with systemic lupus erythematosus. J Immunol. 1982;129(1):1\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLevin M. Anti-interferon auto-antibodies in autoimmune polyendocrinopathy syndrome type 1. PLoS Med. 2006;3(7):e292.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVallbracht A, Treuner J, Flehmig B, Joester KE, Niethammer D. Interferon-neutralizing antibodies in a patient treated with human fibroblast interferon. Nature. 1981;289(5797):496\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHadjadj J, Yatim N, Barnabei L, Corneau A, Boussier J, Smith N, et al. Impaired type I interferon activity and inflammatory responses in severe COVID-19 patients. Science. 2020;369(6504):718\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrouillet-Assant S, Viel S, Gaymard A, Pons S, Richard JC, Perret M, et al. Type I IFN immunoprofiling in COVID-19 patients. J Allergy Clin Immunol. 2020;146(1):206\u0026ndash;8 e2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBastard P, Rosen LB, Zhang Q, Michailidis E, Hoffmann HH, Zhang Y, et al. Autoantibodies against type I IFNs in patients with life-threatening COVID-19. Science. 2020;370(6515).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBastard P, Gervais A, Le Voyer T, Rosain J, Philippot Q, Manry J, et al. Autoantibodies neutralizing type I IFNs are present in ~\u0026thinsp;4% of uninfected individuals over 70 years old and account for ~\u0026thinsp;20% of COVID-19 deaths. Sci Immunol. 2021;6(62).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoning R, Bastard P, Casanova JL, Brouwer MC, van de Beek D, with the Amsterdam UMCC-BI. Autoantibodies against type I interferons are associated with multi-organ failure in COVID-19 patients. Intensive Care Med. 2021;47(6):704\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoncalves D, Mezidi M, Bastard P, Perret M, Saker K, Fabien N, et al. Antibodies against type I interferon: detection and association with severe clinical outcome in COVID-19 patients. Clin Transl Immunology. 2021;10(8):e1327.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLopez J, Mommert M, Mouton W, Pizzorno A, Brengel-Pesce K, Mezidi M, et al. Early nasal type I IFN immunity against SARS-CoV-2 is compromised in patients with autoantibodies against type I IFNs. J Exp Med. 2021;218(10).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCameron MJ, Ran L, Xu L, Danesh A, Bermejo-Martin JF, Cameron CM, et al. Interferon-mediated immunopathological events are associated with atypical innate and adaptive immune responses in patients with severe acute respiratory syndrome. J Virol. 2007;81(16):8692\u0026ndash;706.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTeijaro JR, Ng C, Lee AM, Sullivan BM, Sheehan KC, Welch M, et al. Persistent LCMV infection is controlled by blockade of type I interferon signaling. Science. 2013;340(6129):207\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChannappanavar R, Fehr AR, Vijay R, Mack M, Zhao J, Meyerholz DK, et al. Dysregulated Type I Interferon and Inflammatory Monocyte-Macrophage Responses Cause Lethal Pneumonia in SARS-CoV-Infected Mice. Cell Host Microbe. 2016;19(2):181\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee JS, Park S, Jeong HW, Ahn JY, Choi SJ, Lee H, et al. Immunophenotyping of COVID-19 and influenza highlights the role of type I interferons in development of severe COVID-19. Sci Immunol. 2020;5(49).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeisel C, Akbil B, Meyer T, Lankes E, Corman VM, Staudacher O, et al. Mild COVID-19 despite autoantibodies against type I IFNs in autoimmune polyendocrine syndrome type 1. J Clin Invest. 2021;131(14).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBabcock BR, Kosters A, Eddins DJ, Donaire MSB, Sarvadhavabhatla S, Pae V, et al. Transient anti-interferon autoantibodies in the airways are associated with recovery from COVID-19. Sci Transl Med. 2024;16(772):eadq1789.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJacob RA, Zhang A, Ajoge HO, D'Agostino MR, Nirmalarajah K, Shigayeva A, et al. Sensitivity to Neutralizing Antibodies and Resistance to Type I Interferons in SARS-CoV-2 R.1 Lineage Variants, Canada. Emerg Infect Dis. 2023;29(7):1386\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBanerjee A, Nasir JA, Budylowski P, Yip L, Aftanas P, Christie N, et al. Isolation, Sequence, Infectivity, and Replication Kinetics of Severe Acute Respiratory Syndrome Coronavirus 2. Emerg Infect Dis. 2020;26(9):2054\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBanerjee A, El-Sayes N, Budylowski P, Jacob RA, Richard D, Maan H, et al. Experimental and natural evidence of SARS-CoV-2-infection-induced activation of type I interferon responses. iScience. 2021;24(5):102477.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHatton CF, Botting RA, Duenas ME, Haq IJ, Verdon B, Thompson BJ, et al. Delayed induction of type I and III interferons mediates nasal epithelial cell permissiveness to SARS-CoV-2. Nat Commun. 2021;12(1):7092.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVazquez SE, Bastard P, Kelly K, Gervais A, Norris PJ, Dumont LJ, et al. Neutralizing Autoantibodies to Type I Interferons in COVID-19 Convalescent Donor Plasma. J Clin Immunol. 2021;41(6):1169\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRojas M, Rodriguez Y, Acosta-Ampudia Y, Monsalve DM, Zhu C, Li QZ, et al. Autoimmunity is a hallmark of post-COVID syndrome. J Transl Med. 2022;20(1):129.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorrow P, Moody MA. Immunologic characteristics of HIV-infected individuals who make broadly neutralizing antibodies. Immunol Rev. 2017;275(1):62\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaynes BF, Wiehe K, Borrow P, Saunders KO, Korber B, Wagh K, et al. Strategies for HIV-1 vaccines that induce broadly neutralizing antibodies. Nat Rev Immunol. 2023;23(3):142\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaur K, Zheng NY, Smith K, Huang M, Li L, Pauli NT, et al. High Affinity Antibodies against Influenza Characterize the Plasmablast Response in SLE Patients After Vaccination. PLoS One. 2015;10(5):e0125618.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrammer F. The human antibody response to influenza A virus infection and vaccination. Nat Rev Immunol. 2019;19(6):383\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLandais E, Moore PL. Development of broadly neutralizing antibodies in HIV-1 infected elite neutralizers. Retrovirology. 2018;15(1):61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAltmann DM, Whettlock EM, Liu S, Arachchillage DJ, Boyton RJ. The immunology of long COVID. Nat Rev Immunol. 2023;23(10):618\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeibert FS, Stervbo U, Wiemers L, Skrzypczyk S, Hogeweg M, Bertram S, et al. Severity of neurological Long-COVID symptoms correlates with increased level of autoantibodies targeting vasoregulatory and autonomic nervous system receptors. Autoimmun Rev. 2023;22(11):103445.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTesch F, Ehm F, Vivirito A, Wende D, Batram M, Loser F, et al. Incident autoimmune diseases in association with SARS-CoV-2 infection: a matched cohort study. Clin Rheumatol. 2023;42(10):2905\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaek ANW, Silachamroon U. Presence of autoimmune antibody in chikungunya infection. Case Rep Med. 2009;2009:840183.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLoza-Tulimowska M, Semkow R, Michalak T, Nowoslawski A. Autoantibodies in sera of influenza patients. Acta Virol. 1976;20(3):202\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHouen G, Trier NH. Epstein-Barr Virus and Systemic Autoimmune Diseases. Front Immunol. 2020;11:587380.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGugliesi F, Pasquero S, Griffante G, Scutera S, Albano C, Pacheco SFC, et al. Human Cytomegalovirus and Autoimmune Diseases: Where Are We? Viruses. 2021;13(2).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMurphy TJ, Swail H, Jain J, Anderson M, Awadalla P, Behl L, et al. The evolution of SARS-CoV-2 seroprevalence in Canada: a time-series study, 2020\u0026ndash;2023. CMAJ. 2023;195(31):E1030-E7.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-viruses","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Viruses](https://www.nature.com/npjviruses)","snPcode":"44298","submissionUrl":"https://submission.springernature.com/new-submission/44298/3","title":"npj Viruses","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"SARS-CoV-2, Autoantibodies, Type I Interferon, Neutralizing Antibodies","lastPublishedDoi":"10.21203/rs.3.rs-6647603/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6647603/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSARS-CoV-2 infection disrupts the host\u0026rsquo;s immune system, leading to altered autoimmune responses. This study investigated host autoreactivities in SARS-CoV-2 infections and their association with severe COVID-19 and the neutralizing antibody response. A magnetic bead based multiplex assay was employed to detect 20 clinically relevant circulating autoantibodies in convalescent serum samples from 38 unvaccinated, SARS-CoV-2 infected patients, 14 of whom were hospitalized. Respiratory symptoms and co-morbidities were recorded for all patients. Clustering, correlation analysis, principal component analysis and neural network modeling were used to explore the relationship between autoantibodies, hospitalization and SARS-CoV-2 neutralization. The presence of one autoantibody correlated with the detection of multiple others. Although anti-IFNα antibodies were detected in 11% of the cohort and strongly associated with elevated levels of anti-ENAs, there was no significant association with clinical outcome. COVID-19 hospitalization was significantly associated with the collective expression of autoantibodies targeting three extractable-nuclear antigens (ENAs): SSA/Ro52, Jo-1 and RNP. In contrast, a separate set of autoantibodies targeting three ENAs: RNP/Sm, PCNA and Scl-70 along with the aminoacyl t-RNA synthetase PL-12, was strongly associated with the antiviral humoral immune response. In summary, this study has identified self-antigens targeted in hospitalized COVID-19 patients. Furthermore, we establish a novel link between the host autoantibody response and the humoral immune response, which plays a crucial role in neutralizing SARS-CoV-2 variants.\u003c/p\u003e","manuscriptTitle":"Distinct circulating autoantibodies are associated with COVID-19 hospitalization and SARS-CoV-2 neutralization activity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-22 10:51:37","doi":"10.21203/rs.3.rs-6647603/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-16T07:18:52+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-16T03:51:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"195491808164378959808423105712029327112","date":"2025-06-06T12:36:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"247767704549101369607280069960629378898","date":"2025-06-03T11:38:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-20T02:12:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"292812986733850723595992001534906647835","date":"2025-05-19T23:07:57+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-16T05:54:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-16T05:51:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-15T04:14:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Viruses","date":"2025-05-12T14:19:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-viruses","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Viruses](https://www.nature.com/npjviruses)","snPcode":"44298","submissionUrl":"https://submission.springernature.com/new-submission/44298/3","title":"npj Viruses","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"83759994-538c-4adc-831c-b1e0abb98210","owner":[],"postedDate":"May 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":48758810,"name":"Biological sciences/Immunology/Vaccines/Rna vaccines"},{"id":48758811,"name":"Biological sciences/Microbiology/Virology/Sars cov 2"},{"id":48758812,"name":"Biological sciences/Immunology/Adaptive immunity/Humoral immunity/Antibodies"}],"tags":[],"updatedAt":"2025-08-25T08:38:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-22 10:51:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6647603","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6647603","identity":"rs-6647603","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}