No abstract

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

The SARS-CoV-2 infection is associated with increased levels of autoantibodies targeting immunological proteins such as cytokines and chemokines. Reports further indicate that COVID-19 patients may develop a wide spectrum of autoimmune diseases due to reasons not fully understood. Even so, the landscape of autoantibodies induced by SARS-CoV-2 infection remains uncharted territory. To gain more insight, we carried out a comprehensive assessment of autoantibodies known to be linked to diverse autoimmune diseases observed in COVID-19 patients, in a cohort of 248 individuals, of which 171 were COVID-19 patients (74 with mild, 65 moderate, and 32 with severe disease) and 77 were healthy controls. Dysregulated autoantibody serum levels, characterized mainly by elevated concentrations, occurred mostly in patients with moderate or severe COVID-19 infection, and was accompanied by a progressive disruption of physiologic IgG and IgA autoantibody signatures. A similar perturbation was found in patients with anosmia. Notably, autoantibody levels often accompanied anti-SARS-CoV-2 antibody concentrations, being both indicated by random forest classification as strong predictors of COVID-19 outcome, together with age. Moreover, higher levels of autoantibodies (mainly IgGs) were seen in the elderly with severe disease compared with young COVID-19 patients with severe disease. These findings suggest that the SARS-CoV-2 infection induces a broader loss of self-tolerance than previously thought, providing new ideas for therapeutic interventions.

Fungal infections represent a major global health problem that affects over a billion people and kills more than 1.5 million individuals annually. Here we employed an integrative approach to unravel the landscape of the human immune responses to Candida spp. by performing a meta-analysis of microarray, bulk, and single-cell RNA-sequencing (RNASeq) of blood transcriptome data. We identified across these different studies a consistent interconnected network interplay of signaling molecules involved in both toll-like receptor (TLR) and interferon (IFN) signaling cascades that is activated in response to different Candida species (C. albicans, C. auris, C. glabrata, C. parapsilosis, and C. tropicalis). Among these molecules, there are several types I IFN, indicating an overlap with the anti-viral immune responses. scRNAseq data confirmed that genes commonly identified by the three transcriptomic methods present a cell-type specific expression patterns across innate and adaptive immune cells. Thus, these data shed new lights on the anti-candida immune response, providing putative molecular pathways for therapeutic intervention.

Dengue virus (DENV) infection is a global health problem with no specific therapy or vaccine currently available to combat it. Here we performed a comprehensive analysis of publicly available transcriptome data of patients with natural DENV infection (NDI) and DENV vaccine trials (DVT1, DVT2, and DVT3), identifying common transcriptional signatures according to the time of infection in NDI and DVTs, and disease severity in NDI. We identified 237 common differentially expressed genes (DEGs) between NDI and DVT1. Of those, 20 were commonly affected by dengue vaccination during DVT2 and DVT3. Machine learn analysis ranked the 10 NDI's most critical predictors for disease severity, e.g., IFIT5, ISG15, and HERC5, which play an essential role in the anti-viral immune response. Hence, this work provides new insight into the NDI and vaccine-induced overlapping immune response and suggests novel targets for developing anti-dengue-specific therapies and monitoring the effectiveness of vaccination.