ACE-2-like enzymatic activity is associated with immunoglobulin in COVID-19 patients

Many mechanisms responsible for COVID-19 pathogenesis are well-established, but COVID-19 includes features with unclear pathogenesis, such as autonomic dysregulation, coagulopathies, and high levels of inflammation. The receptor for SARS-CoV-2 spike protein's receptor binding domain (RBD) is angiotensin converting enzyme 2 (ACE2). We hypothesized that some COVID-19 patients may develop antibodies that have negative molecular image of RBD sufficiently similar to ACE2 to yield ACE2-like catalytic activity -- ACE2-like abzymes. To explore this hypothesis, we studied patients hospitalized with COVID-19 who had plasma samples available obtained about 7 days after admission. ACE2 is a metalloprotease that requires Zn2+ for activity. However, we found that the plasma from some patients studied could cleave a synthetic ACE2 peptide substrate, even though the plasma samples were collected using disodium ethylenediaminetetraacetate (EDTA) anticoagulant. When we spiked plasma with synthetic ACE2, no ACE2 substrate cleavage activity was observed unless Zn2+ was added or the plasma was diluted to decrease EDTA concentration. After processing samples by 100 kDa size exclusion columns and protein A/G adsorption, which depleted immunoglobulin by >99.99%, the plasma samples did not cleave the ACE2 substrate peptide. The data suggest that some patients with COVID-19 develop antibodies with abzyme-like activity capable of cleaving synthetic ACE2 substrate. Since abzymes can exhibit promiscuous substrate specificities compared to the enzyme whose active site image they resemble, and since proteolytic cascades regulate many physiologic processes, anti-RBD abzymes may contribute to some otherwise obscure COVID-19 pathogenesis.

Reduction in circulating monocytes correlates with persistent post-COVID pulmonary fibrosis in multi-omic comparison of long-haul COVID and IPF

Rationale: Up to 30% of COVID-19 patients experience persistent sequelae, including dyspnea, restrictive physiology, and early radiographic signs of pulmonary fibrosis (PF). The mechanisms that provoke post-COVID progressive PF are poorly understood, and biomarkers to identify at-risk patients are urgently needed. Methods: We evaluated a cohort of 14 symptomatic COVID survivors with impaired respiratory function and imaging worrisome for developing PF, including bilateral reticulation, traction bronchiectasis and/or honeycombing, and compared these to Idiopathic Pulmonary Fibrosis (IPF) patients and age-matched controls without respiratory disease. We performed single-cell RNA-sequencing and multiplex immunostaining on peripheral blood mononuclear cells collected at the COVID-19 patients first visit after ICU discharge. Six months later, symptoms, restriction and PF improved in some (Early-Resolving COVID PF), but persisted in others (Late-Resolving COVID PF). Results: Circulating monocytes were significantly reduced in Late-Resolving COVID PF patients compared to Early-Resolving COVID PF and non-diseased controls. Monocyte abundance correlated with pulmonary function tests FVC and DLCO. Differential expression analysis revealed MHC-II class molecules were upregulated on the CD8 T cells of Late-Resolving COVID PF patients but downregulated in monocytes. IPF patients had a similar decrease in monocyte abundance and marked decrease in monocyte HLA-DR protein expression compared to Late-Resolving COVID PF patients. Conclusion: Circulating monocyte abundance may distinguish between patients whose post-COVID PF resolves or persists. Furthermore, fibrotic progression coincided with decreases in HLA-DR expression on monocytes, a phenotype previously associated with dampened antigen stimulation and severe respiratory failure.

Quantitative measurement of IgG to SARS-CoV-2 proteins using ImmunoCAP

BackgroundDetailed understanding of the immune response to SARS-CoV-2, the cause of coronavirus disease 2019 (COVID-19), has been hampered by a lack of quantitative antibody assays. ObjectiveTo develop a quantitative assay for IgG to SARS-CoV-2 proteins that could readily be implemented in clinical and research laboratories. MethodsThe biotin-streptavidin technique was used to conjugate SARS-CoV-2 spike receptor-binding-domain (RBD) or nucleocapsid protein to the solid-phase of the ImmunoCAP resin. Plasma and serum samples from patients with COVID-19 (n=51) and samples from donors banked prior to the emergence of COVID-19 (n=109) were used in the assay. SARS-CoV-2 IgG levels were followed longitudinally in a subset of samples and were related to total IgG and IgG to reference antigens using an ImmunoCAP 250 platform. ResultsPerformance characteristics demonstrated 100% sensitivity and 99% specificity at a cut-off level of 2.5 {micro}g/mL for both SARS-CoV-2 proteins. Among 36 patients evaluated in a post-hospital follow-up clinic, median levels of IgG to spike-RBD and nucleocapsid were 34.7 {micro}g/mL (IQR 18-52) and 24.5 {micro}g/mL (IQR 9-59), respectively. Among 17 patients with longitudinal samples there was a wide variation in the magnitude of IgG responses, but generally the response to spike-RBD and to nucleocapsid occurred in parallel, with peak levels approaching 100 {micro}g/mL, or 1% of total IgG. ConclusionsWe have described a quantitative assay to measure IgG to SARS-CoV-2 that could be used in clinical and research laboratories and implemented at scale. The assay can easily be adapted to measure IgG to novel antigens, has good performance characteristics and a read-out in standardized units.

Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy

For an emerging disease like COVID-19, systems immunology tools may quickly identify and quantitatively characterize cells associated with disease progression or clinical response. With repeated sampling, immune monitoring creates a real-time portrait of the cells reacting to a novel virus before disease specific knowledge and tools are established. However, single cell analysis tools can struggle to reveal rare cells that are under 0.1% of the population. Here, the machine learning workflow Tracking Responders Expanding (T-REX) was created to identify changes in both very rare and common cells in diverse human immune monitoring settings. T-REX identified cells that were highly similar in phenotype and localized to hotspots of significant change during rhinovirus and SARS-CoV-2 infections. Specialized reagents used to detect the rhinovirus-specific CD4+ cells, MHCII tetramers, were not used during unsupervised analysis and instead left out to serve as a test of whether T-REX identified biologically significant cells. In the rhinovirus challenge study, T-REX identified virus-specific CD4+ T cells based on these cells being a distinct phenotype that expanded by [≥]95% following infection. T-REX successfully identified hotspots containing virus-specific T cells using pairs of samples comparing Day 7 of infection to samples taken either prior to infection (Day 0) or after clearing the infection (Day 28). Mapping pairwise comparisons in samples according to both the direction and degree of change provided a framework to compare systems level immune changes during infectious disease or therapy response. This revealed that the magnitude and direction of systemic immune change in some COVID-19 patients was comparable to that of blast crisis acute myeloid leukemia patients undergoing induction chemotherapy and characterized the identity of the immune cells that changed the most. Other COVID-19 patients instead matched an immune trajectory like that of individuals with rhinovirus infection or melanoma patients receiving checkpoint inhibitor therapy. T-REX analysis of paired blood samples provides an approach to rapidly identify and characterize mechanistically significant cells and to place emerging diseases into a systems immunology context.