Inhibition of immunoglobulin class-switching prevents pemphigus onset in desmoglein 3-specific B cell receptor knock-in mouse

Although immunoglobulin class-switching is essential for humoral immunity, its role in B-cell immune tolerance remains unclear. Pemphigus vulgaris is an autoimmune blistering disease caused by IgG targeting desmoglein 3, an adhesion molecule of keratinocytes. In this study, we generated knock-in mice that express anti-Dsg3 AK23 autoantibodies. Knock-in B cells developed normally in vivo and showed Ca2+ influx upon IgM cross-linking in vitro. The mice predominantly produced circulating AK23 IgM but little IgG antibodies. Although no IgG deposition or blister formation was observed in Dsg3-bearing tissues, Dsg3 immunization forced to induce pemphigus phenotype after class-switching to IgG in vivo. Transcriptomic analysis revealed that FCGR2B and FcgRIIB-related genes were downregulated in B cells from peripheral blood of pemphigus patients. Indeed, in AK23 knock-in mice, Fcgr2b deficiency or haploinsufficiency spontaneously led to class-switching, AK23 IgG production, and pemphigus phenotype development. Thus, inhibition of pathogenic class-switching is a crucial tolerogenic process to prevent pemphigus onset, where attenuated FcgRIIB signaling is one of the key predispositions to break this tolerogenic state.

Pseudocell Tracer—A method for inferring dynamic trajectories using scRNAseq and its application to B cells undergoing immunoglobulin class switch recombination

Single cell RNA sequencing (scRNAseq) can be used to infer a temporal ordering of cellular states. Current methods for the inference of cellular trajectories rely on unbiased dimensionality reduction techniques. However, such biologically agnostic ordering can prove difficult for modeling complex developmental or differentiation processes. The cellular heterogeneity of dynamic biological compartments can result in sparse sampling of key intermediate cell states. To overcome these limitations, we develop a supervised machine learning framework, called Pseudocell Tracer, which infers trajectories in pseudospace rather than in pseudotime. The method uses a supervised encoder, trained with adjacent biological information, to project scRNAseq data into a low-dimensional manifold that maps the transcriptional states a cell can occupy. Then a generative adversarial network (GAN) is used to simulate pesudocells at regular intervals along a virtual cell-state axis. We demonstrate the utility of Pseudocell Tracer by modeling B cells undergoing immunoglobulin class switch recombination (CSR) during a prototypic antigen-induced antibody response. Our results revealed an ordering of key transcription factors regulating CSR to the IgG1 isotype, including the concomitant expression of Nfkb1 and Stat6 prior to the upregulation of Bach2 expression. Furthermore, the expression dynamics of genes encoding cytokine receptors suggest a poised IL-4 signaling state that preceeds CSR to the IgG1 isotype.

Effect of high-fidelity simulation on alpha-amylase activity and concentrations of secretory immunoglobulin class A, cortisol, and testosterone among medical students

Purpose High-fidelity simulation calls heavily upon cognitive capacities and generates stress and anxiety. The objective of this prospective, observational study was to evaluate the degree of stress in medical students by measuring hormone levels during critical care classes. Methods Overall, 55 students (senior years of medical faculty) of both sexes were divided into 5-person teams. Demographic data and information on diagnosed diseases, stimulants used, and previous experience in the field of medical simulation were collected with a personal questionnaire. Before starting the scenario (T0), after the end of the scenario (T1), and 120 min thereafter (T2), stress level was measured. For this purpose, systolic blood pressure, diastolic blood pressure, mean blood pressure, heart rate and blood oxygen saturation were evaluated. In addition, saliva was collected to determine alpha-amylase activity and the concentrations of secretory immunoglobulin class A, cortisol, and testosterone. Results Among hemodynamic parameters, systolic and mean blood pressure and heart rate were significantly higher in T1 than in T0 and T2 time points (p < 0.05). Cortisol concentration was higher at T2 compared with T0 and T1. Alpha-amylase activity was highest at T1. Secretory immunoglobulin class A concentration was highest at T0, followed by T1 and then T2. These differences were not statistically significant. Testosterone concentration showed significantly higher values at T2 compared with T0 and T1 (p < 0.05). The analysis of team leaders vs. other members revealed significantly lower cortisol and alpha-amylase values in leaders (p < 0.05). Conclusions High-fidelity simulation is a useful education method in medical subjects, especially in cases where a mistake could produce serious or irreversible consequences. It can increase stress hormone concentrations and thus can be assumed effective as a learning aid even in senior-year students of medical faculty.

Comparison of Five Serological Assays for the Detection of SARS-CoV-2 Antibodies

Serological assays can contribute to the estimation of population proportions with previous immunologically relevant contact with the Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) virus. In this study, we compared five commercially available diagnostic assays for the diagnostic identification of SARS-CoV-2-specific antibodies. Depending on the assessed immunoglobulin subclass, recorded sensitivity ranged from 17.0% to 81.9% with best results for immunoglobulin G. Specificity with blood donor sera ranged from 90.2% to 100%, with sera from EBV patients it ranged from 84.3% to 100%. Agreement from fair to nearly perfect was recorded depending on the immunoglobulin class between the assays, the with best results being found for immunoglobulin G. Only for this immunoglobulin class was the association between later sample acquisition times (about three weeks after first positive PCR results) and positive serological results in COVID-19 patients confirmed. In conclusion, acceptable and comparable reliability for the assessed immunoglobulin G-specific assays could be shown, while there is still room for improvement regarding the reliability of the assays targeting the other immunoglobulin classes.

Diagnostic accuracy of serological tests for covid-19: systematic review and meta-analysis

ObjectiveTo determine the diagnostic accuracy of serological tests for coronavirus disease-2019 (covid-19).DesignSystematic review and meta-analysis.Data sourcesMedline, bioRxiv, and medRxiv from 1 January to 30 April 2020, using subject headings or subheadings combined with text words for the concepts of covid-19 and serological tests for covid-19.Eligibility criteria and data analysisEligible studies measured sensitivity or specificity, or both of a covid-19 serological test compared with a reference standard of viral culture or reverse transcriptase polymerase chain reaction. Studies were excluded with fewer than five participants or samples. Risk of bias was assessed using quality assessment of diagnostic accuracy studies 2 (QUADAS-2). Pooled sensitivity and specificity were estimated using random effects bivariate meta-analyses.Main outcome measuresThe primary outcome was overall sensitivity and specificity, stratified by method of serological testing (enzyme linked immunosorbent assays (ELISAs), lateral flow immunoassays (LFIAs), or chemiluminescent immunoassays (CLIAs)) and immunoglobulin class (IgG, IgM, or both). Secondary outcomes were stratum specific sensitivity and specificity within subgroups defined by study or participant characteristics, including time since symptom onset.Results5016 references were identified and 40 studies included. 49 risk of bias assessments were carried out (one for each population and method evaluated). High risk of patient selection bias was found in 98% (48/49) of assessments and high or unclear risk of bias from performance or interpretation of the serological test in 73% (36/49). Only 10% (4/40) of studies included outpatients. Only two studies evaluated tests at the point of care. For each method of testing, pooled sensitivity and specificity were not associated with the immunoglobulin class measured. The pooled sensitivity of ELISAs measuring IgG or IgM was 84.3% (95% confidence interval 75.6% to 90.9%), of LFIAs was 66.0% (49.3% to 79.3%), and of CLIAs was 97.8% (46.2% to 100%). In all analyses, pooled sensitivity was lower for LFIAs, the potential point-of-care method. Pooled specificities ranged from 96.6% to 99.7%. Of the samples used for estimating specificity, 83% (10 465/12 547) were from populations tested before the epidemic or not suspected of having covid-19. Among LFIAs, pooled sensitivity of commercial kits (65.0%, 49.0% to 78.2%) was lower than that of non-commercial tests (88.2%, 83.6% to 91.3%). Heterogeneity was seen in all analyses. Sensitivity was higher at least three weeks after symptom onset (ranging from 69.9% to 98.9%) compared with within the first week (from 13.4% to 50.3%).ConclusionHigher quality clinical studies assessing the diagnostic accuracy of serological tests for covid-19 are urgently needed. Currently, available evidence does not support the continued use of existing point-of-care serological tests.Study registrationPROSPERO CRD42020179452.