BACKGROUNDInitial reports from the severe acute respiratory coronavirus 2 (SARS-CoV-2) pandemic described children as being less susceptible to coronavirus disease 2019 (COVID-19) than adults. ...Subsequently, a severe and novel pediatric disorder termed multisystem inflammatory syndrome in children (MIS-C) emerged. We report on unique hematologic and immunologic parameters that distinguish between COVID-19 and MIS-C and provide insight into pathophysiology.METHODSWe prospectively enrolled hospitalized patients with evidence of SARS-CoV-2 infection and classified them as having MIS-C or COVID-19. Patients with COVID-19 were classified as having either minimal or severe disease. Cytokine profiles, viral cycle thresholds (Cts), blood smears, and soluble C5b-9 values were analyzed with clinical data.RESULTSTwenty patients were enrolled (9 severe COVID-19, 5 minimal COVID-19, and 6 MIS-C). Five cytokines (IFN-γ, IL-10, IL-6, IL-8, and TNF-α) contributed to the analysis. TNF-α and IL-10 discriminated between patients with MIS-C and severe COVID-19. The presence of burr cells on blood smears, as well as Cts, differentiated between patients with severe COVID-19 and those with MIS-C.CONCLUSIONPediatric patients with SARS-CoV-2 are at risk for critical illness with severe COVID-19 and MIS-C. Cytokine profiling and examination of peripheral blood smears may distinguish between patients with MIS-C and those with severe COVID-19.FUNDINGFinancial support for this project was provided by CHOP Frontiers Program Immune Dysregulation Team; National Institute of Allergy and Infectious Diseases; National Cancer Institute; the Leukemia and Lymphoma Society; Cookies for Kids Cancer; Alex's Lemonade Stand Foundation for Childhood Cancer; Children's Oncology Group; Stand UP 2 Cancer; Team Connor; the Kate Amato Foundations; Burroughs Wellcome Fund CAMS; the Clinical Immunology Society; the American Academy of Allergy, Asthma, and Immunology; and the Institute for Translational Medicine and Therapeutics.
Multi-system Inflammatory Syndrome in Children (MIS-C) is a major complication of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection in pediatric patients. Weeks after an often ...mild or asymptomatic initial infection with SARS-CoV-2 children may present with a severe shock-like picture and marked inflammation. Children with MIS-C present with varying degrees of cardiovascular and hyperinflammatory symptoms. Here we perform a comprehensive analysis of the plasma proteome of more than 1400 proteins in children with SARS-CoV-2. We hypothesize that the proteome would reflect heterogeneity in hyperinflammation and vascular injury, and further identify pathogenic mediators of disease. We show that protein signatures demonstrate overlap between MIS-C, and the inflammatory syndromes macrophage activation syndrome (MAS) and thrombotic microangiopathy (TMA). We demonstrate that PLA2G2A is an important marker of MIS-C that associates with TMA. We find that IFNγ responses are dysregulated in MIS-C patients, and that IFNγ levels delineate clinical heterogeneity.
Patients with common variable immunodeficiency (CVID) have a higher incidence of rheumatologic disorders. To delineate this clinical association, we investigated the phenotypic features of patients ...with CVID affected by these conditions.
We conducted a retrospective analysis of 870 pediatric and adult patients with CVID included in the United States Immunodeficiency Network (USIDNET) registry. Outcomes included clinical characteristics (age, gender, ethnicity, rheumatologic diagnosis, and comorbidities), infectious history and basic immunophenotype (serum immunoglobulin levels, CD19+ B cells, and CD4/CD8 ratio) in patients with CVID and rheumatologic disorders compared to those with non-inflammatory CVID. Demographic and clinical data were compared using chi-square, Fisher’s exact or Wilcoxon-Mann-Whitney tests. Non-parametric tests, single and multiple logistic regression models were used to evaluate the relationship between CVID-associated rheumatologic disorders and basic immunophenotypic parameters (IgA, IgM, CD19+ B-cell counts, and CD4/CD8 ratios).
Physician-reported rheumatic diseases were present in 5.9% of patients with CVID (n = 51) included in the registry. Although CVID affects both sexes equally, and patients are of predominantly White-Caucasian ethnicity, there were more females (3.3:1 female to male ratio) and increased proportion of non-white patients in the rheumatologic disease group (p < 0.05). Specific disorders included: inflammatory arthritis (n = 18), Sjogren’s syndrome (n = 11), SLE (n = 8), Raynaud’s syndrome (n = 8), vasculitis (n = 9), MCTD (n = 3), and other (n = 5). In about one-third of patients, a rheumatologic condition was associated with an additional inflammatory complication or malignancy. In regards to the immunophenotype parameters compared (CD19+ B-cell counts, CD4/CD8 ratio, IgA, and IgM), no significant differences were demonstrated between the two groups.
Our findings highlight the coexistence of primary antibody immunodeficiencies and systemic rheumatologic disorders, describe the spectrum of rheumatologic manifestations, and contrast differences in relevant demographic, clinical and immunophenotype parameters in the largest registry of CVID patients in the U.S. In spite of its limitations, our study details the intersection of systemic autoimmunity and CVID and provides valuable insights into these two groups of disorders. Further delineating the link between systemic autoimmunity and humoral immunodeficiencies can provide novel insights into the immune abnormalities underlying these related conditions.
Most children with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection have mild or minimal disease, with a small proportion developing severe disease or multisystem inflammatory ...syndrome in children (MIS-C). Complement-mediated thrombotic microangiopathy (TMA) has been associated with SARS-CoV-2 infection in adults but has not been studied in the pediatric population. We hypothesized that complement activation plays an important role in SARS-CoV-2 infection in children and sought to understand if TMA was present in these patients. We enrolled 50 hospitalized pediatric patients with acute SARS-CoV-2 infection (n = 21, minimal coronavirus disease 2019 COVID-19; n = 11, severe COVID-19) or MIS-C (n = 18). As a biomarker of complement activation and TMA, soluble C5b9 (sC5b9, normal 247 ng/mL) was measured in plasma, and elevations were found in patients with minimal disease (median, 392 ng/mL; interquartile range IQR, 244-622 ng/mL), severe disease (median, 646 ng/mL; IQR, 203-728 ng/mL), and MIS-C (median, 630 ng/mL; IQR, 359-932 ng/mL) compared with 26 healthy control subjects (median, 57 ng/mL; IQR, 9-163 ng/mL; P < .001). Higher sC5b9 levels were associated with higher serum creatinine (P = .01) but not age. Of the 19 patients for whom complete clinical criteria were available, 17 (89%) met criteria for TMA. A high proportion of tested children with SARS-CoV-2 infection had evidence of complement activation and met clinical and diagnostic criteria for TMA. Future studies are needed to determine if hospitalized children with SARS-CoV-2 should be screened for TMA, if TMA-directed management is helpful, and if there are any short- or long-term clinical consequences of complement activation and endothelial damage in children with COVID-19 or MIS-C.
•sC5b9 plasma levels are elevated in children with SARS-CoV-2 infection, even if they have minimal symptoms of COVID-19.•A high proportion of children with SARS-CoV-2 infection met clinical criteria for TMA.
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Autoimmune cytopenias (AICs) regularly occur in profoundly IgG-deficient patients with common variable immunodeficiency (CVID). The isotypes, antigenic targets, and origin(s) of their disease-causing ...autoantibodies are unclear.
We sought to determine reactivity, clonality, and provenance of AIC-associated IgM autoantibodies in patients with CVID.
We used glycan arrays, patient erythrocytes, and platelets to determine targets of CVID IgM autoantibodies. Glycan-binding profiles were used to identify autoreactive clones across B-cell subsets, specifically circulating marginal zone (MZ) B cells, for sorting and IGH sequencing. The locations, transcriptomes, and responses of tonsillar MZ B cells to different TH- cell subsets were determined by confocal microscopy, RNA-sequencing, and cocultures, respectively.
Autoreactive IgM coated erythrocytes and platelets from many CVID patients with AICs (CVID+AIC). On glycan arrays, CVID+AIC plasma IgM narrowly recognized erythrocytic i antigens and platelet i-related antigens and failed to bind hundreds of pathogen- and tumor-associated carbohydrates. Polyclonal i antigen–recognizing B-cell receptors were highly enriched among CVID+AIC circulating MZ B cells. Within tonsillar tissues, MZ B cells secreted copious IgM when activated by the combination of IL-10 and IL-21 or when cultured with IL-10/IL-21–secreting FOXP3−CD25hi T follicular helper (Tfh) cells. In lymph nodes from immunocompetent controls, MZ B cells, plentiful FOXP3+ regulatory T cells, and rare FOXP3−CD25+ cells that represented likely CD25hi Tfh cells all localized outside of germinal centers. In CVID+AIC lymph nodes, cellular positions were similar but CD25hi Tfh cells greatly outnumbered regulatory cells.
Our findings indicate that glycan-reactive IgM autoantibodies produced outside of germinal centers may contribute to the autoimmune pathogenesis of CVID.
There are currently more than 480 primary immune deficiency (PID) diseases and about 7000 rare diseases that together afflict around 1 in every 17 humans. Computational aids based on data mining and ...machine learning might facilitate the diagnostic task by extracting rules from large datasets and making predictions when faced with new problem cases. In a proof-of-concept data mining study, we aimed to predict PID diagnoses using a supervised machine learning algorithm based on classification tree boosting.
Through a data query at the USIDNET registry we obtained a database of 2396 patients with common diagnoses of PID, including their clinical and laboratory features. We kept 286 features and all 12 diagnoses to include in the model. We used the XGBoost package with parallel tree boosting for the supervised classification model, and SHAP for variable importance interpretation, on Python v3.7. The patient database was split into training and testing subsets, and after boosting through gradient descent, the predictive model provides measures of diagnostic prediction accuracy and individual feature importance. After a baseline performance test, we used the Class Weighting Hyperparameter, or scale_pos_weight to correct for imbalanced classification.
The twelve PID diagnoses were CVID (1098 patients), DiGeorge syndrome, Chronic granulomatous disease, Congenital agammaglobulinemia, PID not otherwise classified, Specific antibody deficiency, Complement deficiency, Hyper-IgM, Leukocyte adhesion deficiency, ectodermal dysplasia with immune deficiency, Severe combined immune deficiency, and Wiskott-Aldrich syndrome. For CVID, the model found an accuracy on the train sample of 0.80, with an area under the ROC curve (AUC) of 0.80, and a Gini coefficient of 0.60. In the test subset, accuracy was 0.76, AUC 0.75, and Gini 0.51. The positive feature value to predict CVID was highest for upper respiratory infections, asthma, autoimmunity and hypogammaglobulinemia. Features with the highest negative predictive value were high IgE, growth delay, abscess, lymphopenia, and congenital heart disease. For the rest of the diagnoses, accuracy stayed between 0.75 and 0.99, AUC 0.46–0.87, Gini 0.07–0.75, and LogLoss 0.09–8.55.
Clinicians should remember to consider the negative predictive features together with the positives. We are calling this a proof-of-concept study to continue with our explorations. A good performance is encouraging, and feature importance might aid feature selection for future endeavors. In the meantime, we can learn from the rules derived by the model and build a user-friendly decision tree to generate differential diagnoses.
•We aimed to predict primary immune deficiency diagnoses using a supervised machine learning algorithm based on classification tree boosting.•We obtained and curated a database of 2396 patients with common diagnoses of PID, including their clinical and laboratory features.•We kept 286 features and all 12 diagnoses to include in the model. For the interpretation of variables, to each feature an importance value. Each diagnosis is differentiated or predicted against all others. The patient database is split randomly into training and testing subsets. We found a good performance to predict any of the twelve diagnoses. Accuracy and Area Under the ROC Curve stayed between 0.70 and 0.80 for most diseases, and Gini indexes were around 0.50.•Predictive performance plummeted when the number of disease representatives fell under 50–60 cases.
Although chiefly a B-lymphocyte disorder, several research groups have identified common variable immunodeficiency (CVID) subjects with numeric and/or functional TH cell alterations. The causes, ...interrelationships, and consequences of CVID-associated CD4+ T-cell derangements to hypogammaglobulinemia, autoantibody production, or both remain unclear.
We sought to determine how circulating CD4+ T cells are altered in CVID subjects with autoimmune cytopenias (AICs; CVID+AIC) and the causes of these derangements.
Using hypothesis-generating, high-dimensional single-cell analyses, we created comprehensive phenotypic maps of circulating CD4+ T cells. Differences between subject groups were confirmed in a large and genetically diverse cohort of CVID subjects (n = 69) by using flow cytometry, transcriptional profiling, multiplex cytokine/chemokine detection, and a suite of in vitro functional assays measuring naive T-cell differentiation, B-cell/T-cell cocultures, and regulatory T-cell suppression.
Although CD4+ TH cell profiles from healthy donors and CVID subjects without AICs were virtually indistinguishable, T cells from CVID+AIC subjects exhibited follicular features as early as thymic egress. Follicular skewing correlated with IgA deficiency–associated endotoxemia and endotoxin-induced expression of activin A and inducible T-cell costimulator ligand. The resulting enlarged circulating follicular helper T-cell population from CVID+AIC subjects provided efficient help to receptive healthy donor B cells but not unresponsive CVID B cells. Despite this, circulating follicular helper T cells from CVID+AIC subjects exhibited aberrant transcriptional profiles and altered chemokine/cytokine receptor expression patterns that interfered with regulatory T-cell suppression assays and were associated with autoantibody production.
Endotoxemia is associated with early commitment to the follicular T-cell lineage in IgA-deficient CVID subjects, particularly those with AICs.
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There are no proven safe and effective therapies for children who develop life‐threatening complications of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). Convalescent plasma (CP) has ...demonstrated potential benefit in adults with SARS‐CoV‐2, but has theoretical risks.We present the first report of CP in children with life‐threatening coronavirus disease 2019 (COVID‐19), providing data on four pediatric patients with acute respiratory distress syndrome. We measured donor antibody levels and recipient antibody response prior to and following CP infusion. Infusion of CP was not associated with antibody‐dependent enhancement (ADE) and did not suppress endogenous antibody response. We found CP was safe and possibly efficacious. Randomized pediatric trials are needed.
Medication adherence is the "Plus" in the global challenge to have 90% of HIV-infected individuals tested, 90% of those who are HIV positive treated, and 90% of those treated achieve an undetectable ...viral load. The latter indicates viral suppression, the goal for clinicians treating people living with HIV (PLWH). The comparative importance of different psychosocial scales in predicting the level of antiretroviral adherence, however, has been little studied. Using data from a cross-sectional study of medication adherence with an international convenience sample of 1811 PLWH, we categorized respondent medication adherence as None (0%), Low (1-60%), Moderate (61-94%), and High (95-100%) adherence based on self-report. The survey contained 13 psychosocial scales/indices, all of which were correlated with one another (p < 0.05 or less) and had differing degrees of association with the levels of adherence. Controlling for the influence of race, gender, education, and ability to pay for care, all scales/indices were associated with adherence, with the exception of Berger's perceived stigma scale. Using forward selection stepwise regression, we found that adherence self-efficacy, depression, stressful life events, and perceived stigma were significant predictors of medication adherence. Among the demographic variables entered into the model, nonwhite race was associated with double the odds of being in the None rather than in the High adherence category, suggesting these individuals may require additional support. In addition, asking about self-efficacy, depression, stigma, and stressful life events also will be beneficial in identifying patients requiring greater adherence support. This support is essential to medication adherence, the Plus to 90-90-90.
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